Patent Publication Number: US-8983883-B2

Title: Autonomic and apoptotic, aeronautical and aerospace systems, and controlling scientific data generated therefrom

Description:
CROSS-REFERENCE TO RELATED APPLICATIONS 
     This application is a continuation-in-part of U.S. patent application Ser. No. 12/569,422, filed Sep. 29, 2009 now U.S. Pat. 8,275,724, and this application is also a continuation-in-part of U.S. patent application Ser. No. 13/230,920, filed Sep. 13, 2011 now U.S. Pat. 8,275,725, which in-turn is a divisional application of U.S. patent application Ser. No. 11/836,352, filed Aug. 9, 2007, now, U.S. Pat. No. 8,041,655, which in-turn claims priority to U.S. Patent Application No. 60/822,687, filed Aug. 17, 2006. 
    
    
     ORIGIN OF THE INVENTION 
     The invention described herein was made by an employee of the United States Government and may be manufactured and used by or for the Government of the United States of America for governmental purposes without the payment of any royalties thereon or therefor. 
    
    
     FIELD OF THE INVENTION 
     This invention relates generally to artificial intelligence and, more particularly, to architecture for collective interactions between autonomous entities. 
     BACKGROUND OF THE INVENTION 
     Future aeronautical and aerospace systems will be highly dependent on Autonomy. Autonomy is often considered as automating the tasks to be performed by that system. Yet to achieve safe, dependable and survivable autonomous systems requires that the system is itself self-managing (selfconfiguring, self-healing, self-optimizing and self-protecting) as well as providing the functional autonomy. One such self-managing paradigm is autonomic computing. Biological inspired autonomous and autonomic systems (AAS) are essentially about creating self-directed and self-managing systems based on metaphors such as that of the autonomic nervous system. Agent technologies have been identified as a key enabler for engineering autonomy and autonomicity in systems, both in terms of retrofitting into legacy systems and designing new systems. Handing over responsibility to the systems raises concerns for humans. 
     Prior patents describe a technique for achieving security in agent based systems through the use of apoptosis, that is, the predetermined “death” of an agent unless it receives a reprieve or “stay alive” signal. This mimics the mechanism of cell death in the human (and animal) body, and hence makes use of autonomic and other biologically inspired metaphors. The technique may also be used to send “self destruct” signals to agents (or their current hosts) that may be compromised, or which cannot be identified as “friendly” or as having a right to access certain resources (re ALice signal disclosure). 
     A synthetic neural system is an information processing paradigm that is inspired by the way biological nervous systems, such as the brain, process information. Biological systems inspire system design in many other ways as well, for example reflex reaction and health signs, nature inspired systems (NIS), hive and swarm behavior, and fire flies. These synthetic systems provide an autonomic computing entity that can be arranged to manage complexity, continuous self-adjust, adjustment to unpredictable conditions, and prevention and recovery for failures. 
     One key element is the general architecture of the etic neural system. A synthetic neural system is composed of a large number of highly interconnected processing autonomic elements that may be analogous to neurons in a brain working in parallel to solve specific problems. Unlike general purpose brains, a synthetic neural system is typically configured for a specific application and sometimes for a limited duration. 
     In one application of autonomic elements, each of a number of spacecrafts could be a worker in an autonomous space mission. The space mission can be configured as an autonomous nanotechnology swarm (ANTS). Each spacecraft in an ANTS has a specialized mission, much like ants in an ant colony have a specialized mission. Yet, a heuristic neural system (HNS) architecture of each worker in an ANTS provides coordination and interaction between each HNS that yields performance of the aggregate of the ANTS that exceeds the performance of a group of generalist workers. 
     More specifically, subset neural basis functions (SNBFs) within a HNS can have a hierarchical interaction among themselves much as the workers do in the entire ANTS collective. Hence, although many activities of the spacecraft could be controlled by individual SNBFs, a ruler SNBF could coordinate all of the SNBFs to assure that spacecraft objectives are met. Additionally, to have edundancy for the mission, inactive workers and rulers can only participate if a member of their type is lost. 
     In some situations, the ANTS encounters a challenging situation. For example, in some instances, the operation of a particular autonomic spacecraft can be detrimental either to the autonomic spacecraft or to the mission. It would be desirable to have a self-destruct mechanism that can be employed to avoid such a detrimental outcome, for example, analogous to apoptotic activity in a biological system. The need to replace the agent or spacecraft, and how to select an agent to become the replacement, form bases for various embodiments of the present teachings. Protecting scientific data obtained from such systems also forms the basis for various embodiments of the present teachings. 
     BRIEF DESCRIPTION OF THE INVENTION 
     The above-mentioned shortcomings, disadvantages and problems may be addressed herein, which will be understood by reading and studying the following specification. 
     Autonomic Computing and Autonomic Communications are inspired by the biological Autonomic Nervous System. Apoptotic Computing and Apoptotic Communications are inspired by the apoptosis mechanism in biological systems. This mechanism provides self-management and security for the overall system. These approaches are included in modern ubiquitous/pervasive computer-based systems and next generation SWARM based Space Missions. The present teachings adapt the autonomic and apoptotic systems described, for example, in U.S. Patent Application Publication No. US 2010/0146635 A1 and in U.S. Pat. No. 8,041,655 B2 for Cloud Computing, Grid Computing and other Highly Distributed Systems. Both of these publications are incorporated herein in their entireties by reference. These paradigms of computing axe increasingly become ubiquitous and their management and safety is not only key for the industry in general but also for NASA&#39;s operations and missions. 
     According to various embodiments of the present teachings, an autonomic device, system, and method are provided that include a self-managing autopoiesis function. The present teachings provide self-protection of a self-managing system and are useful in encouraging a secure feeling about utilizing autonomy and autonomicity. These teachings build on previous teachings described and incorporated herein, and include the new self-* property of autopoiesis (self-creation). The Autonomic (self-managing) Environment can comprise collaborating agents to deal with self-configuration, self-healing, self-optimizing and self-protecting aspects of the highly distributed system. These self-* agents depending on their security mechanisms may have an apoptotic (self-destruct) mechanism inbuilt relying on stay-alive or self-destruct apoptotic signals to continue or desist their operations. The autonomic autopoiesis (self-creation) mechanism of the present teachings is a counter-measure to the apoptotic (self-destruct) mechanism. Since potentially an agent may self-destruct due to security or other factors, that necessary self-* function is no longer in existence within the self-managing system in that locale. As such, there may be a need for a mechanism for auto generation of a replacement. Depending on circumstances, this autopoietic agent may not necessarily be a clone of the original but an alternative, for example, providing an equivalent functionality. The Autopoiesis mechanism, although not directly inspired by a biological function, is a follow on mechanism to auto replace (create) an equivalent self-* function that is missing from a system, for example, as a result of an apoptotic action. The device, system, and method can have many of the same or similar components, steps, and other features as described, for example, in U.S. Pat. No. 7,904,396 B2 to Hinehey et al., which is incorporated herein in its entirety by reference. Patent and patent pending technologies that can be used in accordance with the present teachings and which exemplify environments that are self-managing include those describing a range of autonomic computing (self-managing self*) techniques. These technologies include an apoptotic (self-destruct) mechanism for SWARM agents and space craft as described, for example, in U.S. Pat. Nos. 7,627,538 B2 and 7,925,600 B2, and autonomous and autonomic environments as described, for example, in U.S. Pat. No. 7,765,171 B2. Each of these three patents is incorporated herein in its entirety by reference. Other technologies that can also be employed to provide a self-managing computer system in accordance with the present teachings include autonomic quiescence (self-sleep), as described in U.S. Pat. No. 7,899,760 B2, as a less drastic alternative to self-destruct, and ALice (Autonomic Licence) technology as described in U.S. Pat. No. 7,627,538 B2, to ensure authenticity. Each of these two patents is also incorporated herein in its entirety by reference. 
     According to various embodiments of the present teachings, a self-protection mechanism referred to herein as “self-esteem” is included in an autonomic system to assist in encouraging a secure feeling about utilizing autonomy and autonomicity. A self-managing environment is provided with redundancy and dependability through multiple clone and non-clone versions of self-managing agents (SMA) and services, for example, agents with similar if not identical external functionality. Even cloned SMAs may have the inbuilt ability to adapt and thus evolve into something new. As such a novel Darwinian survival of the fittest inherent property is used in the system. Essentially a reward scheme (self-esteem) for the autonomic agents is enforced based on their performance and trust. An SMA with greater self-esteem may then be allowed to clone at a greater rate with the opposite occurring for lower self-esteem SMAs. The system can be configured such that the apoptotic (self-destruct/self-sacrifice) mechanism kicks in for an SMA if it has reached a low enough self-esteem, for example, a level of self-esteem that is so low that it falls below a threshold self-esteem needed to ward off self-destruction. 
     The patents described and incorporated herein present autonomic/self managing systems comprising an all pervasive lower/middle-ware that performs self-configuration, self-healing, self-optimizing, and self-protecting and thus enables the higher level goal—automation/autonomy—of the actual system to adaptively occur. To ensure survivability, dependability and safety, the autonomic systems comprise static agents bound to system components with feedback control loops, mobile agents, deliveries of self-management updates, heartbeat &amp; pulse monitoring, and apoptotic mechanisms. The present teachings add to these features a survival of the fittest reward scheme. The system can provide redundancy and flexibility and be self-managing through different coding implementations of agents, for example, agents having the same external functionality or agents that provide the same service. A measure of performance and trust can be provided to guide the systems self-managing behavior. 
     Given that the agents can, in some situations, self-adapt, and that future versions will evolve to allow the system to self-generate agents, a measure of performance and trust can be especially important. As such, the self-esteem of each autonomic agent of an autonomic system can be used as a method of measuring the success of each agent at doing its tasks and adapting to new ones. As a result, successful agents can be cloned, that is, they can be enabled to multiply, whereas unsuccessful agents can be enabled to die out by triggering an apoptosis (pre-programmed death) mechanism based on a low self-esteem. A self-managing system can be provided with the self-* property of autopoiesis (self-creation) and the autopoiesis can be based on the relative self-esteems of plurality candidate replacement agents. In the event of an agent in the system self-destructing, the autopoiesis property auto-generates a replacement, and if an existing agent is formed into the replacement the selection of the agent can be based on its self-esteem level, for example, compared to the self-esteem level of other possible replacements. 
     The present teachings are not only applicable, in autonomic systems, to downstream activities such as self-managing mission data farms and mission control servers, but also to next generation mission classes, for example, SWARM based multiple craft missions. Accordingly, the present teachings also encompasses applications in missions such as ANTS which use high numbers of cooperating entities having inbuilt adaptability and requiring a self-managing operating system to ensure the success of scientific goals. The device, system, and method can have many of the same or similar components, steps, and other features as described, for example, in U.S. Pat. No. 7,904,396 B2 to Hinchey et al., which is incorporated herein in its entirety by reference. Patent and patent pending technologies that can be used in accordance with the present teachings and which exemplify environments that are self-managing include those describing a range of autonomic computing (self-managing/self*) techniques. These technologies include an apoptotic (self-destruct) mechanism for SWARM agents and space craft as described, for example, in U.S. Pat. Nos. 7,627,538 B2 and 7,925,600 B2, and autonomous and autonomic environments as described, for example, in U.S. Pat. No. 7,765,171 B2. Each of these three patents is incorporated herein in its entirety by reference. Other technologies that can also be employed to provide a self-managing computer system in accordance with the present teachings include autonomic quiescence (self-sleep), as described in U.S. Pat. No. 7,899,760 B2, as a less drastic alternative to self-destruct, and ALice (Autonomic Licence) technology as described in U.S. Pat. No. 7,627,538 B2, to ensure authenticity. Each of these two patents is also incorporated herein in its entirety by reference. 
     According to yet other embodiments of the present teachings, a biologically inspired self-management mechanism is provided for autonomous and autonomic systems, which is referred to herein as “autonomic adhesion.” Following biological inspiration, cellular adhesion is the binding of a cell to a surface, extracellular matrix, or another cell using cell adhesion molecules such as selectins, integrins, and cadherins. Correct cellular adhesion is essential in maintaining multicellular structure. Cellular adhesion can link the cytoplasm of cells and can be involved in signal transduction. Taking inspiration (and not mimicry) and applying this to autonomic agents or craft, this can essentially be considered akin to agents that like or dislike each other. The present teachings provide a self-managing system here there are a high volume of distributed autonomic/self-managing mobile agents, and autonomic adhesion is used to attract similar agents together or to repel dissimilar agents from an event horizon. For instance, similar types of SMAs that can deal with various types of viruses would be attracted to each other at a local to deal with a virus outbreak, while at the same time repelling other SMAs such as self-optimization agents from the busy scene to avoid congestion. As a result, the proper processor cycles can be dedicated to the incident. Just as the cellular adhesion has signal transduction, the autonomic adhesion adds adhesion signaling to existing autonomic signals (I am alive, I am healthy, lubdub of I &amp; environment are healthy, stay alive, self-destruct, stay awake, self-sleep, ALice signal and so forth). 
     The autonomic adhesive mechanism is a self-* function that enables dynamic clustering of similar self-managing agents or crafts, so that they can together deal with a significant self-managing task or incident, while repelling differential SMAs or crafts and thus prioritizing available locally situated bandwidth and processor power to a task or incident at hand. Adhesion can mean physically moving closely together but can also more generally mean working together even if not physically coming together. The device, system, and method can have many of the same or similar components, steps, and other features as described, for example, in U.S. Pat. No. 7,904,396 B2 to Hinchey et al., which is incorporated herein in its entirety by reference. Patent and patent pending technologies that can be used in accordance with the present teachings and which exemplify environments that are self-managing include those describing a range of autonomic computing (self-managing/self*) techniques. These technologies include an apoptotic (self-destruct) mechanism for SWARM agents and space craft as described, for example, in U.S. Pat. Nos. 7,627,538 B2 and 7,925,600 B2, and autonomous and autonomic environments as described, for example, in U.S. Pat. No. 7,765,171 B2. Each of these three patents is incorporated herein in its entirety by reference. Other technologies that can also be employed to provide a self-managing computer system in accordance with the present teachings include autonomic quiescence (self-sleep), as described in U.S. Pat. No. 7,899,760 B2, as a less drastic alternative to self-destruct, and ALice (Autonomic Licence) technology as described in U.S. Pat. No. 7,627,538 B2, to ensure authenticity. Each of these two patents is also incorporated herein in its entirety by reference. 
     According to yet other embodiments of the present teachings, apoptotic computing and apoptotic communications as provided in the patent described and incorporated herein is included in an apoptotic system specifically useful for data and digital objects and can provide, in essence, an “expiry date”. These teachings are not only useful in general for privacy and safety in the modern world but also useful for controlling the loaning and use of space scientific data to researchers. An apoptotic mechanism is hereby provided for data and digital objects, for example, data files, rental movie files, rental music files, limited licensing rights, time-based licensed access, distributed dynamic data, and the like. Although these teachings may seem counter to an objective of preserving data, an inherent death by default for such data and digital objects can be a useful property for dealing with ethics, security, and trust issues. In some embodiments, data can effectively remain in existence, for example, available or out there on the internet, only if it receives periodic stay alive credentials. Otherwise, the data could expire, be subject to password access only, or locked-down. In some embodiments, the system and method can be utilized for licensing and/or loaning scientific data to users, for example, lending or loaning NASA space mission data. 
     Effectively, valuable datasets can be made to have an expiry date or other safeguard if users of the data do not correctly acknowledge, credit, or cite the data creators, and thus the stay alive could be removed. This innovation not only applies to data but to all digital objects such as pictures, video or music files. Apoptotic Computing may be considered a new sub field of Autonomic Computing. The present teachings have particular applicability for on-line shopping services, on-line music services, on-line distributors, and the like. The device, system, and method can have many of the same or similar components, steps, and other features as described, for example, in U.S. Pat. No. 7,904,396 B2 to Hinchey et al., which is incorporated herein in its entirety by reference. Patent and patent pending technologies that can be used in accordance with the present teachings and which exemplify environments that are self-managing include those describing a range of autonomic computing (self-managing/self*) techniques. These technologies include an apoptotic (self-destruct) mechanism for SWARM agents and space craft as described, for example, in U.S. Pat. Nos. 7,627,538 B2 and 7,925,600 B2, and autonomous and autonomic environments as described, for example, in U.S. Pat. No. 7,765,171 B2. Each of these three patents is incorporated herein in its entirety by reference. Other technologies that can also be employed to provide a self-managing computer system in accordance with the present teachings include autonomic quiescence (self-sleep), as described in U.S. Pat. No. 7,899,760 B2, as a less drastic alternative to self-destruct, and ALice (Autonomic Licence) technology as described in U.S. Pat. No. 7,627,538 B2, to ensure authenticity. Each of these two patents is also incorporated herein in its entirety by reference. 
     According to various embodiments of the present teachings, an autonomic system provided that has the following objectives: self-configuration; self-healing; self-optimization; and self-protection. By self-configuration, what is meant is that the system can be able to readjust itself automatically, either to support a change in circumstances or to assist in meeting other system objectives. By self-healing, what is meant is that, in a reactive mode, the system can effectively recover when a fault occurs, identify the fault, and, when possible, repair it. In a proactive mode, self-healing can entail a system configured to monitor vital signs to predict and avoid health problems, or to prevent vital signs from reaching undesirable levels. By self-optimization, what is meant is the system can measure its current performance against a known optimum, and can carry out defined policies for attempting improvements. Self-optimization can also encompass a system configured to react to a user&#39;s policy changes within the system. By self-protection, what is meant is that the system can defend itself from accidental or malicious external attacks, which requires an awareness of potential threats and the means to manage them. 
     According to various embodiments of the present teachings, these self-managing objectives can be achieved be configuring the system to be: self-aware, that is, aware of its internal state; self-situated, that is, aware of current external operating conditions and context; self-monitoring, that is, able to detect changing circumstances; and self-adjusting, that is, able to adapt accordingly. Thus, the autonomic systems of the present teachings can be aware of its available resources and components, their ideal performance characteristics, and current status. The system can also be aware of interconnection with other systems, as well as rules and policies for adjusting as required. The system can also operate in a heterogeneous environment, for example, by relying on open standards to communicate with other systems. 
     Systems, clients, servers, methods, and computer-readable media of varying scope are described herein. In addition to the aspects and advantages described in this summary, further aspects and advantages will become apparent by reference to the drawings and by reading the detailed description that follows. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         FIG. 1  is a block diagram that provides at overview of an evolvable synthetic neural system to manage collective interactions between autonomous entities, according to an embodiment of the invention; 
         FIG. 2  is a block diagram of a neural basis function of a worker, according to an embodiment; 
         FIG. 3  is a block diagram of a heuristic neural system, according to an embodiment; 
         FIG. 4  is a block diagram of an autonomous neural system, according to an embodiment; 
         FIG. 5  is a block diagram neural basis function of a worker, according to an embodiment; 
         FIG. 6  is a block diagram of a multiple level hierarchical evolvable synthetic neural system, according to an embodiment; 
         FIG. 7  is a block diagram of a conventional computer cluster environment in which different embodiments can be practiced; 
         FIG. 8  is a block diagram of a conventional hardware and operating environment in which different embodiments can be practiced; 
         FIG. 9  is a block diagram of a conventional multiprocessor hardware and operating environment in which different embodiments can be practiced; 
         FIG. 10  is a block diagram of a hardware and operating environment, which includes a quiese component, according to an embodiment; 
         FIG. 11  is a diagram of autonomous entities&#39; interaction, according to an embodiment; 
         FIG. 12  is a block diagram of an autonomous entity management system, according to an embodiment; 
         FIG. 13  is a hierarchical chart of an autonomous entity management system, according to an embodiment; 
         FIG. 14  is a block diagram of an autonomic element, according to an embodiment; 
         FIG. 15  is a block diagram of autonomy and autonomicity at a high system level, according to an embodiment; 
         FIG. 16  is a Nock diagram of an architecture of an autonomic element, according to an embodiment, that includes reflection and reflex layers; 
         FIG. 17  is a flowchart of a method to construct an environment to satisfy increasingly demanding external requirements, according to an embodiment; 
         FIG. 18  is a flowchart of a method to construct an environment to satisfy increasingly demanding external requirements, according to an embodiment, where a ruler entity decides to withdraw or generate a stay alive signal; 
         FIG. 19  is a flowchart for a generating stay-alive signal when a warning condition occurs, according to an embodiment; 
         FIG. 20  is a flowchart of a method to construct an environment to satisfy increasingly demanding external requirements, according to an embodiment, where a ruler entity decides to withdraw or generate a stay-awake signal; 
         FIG. 21  is a flowchart for generating an otoacoustic signal when a warning condition occurs, according to an embodiment; 
         FIG. 22  is a flowchart for interrogating an anonymous autonomic agent, according to an embodiment; 
         FIG. 23  is a flowchart of a method of autonomic communication by an autonomic element, according to an embodiment; 
         FIG. 24  is a flowchart of a method of autonomic communication by an autonomic element, according to an embodiment; 
         FIG. 25  is a flowchart of a method of autonomic communication by an autonomic element, according to an embodiment; and 
         FIG. 26  is a flowchart of a method of autonomic communication by an autonomic element, according to an embodiment. 
         FIG. 27  is a diagram depicting the ANTS concept mission scenario according to various embodiments of the present teachings. 
         FIG. 28  depicts the multi-tier specification model of Autonomic System Specification Language (ASSL) according to various embodiments of the present teachings. 
         FIG. 29  depicts a partial specification of a self-sacrifice policy according to various embodiments of the present teachings. 
         FIG. 30  depicts a partial specification of exemplary actions included in a self-sacrifice policy according to various embodiments of the present teachings. 
         FIG. 31A  is a schematic diagram showing a biological system wherein, when a cell constantly receives “stay alive” signals, it turns off its programmed self-destruct sequence, similar to various embodiments of the present teachings. 
         FIG. 31B  is a schematic diagram showing a comparison between biological apoptosis and biological necrosis due to an injury. 
         FIG. 32  is a schematic diagram of a high-level view of a simple autonomic environment with three autonomic elements (AEs), exemplifying various embodiments of the present teachings. 
     
    
    
     DETAILED DESCRIPTION OF THE INVENTION 
     In the following detailed description, reference is made to the accompanying drawings that form a part hereof, and in which is shown by way of illustration specific embodiments that can be practiced. These embodiments are described in sufficient detail to enable those skilled in the art to practice the embodiments, and it is to be understood that other embodiments can be utilized and that logical, mechanical, electrical and other changes can be performed without departing from the scope of the embodiments. The following detailed description is, therefore, not to be taken in a limiting sense. 
     The present teachings can employ a self-managing computer system that has been developed based on autonomic computing. The autonomic computing system is analogous to the biological nervous system, which automatically maintains homeostasis (metabolic equilibrium) and controls responsiveness to external stimuli. An autonomic device, system, and method are provided that include a self-managing autopoiesis function. The autonomic autopoiesis (self-creation) mechanism can be a counter-measure to the apoptotic (self-destruct) mechanism. Since potentially an agent may self-destruct due to security or other factors, that necessary self-* function is no longer in existence within the self-managing system in that locale. As such, there may be a need for a mechanism for auto generation of a replacement, or for an agent to self-create a replacement. Depending on circumstances, this autopoietic agent may not necessarily be a clone of the original but an alternative, for example, providing an equivalent functionality. The Autopoiesis mechanism enables an auto replace (auto-create) an equivalent self-* function that is missing from a system, for example, as a result of an apoptotic action. In some embodiments, a computer-accessible medium is provided in a first autonomic element. The computer-accessible medium has executable instructions of autonomic communication for directing a processor of the first autonomic element to perform the functions of: receiving a autopoiesis instruction from a second autonomic element; and invoking a function of an autopoiesis component of the first autonomic element. Then, if the first autonomic element does not receive a do-not-auto-generate reprieve signal after a predetermined period of time, the first autonomic element undergoes autopoiesis. The function of the autopoiesis component can comprise modifying the autonomic element to self-create a modified element. The instructions can further direct a processor to transmit self health/urgency data, and transmit environment health/urgency data. The instructions can further direct a processor to receive the environment health/urgency data from an environment control loop component of the autonomic element. 
     In some embodiments, an autonomic element is provided that comprises: a self-monitor; a self-adjuster; an environment-monitor; an autonomic manager communications component; and an autopoiesis component. Then self-monitor can receive information from sensors, monitor and analyze the sensor information, and access a knowledge repository. The a self-adjuster can be operably coupled to the self-monitor in a self control loop. The self adjuster can access the knowledge repository, transmit data to effectors, plan, and execute. The environment-monitor can receive information from the sensors, monitor and analyze the sensor information, and access the knowledge repository. The autonomic manager communications component can be operably coupled to the environment-monitor in an environment control loop. The autonomic manager communications component can also access the knowledge repository and produce and transmit a pulse monitor signal. The pulse monitor signal can include a heart beat monitor signal and a reflex signal. The reflex signal include self health/urgency data and environment health/urgency data. The autopoiesis component can be operably coupled to the self-monitor and can receive an autopoiesis instruction from another autonomic element. The autopoiesis component can withdraw a do-not-auto-generate signal, and then, if the autonomic element does not receive a do-not-auto-generate reprieve signal after a predetermined period of time, the autonomic element can auto-generate into a modified agent. The self health/urgency data can comprise uncompressed self health/urgency data, for example, wherein the environment health/urgency data further comprises uncompressed environment health/urgency data. The autonomic manager communications component can transmit the environment health/urgency data and the self health/urgency data together. The autonomic manager communications component can encapsulate the environment health/urgency data and the self health/urgency data in a packet. The pulse monitor signal can comprise at least one of an urgency signal, an environmental condition, and an event condition. 
     In some embodiments, a computer-accessible medium is provided having executable instructions to construct an environment to satisfy increasingly demanding external requirements. The executable instructions can be capable of directing a processor to perform the functions of instantiating a first embryonic evolvable neural interface; and evolving the first embryonic evolvable neural interface towards complex connectivity, wherein the evolvable neural interface receives one or more heart beat monitor signal, pulse monitor signal, and autopoiesis signals. The evolvable neural interface can generate one or more heart beat monitor signal, pulse monitor signal, and autopoiesis signal, wherein the first evolvable neural interface receives an autopoiesis signal from a second evolvable neural interface. If the first evolvable neural interface does not receive a do-not-self-generate reprieve signal after a predetermined period of time, the first evolvable neural interface can undergo autopoiesis. The embryonic evolvable neural interface can further comprise a neural thread possessing only a primitive and minimal connectivity. The autopoiesis signal can further comprise a stay-alive/stay-awake signal. 
     In some embodiments, a method is provided for maintaining an autonomic system after destruction of an agent of the system. The method can comprise: determining the potential benefit of having one or more autonomic agent of the system undergo autopoiesis to create a modified agent; sending a request-for-self-esteem-level signal to the one or more autonomic agent; and monitoring the response of the one or more autonomic agent to the request-for-self-esteem-level signal. If the autonomic agent does not receive a do-not-self-generate reprieve signal after a predetermined period of time, the autonomic agent can undergo autopoiesis to create the modified agent. In some cases, the one or more autonomic agent is a messenger agent, and the modified agent is a ruler agent. In some cases, in response to the request-for-self-esteem-level signal, the one or more autonomic agent can send a signal indicative of a level of self-esteem that is below a threshold level, and if so, the method can further comprise then sending a do-not-self-generate signal to the one or more autonomic agent. In some cases, in response to the request-for-self-esteem-level signal, the one or more autonomic agent sends a signal indicative of a level of self-esteem that is above a threshold level, and the method then further comprises: sending a do-not-self-generate reprieve signal to the one or more autonomic agent; and causing the one or more autonomic agent to undergo autopoiesis. 
     According to yet other embodiments of the present teachings, a computer-accessible medium is provided having executable instructions to protect an autonomic system when encountering one or more autonomic agent. The executable instructions can be capable of directing a processor of an autonomic agent to perform the functions of: sending a request-for-self-esteem-level signal to the autonomic agent; monitoring the response of the autonomic agent to the request-for-self-esteem-level signal; and determining the autonomic agent potential for autopoiesis. Then, if the autonomic agent does not receive a do-not-autopoiesis reprieve signal after a predetermined period of time, the autonomic agent can undergo autopoiesis. The computer-accessible medium can further comprise instructions for: controlling the autonomic system based on the autonomic agent potential for autopoiesis, wherein a request-for-self-esteem-level signal is a request for the autonomic agent to undergo autopoiesis. The controlling the autonomic system can further comprise granting the autonomic agent access to certain resources; and generating a signal to the autonomic agent to transmit an autopoiesis signal. In some cases, the controlling the autonomic system can further comprise generating a signal to the autonomic agent to withdraw the autopoiesis signal. 
     According to various embodiments of the present teachings, a computer-accessible medium is provided having executable instructions for managing a self-similar neural system based on its functioning status and operating state. The computer-accessible medium can comprise: computer executable self-similar neural code to generate one or more stay-alive signals based on the functioning status and operating state of the system, by a processor. The stay-alive signals can include one or more of a withdrawing of a stay-alive signal, an initiating a self-destruct sequence signal, an initiating autopoiesis sequence signal, or a continuing to stay alive signal. The one or more stay-alive signals can be based on one or more received signals from the system, and the received signals can be indicative of the functioning status and operating state, to obtain an analysis of the condition of the system. The processor can process the one or more received signals and thereby manage operations and resources of the system. The functioning status of the system can be one or more on signal, off signal, active signal, or inactive signal. The operating state of the system can be indicated by one or more urgency signal, reflex signal, environmental condition, or event condition. An event condition can be one or more incorrect operation, emergent behavior, failure to perform self healing, or likelihood of jeopardizing primary objectives. In some cases, the one or more stay-alive signals comprises an initiating autopoiesis sequence signal that includes instructions for modifying an agent of the system to undergo autopoiesis and become a modified agent. 
     According to various embodiments of the present teachings, a self-protection referred to herein as “self-esteem” is included in an autonomic system to assist in encouraging a secure feeling about utilizing autonomy and autonomicity. A self-managing environment is provided with redundancy and dependability through multiple clone and non-clone versions of self-managing agents (SMA) and services, for example, agents with similar if not identical external functionality. Even cloned SMAS may have the inbuilt ability to adapt and thus evolve into something new. As such a novel Darwinian survival of the fittest inherent property is used in the system. A reward scheme, referred to herein as self-esteem, is provided for the autonomic agents and is enforced based on their performance and trust. As an SMA is more important or successful in accomplishing its goals, the system can reward the SMA by sending a signal instructing the SMA to elevate its self-esteem value. Alternatively, the SMA can be provided with goals, and once a goal is achieved, the SMA can be preprogrammed to then raise its own level of self-esteem. If an SMA is not reaching its goals, or a time period passes without the SMA accomplishing a goal, then similarly the self-esteem of that SMA can be lowered, either based on a signal sent from another agent or based on a preprogrammed protocol. An SMA with greater self-esteem may then be allowed to clone at a greater rate with the opposite occurring for lower self-esteem SMAs. The system can be configured such that the apoptotic (self-destruct/self-sacrifice) mechanism kicks in for an SMA if it has reached a low enough self-esteem, for example, a level of self-esteem that is so low that it falls below a threshold self-esteem needed to ward off self-destruction. 
     The present disclosure provides autonomic/self managing systems comprising an all pervasive lower/middle-ware that performs self-configuration, self-healing, self-optimizing, and self-protecting and thus enables the higher level goal of automation and autonomy to adaptively occur. To ensure survivability, dependability and safety, the autonomic systems comprise static agents bound to system components with feedback control loops, mobile agents, deliveries of self-management updates, heartbeat &amp; pulse monitoring, and apoptotic mechanisms. The present teachings add to these features a survival of the fittest reward scheme. The system can provide redundancy and flexibility and be self-managing through different coding implementations of agents, for example, agents having the same external functionality or agents that provide the same service. A measure of performance and trust can be provided to guide the systems self-managing behavior. In an exemplary system, a first self managing agent (SMA- 1 ) and a second self managing agent (SMA- 2 ) are each tasked with photographing craters on the surface of a planet. SMA- 1  finds and photographs  200  craters on the Western hemisphere of the planet whereas SMA- 2  finds and photographs only three craters on the Eastern hemisphere of the planet. After a predetermined period of three months, SMA- 1  and SMA- 2  each look into their respective information repositories to see whether their work entitles them to a self-esteem reward. The information repository indicates that each hemisphere of the planet was estimated to have about 190 craters, so SMA- 1  found most, if not all, of the craters and is rewarded with an elevated self-esteem level, for example, a self-esteem value increase of two points. SMA- 2  barely found any craters at all, and is thus penalized by reducing its self-esteem level, for example, by decreasing its self-esteem value by two points. SMA- 2  could be malfunctioning, or the estimate might be wrong such that there is not anywhere close to 190 craters in the Eastern hemisphere. Either way, the value of SMA- 2  to the scientific mission of mapping and photographing craters on the planet is far less than the value of SMA- 1  to the mission. As a result, the overall system including SMA- 1  and SMA- 2  has more trust in SMA- 1 , and that trust is measured by the self-esteem level. Therefore, if there is a need to send one SMA to a different planet to map and photograph craters, SMA- 1  would be considered a better candidate for that mission due to its higher level of self-esteem. Furthermore, if the system is to be simplified be deactivating or self-destructing one or more SMA, SAM- 2  would be a better candidate for deactivation or self-destruction as its level of self-esteem indicates it is less important and/or valuable to the overall system compared to SMA- 1 . Also, if a need exists for an SMA to self-create a new agent by autopoiesis, SMA- 1  would be a better candidate for autopoiesis compared to SMA- 2  because of the higher level of self-esteem that had been rewarded to SMA- 1 . The new agent might be an asteroid belt, photographing agent having different goals and programming than had been provided for either SMA- 1  or SMA- 2 . Regardless, however, of the goals of the new agent, the respective self-esteem levels provide the overall system with greater confidence that SMA- 1  can perform the new task as opposed to SMA- 2 . 
     According to various embodiments, a method for maintaining an autonomic system comprising a plurality of self-managing agents, is provided. The method can comprise: measuring the performance, trust, or both, of each self-managing agent of the autonomic system; sending a request-for-self-esteem-level signal to each of the one or more self-managing agents; monitoring the response of each self-managing agent to the respective request-for-self-esteem-level signal; and modifying the autonomic system based on the responses. The measuring can comprise measuring the performance of each self-managing agent of the autonomic system. The one or more of the responses can indicate that one or more of the self-managing agents are faulty, below average, not optimal, or outside an acceptable range of values, based on a respective level of self-esteem that is below a threshold value. The one or more of the responses can indicate that one or more of the self-managing agents are fit or not fit for a particular task, mission, ability, or command level, based on a respective level of self-esteem. Modifying the autonomic system can comprise causing one or more of the self-managing agents to self-destruct, for example, if it is determined from the response that the self-managing agent is not fit for a particular task or is faulty. The one or more responses can indicate that one or more of the self-managing agents has a higher level of self-esteem than one or more of the other self-managing agents, and in such a case, the modifying the autonomic system can comprise causing the one or more self-managing agents with the higher level of self-esteem to receive a self-destruct reprieve signal. In some cases, the one or more responses can indicate that one or more of the self-managing agents has a higher level of self-esteem than one or more of the other self-managing agents, and the modifying the autonomic system can comprise causing the one or more self-managing agents with the higher level of self-esteem to undergo autopoiesis. According to some embodiments, an autonomic system is provided that comprises a plurality of self-managing agents and is configured to carry out such a method based on levels of self-esteem, for example, based on relative levels of self-esteem. 
     In some embodiments, a computer-accessible medium is provided having executable instructions to protect an autonomic system when encountering one or more self-managing agents of the autonomic system. The executable instructions can be configured to direct a processor of an autonomic agent to perform the functions of: measuring the performance, trust, or both, of each of the self-managing agents encountered; sending a request-for-self-esteem-level signal to each of the self-managing agents encountered; monitoring the response of each self-managing agent to the respective request-for-self-esteem-level signal; and modifying the autonomic system based on the response from each self-managing agent encountered. The executable instructions can be configured to direct a processor of an autonomic agent to measure the performance of each self-managing agent of the autonomic system. The executable instructions can be configured to direct a processor of an autonomic agent to modify the autonomic system to cause a faulty, below average, not optimal, unacceptable, below threshold, or unfit self-managing agent to self-destruct based on a respective level of self-esteem. For example, the self-managing agent can self-destruct if it is not fit for a particular task, mission, ability, or command level. The executable instructions can be configured to direct a processor of an autonomic agent to modify the autonomic system to cause one or more self-managing agents having a relatively higher level of self-esteem to receive a self-destruct reprieve signal. The executable instructions can be configured to direct a processor of an autonomic agent to modify the autonomic system to cause one or more self-managing agents having a relatively higher level of self-esteem to undergo autopoiesis. 
     Given that the agents can, in some situations, self-adapt, and that future versions will evolve to allow the system to self-generate agents, a measure of performance and trust can be especially important. As such, the self-esteem of each autonomic agent of an autonomic system can be used as a method of measuring the success of each agent at doing its tasks and adapting to new ones. As a result, successful agents can be cloned enabled to multiply whereas unsuccessful agents can be enabled to die out by triggering an apoptosis (pre-programmed death) mechanism based on a low self-esteem. A self-managing system can be provided with the self-* property of autopoiesis (self-creation) and the autopoiesis can be based on the relative self-esteems of plurality candidate replacement agents. In the event of an agent in the system self-destructing, the autopoiesis property auto-generates a replacement, and if an existing agent is formed into the replacement the selection of the agent can be based on its self-esteem level, for example, compared to the self-esteem level of other possible replacements. 
     The present teachings are not only applicable, in autonomic systems, to downstream activities such as self-managing mission data farms and mission control servers, but also to next generation mission classes, for example, SWARM based multiple craft missions. Accordingly, the present teachings also encompasses applications in missions such as ANTS which use high numbers of cooperating entities having inbuilt adaptability and requiring a self-managing operating system to ensure the success of scientific goals. 
     According to yet other embodiments of the present teachings, a biologically inspired self-management mechanism is provided for autonomous and autonomic systems, which is referred to herein as “autonomic adhesion.” Following biological inspiration, cellular adhesion is the binding of a cell to a surface, extracellular matrix, or another cell using cell adhesion molecules such as selectins, integrins, and cadherins. Correct cellular adhesion is essential in maintaining multicellular structure. Cellular adhesion can link the cytoplasm of cells and can be involved in signal transduction. Taking inspiration (and not mimicry) and applying this to autonomic agents or craft, this can essentially be considered akin to agents that like or dislike each other. The present teachings provide a self-managing system where there are a high volume of distributed autonomic/self-managing mobile agents, and autonomic adhesion is used to attract similar agents together or to repel dissimilar agents from an event horizon. For instance, similar types of SMAs that can deal with various types of viruses would be attracted to each other at a local to deal with a virus outbreak, while at the same time repelling other SMAs such as self-optimization agents from the busy scene to avoid congestion. As a result, the proper processor cycles can be dedicated to the incident. 
     In an exemplary embodiment, an autonomic system can provide a high resolution composite photograph of a nebula by using multiple cameras from multiple spacecraft of a swarm. Different agents or spacecrafts of the swarm can receive signals requesting all spacecraft with infrared cameras to adhere together so the composite photograph can be taken. At the same time, those spacecraft of the swarm that do not have infrared cameras are not needed and would be in the way of the others that are to be used in generating the composite photograph. As a result, those spacecraft of the swarm that do not have infrared cameras may receive the same “adhered at location ‘X’ if you have an infrared camera” but they would interpret the signal as “repel from the group” because they do not have an infrared camera. En some cases, rather than each agent receiving the same signal, different signals can be sent to and/or received by each agent of the swarm, for example, depending on the hardware, firmware, software, or a combination thereof, known to exist in each particular agent. So agents with infrared cameras might receive an “adhere at rally point A” signal whereas agents without an infrared camera might receive a “stay away from rally point A” signal. 
     Just as the cellular adhesion has signal transduction, the autonomic adhesion adds adhesion signaling to existing autonomic signals (I am alive, I am healthy, lubdub of I &amp; environment are healthy, stay alive, self-destruct, stay awake, self-sleep. ALice signal and so forth). 
     The autonomic adhesive mechanism is a self-* function that enables dynamic clustering of similar self-managing agents or crafts, so that they can together deal with a significant self-managing task or incident, while repelling differential SMAs or crafts and thus prioritizing available locally situated bandwidth and processor power to a task or incident at hand. 
     According to various embodiments, a method is provided for maintaining an autonomic system comprising a plurality of self-managing agents. The method can comprise: sending an adhere or repel signal to each of the plurality of self-managing agents; causing one or more of the plurality of self-managing agents to rendezvous at the locale if the self-managing agent meets the requirements for adhesion; and causing one or more of the plurality of self-managing agents to repel away from the locale if the self-managing agent does not meet the requirements for adhesion. The adhere or repel signal can comprise information pertaining to requirements for adhesion and a locale for rendezvous purposes. The method can further comprise causing two or more of the self-managing agents that rendezvous at the locale to work together to accomplish an objective. The requirements can comprise one or more hardware requirements, firmware requirements, software requirements, a combination thereof, a scientific instrument requirement, or the like. The method can further comprise causing two or more of the self-managing agents that rendezvous at the locale to share at least one resource with each other, for example, a power resource, a computer processing resource, a scientific instrument, battery power, fuel, a solar panel, or the like. In some cases, an autonomic system is provided that comprises a plurality of self-managing agents configured to carry out such a method. 
     While examples of “adhesion” are described herein that relate to different components of a system gathering physically close together, it is to be understood that “adhesion” can also more generally mean that components of a system would be drawn together to work together, even if they are not gathered closely together. In the case of ANTS and similar missions and missions using low power, short distance transmissions, and the like limits, the components can benefit from a rendez-vous close together. “Adhesion,” as used herein, encompasses components that come together, are inclined to work together, or both, even if not in the same physical location, for example, to form a daisy chain of receiving and transmitting points through space. Self-adhesion means that different components may or may not rendezvous physically closely together but are predisposed to working together even if not in the same physical location. Similarly, “repel” does not necessarily mean to physically move away from one another, but instead, can more generally mean to not cooperate with one another. 
     According to yet other embodiments, a computer-accessible medium is provided having executable instructions to protect an autonomic system when encountering one or more self-managing agents of the autonomic system. The executable instructions can be configured to direct a processor of an autonomic agent to perform the functions of: sending an adhere or repel signal to each of the plurality of self-managing agents; causing one or more of the plurality of self-managing agents to rendezvous at the locale if the self-managing agent meets the requirements for adhesion; and causing one or more of the plurality of self-managing agents to repel away from the locale if the self-managing agent does not meet the requirements for adhesion. The adhere or repel signal can comprise information pertaining to requirements for adhesion and a locale for rendezvous purposes. The requirements can comprise one or more hardware requirements, firmware requirements, software requirements, a combination thereof, a scientific instrument requirement, or the like. The executable instructions can be configured to direct a processor of an autonomic agent to cause two or more of the self-managing agents that rendezvous at the locale to share at least one resource with each other, for example, a power resource, a computer processing resource, a scientific instrument, battery power, fuel, a solar panel, or the like. 
     According to yet other embodiments of the present teachings, apoptotic computing and apoptotic communications as provided in the patent described and incorporated herein is included in an apoptotic system specifically useful for data and digital objects and can provide, in essence, an “expiry date”. These teachings are not only useful in general for privacy and safety in the modem world but also useful for controlling the loaning and use of space scientific data to researchers. An apoptotic mechanism is hereby provided for data and digital objects, for example, data files, rental movie files, rental music files, limited licensing rights, time-based licensed access, distributed dynamic data, and the like. Although these teachings may seem counter to an objective of preserving data, an inherent death by default for such data and digital objects can be a useful property for dealing with ethics, security, and trust issues. In some embodiments, data can effectively remain in existence, for example, available or out there on the internet, only if it receives periodic stay alive credentials. Otherwise, the data could expire, be subject to password access only, or locked-down. In some embodiments, the system and method can be utilized for licensing and/or loaning scientific data to users, for example, lending or loaning NASA space mission data. 
     Effectively, valuable datasets can be made to have an expiry date or other safeguard if users of the data do not correctly acknowledge, credit, or cite the data creators, and thus the stay alive could be removed. This innovation not only applies to data but to all digital objects such as pictures, video or music files. Apoptotic Computing may be considered a new sub field of Autonomic Computing. The present teachings have particular applicability for on-line shopping services, on-line music services, on-line distributors, and the like. 
     According to various embodiments, a computer-accessible medium is provided having executable instructions for managing digital data or a digital object, for example, based on functioning status and operating state. The computer-accessible medium can comprise: computer executable self-similar neural code to generate one or more stay-alive signals based on the functioning status and operating state, by a processor. The stay-alive signals can include one or more of a withdrawing-of-access signal, an initiating-of-self-destruct signal, or a continuing-to-provide-access signal. The at least one stay-alive signal can be based on one or more received signals from a user. The received signals can be indicative of the accessibility to the digital data or digital object, which is granted to the particular user. The processor can process the one or more received signals and thereby managing operations and accessibility of the digital data or digital object. In some cases, the received signals indicate the user has complete, unrestricted access to the entirety of the digital data or digital object, or limited access to the digital data or digital object, or no access to the digital data or digital object, or access to a only a selected portion of the digital data or digital object. The received signals can indicate that the user has access only to an abstract of the digital data or digital object, or access for only a limited time, for example, 24 hours. The digital data or digital object can comprise photographs, scientific data from a space mission, a video file, a music file, an audio file, or the like. The one or more received signals from a user can comprise an IP address, a password, a user name, or a combination thereof. In some embodiments, a computer processing system is provided that comprises a memory having stored therein the computer-accessible medium described above. 
     According to yet other embodiments, a method is provided for managing access to digital data or to a digital object. The method can comprise: receiving signals from a user; processing the one or more received signals and managing operations and accessibility of the digital data or digital object based on functioning status and operating state. The received signals can be indicative of the accessibility to the digital data or digital object, which is granted to the user. The processing can comprise running computer executable self-similar neural code to generate one or more stay-alive signals based on the functioning status and operating state. The stay-alive signals can include one or more of a withdrawing-of-access signal, an initiating-of-self-destruct signal, or a continuing-to-provide-access signal. The at least one stay-alive signal can be based on one or more received signals from a user, and the received signals can be indicative of the accessibility to the digital data or digital object, which is granted to the user. 
     In various embodiments of the present teachings, a method for managing a system includes receiving a potentially harmful signal and transmitting an otoacoustic signal to counteract the potentially harmful signal. In other embodiments, an autonomic element includes a self-monitor that is operable to receive information from sensors and is operable to monitor and analyze the sensor information and access a knowledge repository, a self-adjuster operably coupled to the self-monitor in a self-control loop, the self-adjuster operable to access the knowledge repository, the self-adjuster operable to transmit data to effectors, and the self-adjuster operable to plan and execute, an environment monitor that is operable to receive information from sensors and operable to monitor and analyze the sensor information and access the knowledge repository, and an autonomic manager communications component operably coupled to the environment monitor in an environment control loop, the autonomic manager communications component operable to access the knowledge repository, the autonomic manager communications component also operable to produce and transmit a counteracting signal to an incoming harmful signal. 
     In yet other embodiments, an autonomic system includes a plurality of autonomic agents performing one or more programmed tasks. The autonomic system also includes a coordinating autonomic agent for assigning programmed task and for issuing instructions to the plurality of autonomic agents. The autonomic system also includes a messenger autonomic agent for facilitating communication between the coordinating autonomic agent, plurality of autonomic agents, a remote system. One or more programmed task performed by the plurality of autonomic objects is at least generating signals indicative of a potentially harmful signal. The coordinating autonomic agent transmits an otoacoustic signal to one or more of the plurality of autonomic agents, based on the generated signals. 
     In still yet other embodiments, an autonomous nanotechnology swarm includes a first worker composed of self-similar autonomic components. The autonomous nanotechnology swarm also includes a second worker composed of self-similar autonomic components. The autonomous nanotechnology swarm also includes a third worker composed of self-similar autonomic components. In the autonomous nanotechnology swarm, the third worker facilitates communication between the first worker and the second worker. In the autonomous nanotechnology swarm, the first worker generates a heart beat monitor signal and pulse monitor signal. In the autonomous nanotechnology swarm, the second worker includes an otoacoustic component that is operable to counteract a harmful signal. 
     In further embodiments, a method includes instantiating an embryonic evolvable neural interface. The method also includes evolving the embryonic evolvable neural interface towards complex complete connectivity. The evolvable neural interface receives one or more heart beat monitor signal, pulse monitor signal, and otoacoustic signal. The evolvable neural interface generates one or more heart beat monitor signal, pulse monitor signal, and otoacoustic signals. 
     In yet a further embodiment, a method for protecting an autonomic system when encountering one or more autonomic agents includes determining the potential harm of the autonomic agent. The method also includes sending an otoacoustic signal to the autonomic agent and monitoring the response of the autonomic agent to the otoacoustic signal. 
     In still yet a further embodiment, a system includes a processor and a storage device coupled to the processor. The system also includes software means operative on the processor for sending an otoacoustic signal to the autonomic agent, monitoring the response of the autonomic agent to the otoacoustic signal, and determining the autonomic agent potential for causing harm to the autonomic system. 
     System Level Overview 
       FIG. 1  is a block diagram that provides an overview of an evolvable synthetic neural system to manage collective interactions between autonomous entities, according to an embodiment. System  100  can include a first plurality of neural basis functions (NBFs)  102  and  104 . NBFs are the fundamental building block of system  100 . In some embodiments of system  100 , the plurality of NBFs includes more than the two NBFs  102  and  104  shown in  FIG. 1 . In some embodiments, system  100  includes only one NBF. One embodiment of a NBF is described below with reference to  FIG. 2 . 
     System  100  can also include a firstinter-evolvable neural interface (ENI)  106  that is operably coupled to each of the first plurality of neural basis functions. The NBFs  102  and  104  can be highly integrated, and coupling between the NBFs through the ENI  106  provides a three dimensional complexity. Thus, for example, then system  100  is implemented on microprocessors such as microprocessor  804  described below with reference to  FIG. 8 , system  100  can provide a synthetic neural system that reconciles the two dimensional nature of microprocessor technologies to the three dimensional nature of biological neural systems. 
     This embodiment of the inter-ENI  106  can be known as an inter-NBF ENI because the inter-ENI  106  is illustrated as being between or among the NBFs  102  and  104  at the same level within a hierarchy. System  100  shows only one level  108  of a hierarchy, although one skilled in the art will recognize that multiple hierarchies can be used within the scope of this invention. 
     System  100  can also operate autonomously. A system operates autonomously when the system exhibits the properties of being self managing and self governing, often termed as autonomic, pervasive, sustainable, ubiquitous, biologically inspired, organic or with similar such terms. ENI  106  can adapt system  100  by instantiating new NBFs and ENIs and establishing operable communication paths  110  to the new NBFs and the ENIs to system  100 . ENI  106  can also adapt system  100  by removing or disabling the operable communication paths  110  to the new NBFs and ENIs. The adapting, establishing, removing and disabling of the communication paths  110  can be performed autonomously. Thus, system  100  can satisfy the need for a synthetic neural system that performs significant tasks with complete autonomy. 
     System  100  can be capable of establishing and removing links to other similarly configured systems (not shown). Thus, the system  100  can be described as self-similar. 
     The system level overview of the operation of an embodiment is described in this section of the detailed description. Some embodiments can operate in a multi-processing, multi-threaded operating environment on a computer, such as computer  802  in  FIG. 8 . 
     While the system  100  is not limited to any particular NBF or ENI, for sake of clarity simplified NBFs and a simplified ENI are described. 
     Apparatus Embodiments 
     In the previous section, a system level overview of the operation of an embodiment is described. In this section, particular apparatus of such an embodiment are described by reference to a series of block diagrams. Describing the apparatus by reference to block diagrams enables one skilled in the art to develop programs, firmware, or hardware, including such instructions to implement the apparatus on suitable computers, and executing the instructions from computer-readable media. 
     In some embodiments, apparatus  200 - 600  are implemented by a program executing on, or performed by firmware or hardware that is a part of a computer, such as computer  802  in  FIG. 8 . 
       FIG. 2  is a block diagram of a neural basis function (NBF)  200  of a worker according to an embodiment. NBF  200  is illustrated as a hi-level neural system because both high-level functions and low-level functions are performed by NBF  200 . 
     NBF  200  can include an intra-evolvable neural interface (intra-ENI)  202 . The ENI  202  can be operably coupled to a heuristic neural system (HNS)  204  and operably coupled to an autonomous neural system (ANS)  206 . The HNS  204  can perform high-level functions and the ANS  206  performs low-level functions that are often described as “motor functions” such as “motor”  1510  in  FIG. 15  below. In NBF  200 , the HNS  204  and the ANS  206  in aggregate can provide a function of a biological neural system. The intra-ENI  202  shown in  FIG. 2  is an ENI that is wholly contained within an NBF, and is therefore prefixed with “intra.” 
     The intra-ENI  202  can send action messages to and receive request messages from the HNS  204  and the ANS  206  during learning and task execution cycles, as well as during interfacing operations between the intra-ENI and the FINS  204  and the ANS  206  when the HNS  204  and the ANS  206  need to be modified as a result of other system failures or modification of objectives. NBF  200  is illustrated as a worker NBF because this NBF performs functions, but does not provide instructions commands to other NBFs. 
       FIG. 3  is a block diagram of a heuristic neural system  300  according to an embodiment. 
     The heuristic neural system GINS)  300  can be composed of a neural net  302  for pattern recognition and a fuzzy logic package  304  to perform in decisions based on recognitions. Taken together the neural net  302  and the fuzzy logic package  304  can form basis for a higher level heuristic intelligence. 
       FIG. 4  is a Mock diagram of an autonomous neural system  400  according to an embodiment. 
     The autonomous neural system (ANS)  400  can include a non-linear dynamics simulation  402  that represents smart servo system behavior. 
       FIG. 5  is a block diagram of a neural basis function (NBF)  500  of a worker according to an embodiment. NBF  500  is shown as a bi-level neural system. 
     In some embodiments, NBF  500  can include a self assessment loop (SAL)  502  at each interface between autonomic components. Each SAL  502  can continuously gauge efficiency of operations of the combined HNS  204  and ANS  206 . The standards and criteria of the efficiency can be set or defined by objectives of the NBF  500 . 
     In some embodiments, NBF  500  can also include genetic algorithms (GA)  504  at each interface between autonomic components. The GAs  504  can modify the intra-ENI  202  to satisfy requirements of the SALs  502  during learning, task execution or impairment of other subsystems. 
     Similarly, the HNS  204  can have a SAL  502  interface and a GA  504  interface to a core heuristic genetic code (CHGC)  506 , and the ANS  206  can have a SAL  502  interface and a GA  504  interface to a core autonomic genetic code (CAGC)  508 . The CHGC  506  and CAGC  508  can allow modifications to a worker functionality in response to new objectives or injury. The CHGC  506  and the CAGC  508  autonomic elements cannot be part of an operational neural system, but rather can store architectural constraints on the operating neural system for both parts of the bi-level system. The CHGC  506  and the CAGC  508  can both be modifiable depending on variations in sensory inputs via GAs  504 . 
     In some embodiments, the CHGC  506  and the CAGC  508  in conjunction with SALs  502  and GAs  504  can be generalized within this self similar neural system to reconfigure the relationship between NBFs as well as to permit the instantiation of new NBFs to increase the overall fitness of the neural system. Thus, NBF  500  can provide a form of evolution possible only over generations of BNF workers. 
     In some embodiments, NBF  500  can also include genetic algorithms  510  and  512  that provide process information to the CHGC  506  and the CAGC  508 , respectively. HNS  204  and ANS  206  can receive sensory input  514  and  516 , respectively, process the sensory input and generate high level actions  518  and low level actions  520 , respectively. 
       FIG. 6  is a block diagram of a multiple level hierarchical evolvable synthetic neural system (ESNS)  600  according to an embodiment. 
     The multiple level hierarchical ESNS  600  can include a first level of hierarchy  602  that includes a NBF  604  and inter-ENI  606  and a ruler NBF  608 . A ruler NBF, such as ruler NBF  608  can perform functions and also provide instructions commands to other subordinate NBFs. 
     The ruler NBF  608  of the first hierarchical level  602  is illustrated as being operably coupled to a ruler NBF  610  in a second hierarchical level  612 . Ruler NBF  610  can perform functions, receive instructions and commands from other ruler NBFs that are higher in the hierarchy of the ESNS  600  and also provide instructions commands to other subordinate NBFs. 
     The second hierarchical level  612  can also include an inter-ENI  614 . The second hierarchical level  612  of  FIG. 6  shows the embodiment of an ESNS  600  having one NBF operably coupled to an ENI. The ruler NBF  610  of the second hierarchical level  612  can be operably coupled to a ruler NBF  616  in a third hierarchical level  618 . 
     The third hierarchical level  616  can also include an inter-ENI  620 . The third hierarchical level  616  of  FIG. 6  shows the embodiment of an ESNS  600  having more than two NBFs (e.g.  616 ,  622  and  624 ) operably coupled to an ENI. 
     In some embodiments, the NBFs  604 ,  608 ,  610 ,  616 ,  622  and  624  can include the aspects of NBFs  102  and  104  in  FIG. 1  above, and/or NBF  200  in  FIG. 2  above. One skilled in the art will appreciate that many combinations exist that fall within the purview of this invention. 
     Hardware and Operating Environments 
       FIGS. 7 ,  8 ,  9  and  10  are diagrams of hardware and operating environments in which different embodiments can be practiced. The description of  FIGS. 7 ,  8 ,  9  and  10  provide an overview of computer hardware and suitable autonomic computing environments in conjunction with which some embodiments can be implemented. Embodiments are described in terms of a computer executing computer-executable instructions. However, some embodiments can be implemented entirely in computer hardware in which the computer-executable instructions are implemented in read-only memory. Some embodiments can also be implemented in client/server autonomic computing environments where remote devices that perform tasks are linked through a communications network. Program modules can be located in both local and remote memory storage devices in a distributed autonomic computing environment. Those skilled in the art will know that these are only a few of the possible computing environments in which the invention can be practiced and therefore these examples are given by way of illustration rather than limitation. 
       FIG. 7  is a block diagram of a computer cluster environment  700  in which different embodiments can be practiced. System  100 , apparatus  200 ,  300 ,  400 ,  500 ,  600 , method  2000  and ESNS  1100  and  1200  can be implemented on computer cluster environment  700 . 
     Computer cluster environment  700  can include a network  702 , such as an EtherFast 10/100 backbone, that is operably coupled to a cluster server  704  and a plurality of computers  706 ,  708 ,  710  and  712 . One possible embodiment of the computers is computer  802  described below with reference to  FIG. 8 . The plurality of computers can include any number of computers, but some implementations can include 7, 16, 32 and as many as 512 computers. The ESNSs and NBFs described above can be distributed on the plurality of computers. 
     One example of the computer cluster environment  700  is a Beowolf computer cluster. The computer cluster environment  700  provides an environment in which a plurality of ESNSs and NBFs can be hosted in an environment that facilitates cooperation and communication between the ESNSs and the NBF&#39;s. 
       FIG. 8  is a block diagram of a hardware and operating environment  800  in which different embodiments can be practiced. Computer  802  can include a processor  804 , which can be a microprocessor, commercially available from Intel, Motorola, Cyrix and others. Computer  802  can also include random-access memory (RAM)  806 , read-only memory (ROM)  808 , and one or more mass storage devices  810 , and a system bus  812 , that operatively couples various system components to the processing unit  804 . The memory  806 ,  808 , and mass storage devices,  810 , are illustrated as types of computer-accessible media. Mass storage devices  810  can be more specifically types of nonvolatile computer-accessible media and can include one or more hard disk drives, floppy disk drives, optical disk drives, and tape cartridge drives. The processor  804  can execute computer programs stored on the computer-accessible media. 
     Computer  802  can be communicatively connected to the Internet  814  via a communication device  816 . Internet  814  connectivity is well known within the art. In one embodiment, a communication device  816  can be a modem that responds to communication drivers to connect to the Internet via what is known in the art as a “dial-up connection.” In another embodiment, a communication device  816  can be an Ethernet® or similar hardware network card connected to a local-area network (LAN) that itself is connected to the Internet via what is known in the art as a “direct connection” (e.g., T1 line, etc.). 
     A user can enter commands and information into the computer  802  through input devices such as a keyboard  818  or a pointing device  820 . The keyboard  818  can permit entry of textual info into computer  802 , as known within the art, and embodiments are not limited to any particular type of keyboard. Pointing device  820  can permit the control of the screen pointer provided by a graphical user interface (GUI) of operating systems such as versions of Microsoft Windows®, Embodiments are not limited to any particular pointing device  820 . Such pointing devices can include mice, touch pads, trackballs, remote controls and point sticks. Other input devices (not shown) could include a microphone, joystick, game pad, satellite dish, scanner, or the like. 
     In some embodiments, computer  802  can be operatively coupled to a display device  822 . Display device  822  can be connected to the system bus  812 . Display device  822  permits the display of information, including computer, video and other information, for viewing by a user of the computer. Embodiments are not limited to any particular display device  822 . Such display devices can include cathode ray tube (CRT) displays (monitors), as well as flat panel displays such as liquid crystal displays (LCDs). In addition to a monitor, computers can typically include other peripheral input/output devices such as printers (not shown). Speakers  824  and  826  provide audio output of signals. Speakers  824  and  826  can also be connected to the system bus  812 . 
     Computer  802  can also include an operating system (not shown) that could be stored on the computer accessible media RAM  806 , ROM  808 , and mass storage device  810 , and can be and executed by the processor  804 . Examples of operating systems include Microsoft Windows®, Apple MacOS®, Linux®, UNIX®. Examples are not limited to any particular operating system, however, and the construction and use of such operating systems are well known within the art. 
     Embodiments of computer  802  are not limited to any type of computer  802 . In varying embodiments, computer  802  can comprise a PC-compatible computer, a MacOS®—compatible computer, a Linux®-compatible computer, or a UNIX®-compatible computer. The construction and operation of such computers are well known within the art. 
     Computer  802  can be operated using at least one operating system to provide a graphical user interface (GUI) including a user-controllable pointer. Computer  802  can have at least one web browser application program executing within at least one operating system, to permit users of computer  802  to access an intranet, extranet or Internet world-wide-web pages as addressed by Universal Resource Locator (URL) addresses. Examples of browser application programs include Netscape Navigator® and Microsoft Internet Explorer®. 
     The computer  802  can operate in a networked environment using logical connections to one or more remote computers, such as remote computer  828 . These logical connections can be achieved by a communication device coupled to, or a part of, the computer  802 . Embodiments are not limited to a particular type of communications device. The remote computer  828  could be another computer, a server, a router, a network PC, a client, a peer device or other common network node. The logical connections depicted in  FIG. 8  include a local-area network (LAN)  830  and a wide-area network (WAN)  832 . Such networking environments are commonplace in offices, enterprise-wide computer networks, intranets, extranets and the Internet. 
     When used in a LAN-networking environment, the computer  802  and remote computer  828  can be connected to the local network  830  through network interfaces or adapters  834 , which is one type of communications device  816 . Remote computer  828  can also include a network device  836 . When used in a conventional WAN-networking environment, the computer  802  and remote computer  828  can communicate with a WAN  832  through modems (not shown). The modem, which can be internal or external, is connected to the system bus  812 . In a networked environment, program modules depicted relative to the computer  802 , or portions thereof, can be stored in the remote computer  828 . 
     Computer  802  can also include power supply  838 . Each power supply can be a battery. 
       FIG. 9  is a block diagram of a multiprocessor hardware and operating environment  900  in which different embodiments can be practiced. Computer  902  can include a plurality of microprocessors, such as microprocessor  804 ,  904 ,  906 , and  908 . The four microprocessors of computer  902  can be one example of a multi-processor hardware and operating environment; other numbers of microprocessors can be used in other embodiments. 
     Similar to the computer cluster environment  700  in  FIG. 7  above, the computer  902  can provide an environment in which a plurality of ESNSs and NBFs can be hosted in an environment that facilitates cooperation and communication between the ESNSs and the NBFs. 
       FIG. 10  is a block diagram of a hardware and operating environment  1000  which can include a quiese component, according to an embodiment. The hardware and operating environment  1000  reduces the possibility that an autonomic element will jeopardize the mission of the autonomic unit. 
     A quiesce component  1002  of an autonomic unit can render the autonomic unit inactive for a specific amount of time or until a challenging situation has passed. The quiesce component  1002  can be invoked when either an external supervisory entity or the autonomic unit itself determines that the autonomic unit could best serve the needs of the swarm by quiescing. Quiescing can render the autonomic unit temporarily inactive or disabled. Thus, the quiesce component  1002  can reduce the possibility that an autonomic element will jeopardize the mission of the autonomic element by deactivation or inactivating the autonomic element. 
     Quiesce time can be defined as the length of time taken to quiesce a system (to render the system inactive), or the length of time between periods of activity (i.e., the length of time of inactivity). The quiescing can be somewhat analogous to the cell lifecycle, were cells can stop dividing and go into a quiescent state. 
     Components of the system  100 , apparatus  200 ,  300 ,  400 ,  500 ,  600 ,  1000 ,  1400 ,  1200 ,  1300 ,  1400 ,  1500  and  1600  and methods  1700 ,  1800 ,  1900 ,  2000 ,  2100 ,  2200 ,  2300 ,  2400 ,  2500  and  2600  can be embodied as computer hardware circuitry or as a computer-readable program, or a combination of both. 
     More specifically, in one computer-readable program embodiment, the programs can be structured in an object-orientation using an object-oriented language such as Java, Smalltalk or C++, and the programs can be structured in a procedural-orientation using a procedural language such as COBOL or C. The software components can communicate in any of a number of ways that are well-known to those skilled in the art, such as application program interfaces (API) or interprocess communication techniques such as remote procedure call (RPC), common object request broker architecture (CORBA), Component Object Model (COM), Distributed Component Object Model (DCOM), Distributed System Object Model (DSOM) and Remote Method Invocation (RMI). The components execute on as few as one computer as in computer  802  in  FIG. 8 , or on at least as many computers as there are components. 
     Implementation of an Evolvable Synthetic Neural System in a Tetrahedral Architecture 
       FIG. 11  is a diagram representation of a plurality of autonomic entities that have been assembled to perform a task. These entities can be self-configuring: adapt automatically to the dynamically changing environments; self-optimizing: monitor and tune resources automatically; self-protecting: anticipate, detect, identify, and protect against attacks from anywhere; and, self-healing: discover, diagnose, and react to disruptions. As shown with reference to autonomic entities  1118  and  1120  autonomic computing can have a self-aware layer and an environment aware layer. The self-aware layer of the autonomic entity (agent or other) can be comprised of a managed component and autonomic manager, which can be an agent, termed a self-managing cell (SMC). Control loops with sensors (self-monitor) and effectors (self-adjuster) together with system knowledge and planning/adapting policies can allow the autonomic entities to be self aware and to self manage. A similar scheme can facilitate environment awareness—allowing self managing if necessary, but without the immediate control to change the environment; this could be affected through communication with other autonomic managers that have the relevant influence, through reflex or event messages. The autonomic entities can be arranged or assigned distinctive roles such as worker entities, coordinating or managing entities, and message entities. Based on the task a ruler entity could be assigned a set of worker entities to manage inclusive of determining if a stay alive signal ought to be withdrawn. Further, the communication between the ruler and the worker can be facilitated through the message entity. The message entity could have the additional task of communicating with a remote system. In the ease of space exploration, the remote system could be mission control on earth, mission control on an orbital platform, or any other arrangement that can facilitate that is external to the collection of autonomic elements. The remote system could be an autonomic entity acting like the project manager for the mission. Communication with mission control will be limited to the download of science data and status information. An example of such a grouping is shown in  FIG. 11  where autonomic entity  1102  is shown as a ruler entity, autonomic entity  1110  as a message entity, and autonomic entities  1118  and  1120  are examples of worker entities. In terms of hardware, these entities can be all identical with the discernable difference being programming to accomplish assigned tasks. An added advantage to having identical hardware is replacing failed entities, which can be accomplished by activating software code found in the autonomic entity. If hardware differences exist they can be based on specialized equipment suitable for a particular task. However, at a minimum, certain functions or roles, such as ruler and messenger, can be expected to be within the skill set of all the autonomic entities. 
     As shown in  FIG. 11 , ruler autonomic entity  1102  can comprise a program or process  1104  executing in ruler entity  1102 . Ruler entity  1102  can be implemented using a data processing system, such as data processing system  902  in  FIG. 9 , or in the form of an autonomous agent compiled by a data processing system. In the alternative, the ruler entity could be an autonomous nano-technology swarm that is launched from a factory ship for exploring planets, asteroids, or comets. Further, an analysis module  1106  or agent as executed by ruler entity  1102  can be used to monitor process  1104  and to receive pulse monitor and heart beat monitor signals from worker entities through the messenger entity. When the analysis module  1106  is used to monitor process  1104  the analysis module  1106  can be to detect errors or problems with the operation of process  1104 . 
     As shown in  FIG. 11 , analysis agent  1106  can include an evaluator or other monitoring engine used to monitor the operation of process  1104 . Analysis agent  1106  can be executed in response to some event. This event can be a periodic event, such as the passage of some period of time, data received from one or more of the worker entities. Further, the event can be the initialization of internal procedures in process  1104  or the starting or restarting of ruler entity  1102 . Depending on the particular implementation, analysis agent  1106  can continuously run in the background monitoring process  1104  and analyzing the worker entity signals. See method  2100  in  FIG. 21  below for actions taken by analysis agent module  1106  in formulating a strategy for the worker entities. Further, analysis agent  1106  can be subject to any self-healing routines found in ruler entity  1102 . 
     This monitoring by analysis agent  1106  can be based on rules stored in behavior storage  1108 , which could be used to compare the actual behavior of the received data to an expected behavior as defined in behavior storage  1108 . In the present arrangement, behavior storage  1108  (ruler entity  1102 ) can be a collection of rules that can be updated by a remote computer through the messenger entity that reflects most current fixes (self-healing) or repair procedures and responses to worker entities upon the occurrence of an event, change in condition, or deviation from a normal operation. Behavior storage  1108  can be narrowly tailored based on the use and purpose of the autonomic entity, such as messenger entity  1110  and have only those procedures needed to perform its programming. 
     When messenger entity connects to remote computer at a command and control station, database  1116  can be updated with information that can later be used to program ruler entity or worker entity. In most cases a copy of the rules in database  1116  contains the most up-to-date information. If the objective changes or a solution to a problem requires an updated version not found within the autonomic entity, the entities can attempt to contact message entity  1110  to see if more recent or up-to-date information is available. If updates are available, these updates can be sent to the requesting entity for processing. 
     The information in behavior storage  1108  and databases in messenger and worker entity can include an array of values that are expected when selected process or operations are implemented in the respective entity. Examples processes can be initializing software, timing requirements, synchronization of software modules, and other metrics that can provide information concerning the running of a process within the respective entity. Examples operations can be data gathering, processing of information, controlling machinery, or any other operation where data processing systems are employed. These expected values can be compared to determine if an error condition has occurred in the operation of the entity. An error condition can be analyzed to determine its causes and possible correction. In the case of a worker entity, the error can be internally analyzed to select the appropriate self-healing procedure and the error can be sent to the ruler entity to be analyzed by analysis agent  1106  using the rules in behavior storage  1108 . Based on the analysis, the ruler entity can elect to either withdraw the stay alive signal to the malfunctioning worker entity or wait a selected period to generate one or more stay alive signal, withdrawal of a stay alive signal, or a self-destruct signal. If the stay alive signal is withdrawn, the malfunctioning entity could be disconnected from the operation and the assigned to another entity or partially performed by the remaining entity to insure its completion. 
       FIG. 12  is a block diagram of an autonomous entity management system  1200  according to an embodiment. The system  1200  can be a generic system because the system  1200  represents a myriad of devices, processes, or device and process that perform a task in accordance to its programming or design. The illustrated system  1200  represents an instance when an autonomous system  1204  encounters an anonymous autonomic agent  1202 . An anonymous autonomous agent can be a visiting agent, a mobile agent that can enter the sphere of influence of the autonomous system  1204 , or any device for which the autonomous system  1204  has no established relationship. Example encounters can be a wireless device (agent) and communication tower (system), a client and server, a video subscriber and video provider, a process and an operating system. System  1200  manages autonomous entities that can be functionally extracted from an environment upon the occurrence of a predetermined condition such as a potential security breach. 
     The autonomous system  1204  can comprise one or more autonomic agents  1208 ,  1210 , and  1212  all performing assigned functions and roles. As noted earlier, roles can be a combination of ruler, messenger, and worker. Functions can be data gathering, communication functions, scheduling, controlling, security, and so forth. Upon detecting anonymous autonomic agent  1202  the assigned autonomous agent for performing security functions for autonomous system  1204  can interrogate the anonymous autonomic agent  1202 , requesting production of valid credentials. Detection can occur by employing various schemes such as when the anonymous autonomic agent  1202  requests resources from the system  1204  or from any autonomic entity that forms part of the system, response to polling signals from the autonomous system  1204 , or through a friend or foe signal that indicates the presence of an anonymous entity  1202  in proximity to the autonomous system  1204 . 
     To the autonomous system  1204 , security can be important because of compromises by the accidental misuse of hosts by agents, as well as the accidental or intentional misuse of agents by hosts and agents by other agents. The result can be damage, denial-of-service, breach-of-privacy, harassment, social engineering, event-triggered attacks, or compound attacks. To prevent security breaches, visiting agents can be verified to have valid and justified reasons for being there as well as providing security to the visiting agent with interaction with other agents and host. Upon detection the visiting agent  1202  can be sent an asynchronous ALice signal (Autonomic license)  1206  requiring valid credentials from the agent  1202 . The anonymous agent  1202  can need to work within the autonomic system  1204  to facilitate self-management, as such the anonymous agent  1202  and its host can need to be able to identify each other&#39;s credentials through such as an ALice signal. The autonomic system  1204  can establish certain response characteristics for the returned signal from the agent  1202 . For example, the autonomic system  1204  can require a response in an appropriate format, within a certain timeout period, and with a valid and justified reason for being within the locust of interest or domain of the autonomous system  1204 . For protection the autonomic system  1204  can make an assessment of the quality of the response from the anonymous agent  1202  to ascertain the potential of the agent for causing harm to the autonomous system  1204 . Based on this determination the autonomous system  1204  can control the type of interaction with the agent  1202 . The agent can be destroyed, blocked, partially blocked, stay alive signal withdrawn, or allowed to communicate with other agents within the autonomous system  1204 . The protection can be triggered at any level of infraction or by a combination of infractions by the anonymous autonomous agent  1202  when responding to the ALice signal. If the agent  1202  fails to identify itself appropriately following an Alice interrogation, the agent  1202  can be blocked from the system and given either a self-destruct signal, or its “stay alive” reprieve is withdrawn. A consequence of unacceptable response within a timeout period is that the anonymous agent  1202  can be identified as an intruder or other invalid agent (process) and consequently, the anonymous agent  1202  is destroyed and/or excluded from communicating with other agents  1208 ,  1210 ,  1212  in the system. As an alternative to the ALice signal, a quiese signal, command or instruction can be sent. The quiesce signal is discussed in more detail in conjunction with  FIGS. 10 ,  19  and  20 . 
       FIG. 13  is a hierarchical chart of an autonomous entity management system  1300  according to an embodiment. Properties that a system can possess in order to constitute an autonomic system are depicted in the autonomous entity management system  1300 . 
     General properties of an autonomic (self-managing) system can include four objectives defined by International Business Machines  1302 : self-configuring  1304 , self-healing  1306 , self-optimizing  1308  and self-protecting  1310 , and four attributes  1312 : self-awareness  1314 , environment-awareness  1316 , self-monitoring  1318  and self-adjusting  1320 . One skilled in the art will recognize that other properties also exist, such as self-quiescing  1324 . Essentially, the objectives  1302  could represent broad system requirements, while the attributes  1312  identify basic implementation mechanisms. 
     Self-configuring  1304  can represent an ability of the system  1300  to re-adjust itself automatically; this can simply be in support of changing circumstances, or to assist in self-healing  1306 , self-optimization  1308  or self-protection  1310 . Self-healing  1306 , in reactive mode, is a mechanism concerned with ensuring effective recovery when a fault occurs, identifying the fault, and then, where possible, repairing it. In proactive mode, the self-healing  1306  objective can monitor vital signs in an attempt to predict and avoid “health” problems (i.e., reaching undesirable situations). 
     Self-optimization  1308  can mean that the system  1300  is aware of ideal performance of the system  1300 , can measure current performance of the system  1300  against that ideal, and has defined policies for attempting improvements. The system  1300  can also react to policy changes within the system as indicated by the users. A self-protecting  1310  system  1300  can defend the system  1300  from accidental or malicious external attack, which necessitates awareness of potential threats and a way of handling those threats. 
     Self-managing objectives  1302  can require awareness of an internal state of the system  1300  (i.e., self-aware  1314 ) and current external operating conditions (i.e., environment-aware  1316 ). Changing circumstances can be detected through self-monitoring and adaptations are made accordingly (i.e., self-adjusting  1320 ). Thus, system  1300  can have knowledge of available resources, components, performance characteristics and current status of the system, and the status of inter-connections with other systems, along with rules and policies therein can be adjusted. Such ability to operate in a heterogeneous environment can require the use of open standards to enable global understanding and communication with other systems. 
     These mechanisms may not be independent entities. For instance, if an attack is successful, this can include self-healing actions, and a mix of self-configuration and self-optimisation, in the first instance to ensure dependability and continued operation of the system, and later to increase the self-protection against similar future attacks. Finally, these self-mechanisms could ensure there is minimal disruption to users, avoiding significant delays in processing. 
     Other self* properties have emerged or have been revisited in the context of autonomicity. We highlight some of these briefly here. Self-*  1322  can be self-managing properties, as follows. Self-anticipating is an ability to predict likely outcomes or simulate self-* actions. Self-assembling is an assembly of models, algorithms, agents, robots, etc.; self-assembly is often influenced by nature, such as nest construction in social insects. Self-assembly is also referred to as self-reconfigurable systems. Self-awareness is “know thyself” awareness of internal state; knowledge of past states and operating abilities. Self-chop is the initial four self-properties (Self-Configuration  1304 , Self-Healing  1306 , Self-Optimisation  1308  and Self-Protection  1310 ). Self-configuring is an ability to configure and re-configure in order to meet policies/goals. Self-critical is an ability to consider if policies are being met or goals are being achieved (alternatively, self-reflect). Self-defining is a reference to autonomic event messages between Autonomic Managers: contains data and definition of that data—metadata (for instance using XML). In reference to goals/policies: defining these (from self-reflection, etc.). Self-governing is autonomous: responsibility for achieving goals/tasks. Self-healing is reactive (self-repair of faults) and proactive (predicting and preventing faults). Self-installing is a specialized form of self-configuration—installing patches, new components, etc or re-installation of an operating system after a major crash. Self-managing is autonomous, along with responsibility for wider self-* management issues. Self-optimizing is optimization of tasks and nodes. Self-organized is organization of effort/nodes; particularly used in networks/communications. Self-protecting is an ability of a system to protect itself. Self-reflecting is an ability to consider if routine and reflex operations of self-* operations are as expected and can involve self-simulation to test scenarios. Self-similar is self-managing components created from similar components that adapt to a specific task, for instance a self-managing agent. Self-simulation is an ability to generate and test scenarios, without affecting the live system. Self-aware is self-managing software, firmware and hardware. 
       FIG. 14  is a block diagram of an autonomic element  1400  according to an embodiment. Autonomic element  1400  can include an element  1402  that is operably coupled to sensors and  1404  and effectors  1406 . 
     Autonomic element  1400  can also include components that monitor  1408 , execute  1410 , analyze  1412  and plan  1414 ; those components can access knowledge  1416 . Those components can interact with sensors  1418  and effectors  1420 . 
       FIG. 15  is a block diagram of autonomy and autonomicity  1500  at a high system level, according to an embodiment. A high level perspective for an intelligent machine design is depicted in  FIG. 15 . This diagram of autonomy and autonomicity  1500  includes intelligent machine design and system level autonomy and autonomicity. 
       FIG. 15  describes three levels for the design of intelligent systems: 
     1) Reaction  1502 —the lowest level, where no learning occurs but there is immediate response to state information coming from sensory systems  1504 . 
     2) Routine  1506 —middle level, where largely routine evaluation and planning behaviors take place. Input is received from sensory system  1504  as well as from the reaction level and reflection level. This level of assessment results in three dimensions of affect and emotion values: positive affect, negative affect, and (energetic) arousal. 
     3) Reflection  1508 —top level, receives no sensory  1504  input or has no motor  1510  output; input is received from below. Reflection is a meta-process, whereby the mind deliberates about itself. Essentially, operations at this level look at the system&#39;s representations of its experiences, its current behavior, its current environment, etc. 
     As illustrated, input from, and output to, the environment only takes place within the reaction  1502  and routine  1506  layers. One can consider that reaction  1502  level essentially sits within the “hard” engineering domain, monitoring the current state of both the machine and its environment, with rapid reaction to changing circumstances; and, that the reflection  1502  level can reside within an artificial domain utilizing its techniques to consider the behavior of the system and learn new strategies. The routine  1506  level can be a cooperative mixture of both. The high-level intelligent machine design can be appropriate for autonomic systems as depicted here in  FIG. 15 , in consideration of the dynamics of responses including reaction  1502  and also for reflection  1508  of self-managing behavior. 
     As depicted autonomic computing can reside within the domain of the reaction  1502  layer as a result of a metaphoric link with the autonomic biological nervous system, where no conscious or cognitive activity takes place. Other biologically-inspired computing (also referred to as nature-inspired computing, organic computing, etc.) can provide such higher level cognitive approaches for instance as in swarm intelligence. Within the autonomic computing research community, autonomicity cannot normally be considered to imply this narrower view. Essentially, the autonomic self-managing metaphor can be considered to aim for a user/manager to be able to set high-level policies, while the system achieves the goals. Similar overarching views exist in other related initiatives and, increasingly, they are influencing each other. 
     In terms of autonomy and autonomicity, autonomy can be considered as being self-governing while autonomicity can be considered being self-managing. At the element level, an element can have some autonomy and autonomic properties, since to self-manage implies some autonomy, while to provide a dependable autonomous element requires such autonomic properties as self-healing along with the elements self-directed task. From this perspective, separation of autonomy and autonomicity as characteristics will decrease in the future and eventually will become negligible. On the other hand, at the system level if one considers again the three tiers of the intelligent machine design (reaction  1502 , routine  1506 , and reflection  1508 ) and accepts the narrower view of autonomicity, there is a potential correlation between the levels. That is, the reaction  1502  level correlates with autonomicity, and the reflection  1508  level correlates with autonomy; autonomy as in self-governing of the self-managing policies within the system. 
       FIG. 16  is a block diagram of an architecture of an autonomic element (AE)  1600  according to an embodiment that includes reflection and reflex layers. The autonomic element  1600  can include a managed component (MC)  1602  that is managed, and the autonomic element  1600  can further include an autonomic manager (AM), not shown. The AM can be responsible for the MC  1602  within the AE  1600 . The AM can be designed as part of the component or provided externally to the component, as an agent, for instance. Interaction of the autonomic element  1600  can occur with remote (external) autonomic managers (cf. the autonomic communications channel  1606 ) through virtual, peer-to-peer, client-server or grid configurations. 
     An important aspect of the architecture of many autonomic systems can be sensors and effectors, such as shown in  FIG. 14 . A control loop  1608  can be created by monitoring  1610  behavior through sensors, comparing this with expectations (knowledge  1416 , as in historical and current data, rules and beliefs), planning  1612  what action is necessary (if any), and then executing that action through effectors. The closed loop of feedback control  1608  can provide a basic backbone structure for each system component.  FIG. 16  describes at least two control loops in the autonomic element  1600 , one for self-awareness  1614  and another control loop  1608  for environmental awareness. 
     In some embodiments, the self-monitor/self-adjuster control loop  1614  can be substantially similar to the monitor, analyze, plan and execute (MAPE) control loop described in  FIG. 14 . The monitor-and-analyze parts of the structure can perform a function of processing information from the sensors to provide both self-awareness  1614  and an awareness  1608  of the external environment. The plan-and-execute parts can decide on the necessary self-management behavior that will be executed through the effectors. The MAPE components can use the correlations, rules, beliefs, expectations, histories, and other information known to the autonomic element, or available to the autonomic element through the knowledge repository  1416  within the AM  1604 . 
     A reflection component  1616  can perform analysis computation on the AE  1600  (cf. the reflection component  1616  within the autonomic manager). In terms of an autonomic system, reflection can be particularly helpful in order to allow the system to consider the self-managing policies, and to ensure that the policies are being performed as expected. This can be important since autonomicity involves self-adaptation to the changing circumstances in the environment. An autonomic manager communications (AM/AM) component  1618  can also produce a reflex signal  1620 . A self adjuster  1622  can be operably coupled to a self-monitor  1624  in the self control loop  1614 . 
     Method Embodiments 
     In the previous section, apparatus embodiments are described. In this section, the particular methods of such embodiments are described by reference to a series of flowcharts. Describing the methods by reference to a flowchart enables one skilled in the art to develop such programs, firmware, or hardware, including such instructions to carry out the methods on suitable computers, executing the instructions from computer-readable media. Similarly, the methods performed by the server computer programs, firmware, or hardware can also be composed of computer-executable instructions. In some embodiments, methods  1700 - 2600  can be performed by a program executing on, or performed by firmware or hardware that is a part of a computer, such as computer  802  in  FIG. 8 . 
       FIG. 17  is a flowchart of a method  1700  to construct an environment to satisfy increasingly demanding external requirements according to an embodiment where a ruler entity decides to withdraw or generate a stay alive signal. Method  1700  manages autonomous entities that can be functionally extracted from an environment upon the occurrence of a predetermined condition. 
     Method  1700  can begin with action  1702  when receiving a signal from a managed entity. Action  1702  can receive a heart beat monitor (HBM) signal and pulse monitor (PBM) signal from a managed entity such as worker entities  1118  or  1120 . The I-IBM signal can be an indication that the managed entity (worker entity) is operating. The HBM can be an “ON/OFF” state signal, an indication that a process is being performed, or any other signal that can convey information that the worker entity is alive or active. The PBM signal can extend the HBM signal to incorporate reflex/urgency/health indicators from the autonomic manager representing its view of the current self-management state. The PBM signal can thus convey the performance and characteristics of the entity in the form of engineering data summarization to add context to the received HBM signal. Engineering data summarization can be a set of abstractions regarding sensor that can comprise rise and fall of data by a certain amount, external causes for parameter deviations, actual numerical value of the parameters being summarized, warning conditions, alarm conditions, and any other summarization that would convey the general health of the system. Once the HBM and PBM signals have been received, control can be forwarded to action  1704  for further processing. 
     In action  1704 , an analysis of the HBM and PBM signal can be performed to determine trends and possible areas of concern. Some purposes of the analysis can be to determine if a predetermined condition is exceeded, to make projection through simulation and data modeling areas of parameters that can lead to the failure of the worker entity or that might jeopardize the assigned mission, and ascertain the quality of performance of the system. The analysis can be performed by using regression techniques, neural network techniques, statistical techniques, or any other technique that can convey information about the state of a system or emergent behavior of the system. Once the analysis has been performed, control can pass to action  1706  for further processing. 
     In action  1706 , an alarmed condition can be determined. In action  1706 , the analysis of action  1704  can be referenced to determine if there is one or more alarm condition that can trigger the withdrawal of a stay alive signal. If no alarm conditions are determined, control can be passed to action  1708  to generate a stay alive signal. In the event that an alarm condition is present, control can be passed to action  1710  for further processing. 
     In action  1710 , a determination can be performed to ascertain whether the identified alarmed condition of action  1706  is recoverable by the managed entity, such as worker entities  1118  and  1120  of  FIG. 11 . When an alarmed condition is determined to be recoverable, control can be passed to action  1708  to generate a stay alive signal. When an alarmed condition is determined not to be recoverable, control can be passed to action  1712  to withdraw the stay alive signal. Method  1800  below can be one embodiment of determining  1710  if the identified alarmed condition is recoverable. 
       FIG. 18  is a flowchart of a method  1800  for ascertaining the recoverability of an alarmed condition determined at action  1706  according to an embodiment. Method  1800  manages autonomous entities that can be functionally extracted from an environment upon the occurrence of a predetermined condition. Method  1800  is one possible embodiment of the action in  FIG. 17  above of determining  1710  if the identified alarmed condition is recoverable. 
     Method  1800  can begin with action  1802  when receiving one or more alarmed conditions. In action  1802 , a determination is performed of whether or not an incorrect operation from the managed system has been identified in action  1704  of  FIG. 17 . An incorrect operation can range from not initializing sensors to failing to self-heal when internal decision logic recommends as an appropriate cause of action. In action  1802  in addition to determining if an incorrect operation has been identified, the number of devices or processes within the entity that registered an incorrect operation can be ascertained. If at least one incorrect operation is determined, the action can transfer the identity of the unit to evaluation block  1808  for further processing. 
     In action  1804 , a determination is performed of whether or not emergent behavior from the managed system has been identified in action  1704  of  FIG. 17 . An emergent behavior or emergent property can appear when a number of entities (agents) operate in an environment forming behaviors that are more complex as a collective. The property itself can often be unpredictable and unprecedented and can represent a new level of the system&#39;s evolution. This complex behavior in the context of control system can be known as non-linearity, chaos, or capacity limits. The complex behavior or properties cannot be properties of any single such entity, nor can they easily be predicted or deduced from behavior in the lower-level entities. One reason why emergent behavior occurs can be that the number of interactions between autonomic components of a system increases combinatorially with the number of autonomic components, thus potentially allowing for many new and subtle types of behavior to emerge. Nothing can directly command the system to form a pattern, but the interactions of each part (entities) to its immediate surroundings can cause a complex process that leads to order. Emergent behavior can be identified based on parameters that give rise to the complex behavior in a system such as demands on resources. Once an emergent behavior condition has been identified, the information can be forwarded to evaluation block  1808  for further processing. 
     In action  1806 , a determination can be performed of alarm conditions that can have an impact on the success of the mission or task by which all entities are striving to accomplish. The impact could be the ability to accomplish individual tasks or the potential for failure of the overall mission by permitting an entity to stay alive. This impact can be determined through Bayesian belief networks, statistical inference engines, or by any other presently developed or future developed inference engine that can ascertain the impact on a particular task if one or more agent is showing incorrect operation or harmful emergent behavior. Once the impact has been determined the information can be passed to evaluation block  1808  for further processing. 
     Evaluation block  1808  can marshal the incorrect operation identified in action  1802 , the emergent behavior in action  1804 , or the effect on mission in action  1806  to suggest a course of action that the managed entities should adopt, which in the present arrangement is based on a stay alive signal. The determination of withdrawing or affirming the stay alive signal can be based on the occurrence of one or more of the identified alarmed conditions, or a combination of two or more of the identified alarmed conditions. For example, the stay alive signal could be withdrawn if there is emergent behavior and there would be an effect on the mission. In the alternative, the stay alive signal could be affirmed if there was only emergent behavior, or incorrect operation. Once the evaluation is determined, control can be passed to decision block  1810  for further processing in accordance to the decision made in evaluation block  1808 . 
     In action  1810 , if the desired control instruction is to maintain the stay alive signal, control can be passed to action  1708  for further processing. In the alternative, a withdrawal of the stay alive signal can be sent to action  1712  for further processing. Generating a stay alive signal can be equivalent to generating a stay alive signal, affirming a stay alive signal, not withdrawing a stay alive signal, or any other condition that can determine if an entity is to perish or to extinguish unless allowed to continue by another entity. The other entity might be a managing entity since the other entity can determine the outcome (life or death) of an entity. 
       FIG. 19  is a flowchart of a method  1900  to construct an environment to satisfy increasingly demanding external requirements according to an embodiment where a ruler entity decides to withdraw or generate a stay-awake signal. Method  1900  reduces the possibility that an autonomic element will jeopardize the mission of the autonomic element. 
     Method  1900  can begin with action  1702  when receiving a signal from a managed entity. Action  1702  can receive a heart beat monitor (HBM) signal and pulse monitor (PBM) signal from a managed entity such as worker entities  1118  or  1120 . In some embodiments, the HBM signal is an indication that the managed entity (worker entity) is operating. The HBM can be an “ON/OFF” state signal, an indication that a process is being performed, or any other signal that can convey information that the worker entity is awake or active. The PBM signal can extend the HBM signal to incorporate reflex/urgency/health indicators from the autonomic manager representing its view of the current self-management state. The PBM signal can thus convey the performance and characteristics of the entity in the form of engineering data summarization to add context to the received HBM signal. Engineering data summarization could be a set of abstractions regarding sensors that, in some embodiments, could comprise rise and fall of data by a certain amount, external causes for parameter deviations, actual numerical value of the parameters being summarized, warning conditions, alarm conditions, and any other summarization that would convey the general health of the system. Once the HBM and PBM signals have been received, control can be forwarded to action  1704  for further processing. 
     In action  1904 , an analysis of the HBM and PBM signal can be performed to determine trends and possible areas of concern. The purpose of the analysis can be to determine that a predetermined condition has been exceeded, generate a projection through simulation and data modeling areas of parameters that can lead to the failure of the worker entity or that might jeopardize the assigned mission, and ascertain the quality of performance of the system. The analysis can be performed by using regression techniques, neural network techniques, statistical techniques, or any other technique that can convey information about the state of a system or emergent behavior of the system. Once the analysis has been performed, control can be passed to action  1706  for further processing. 
     In action  1706 , an alarmed condition can be determined. In action  1706 , the analysis of action  1704  can be referenced to determine if there is one or more alarm condition that can trigger the withdrawal of a stay-awake signal. If no alarm conditions are determined, control can be passed to action  1902  to generate a stay-alive signal. In the event that an alarm condition is present, control can be passed to action  1904  for further processing. 
     In action  1904 , a determination can be performed to ascertain if the identified alarmed condition of action  1706  is recoverable by the managed entity such as worker entities  1118  and  1120  of  FIG. 11 . When an alarmed condition is determined not to be recoverable, control can be passed to action  1712  to withdraw the stay-alive signal. Method  2000  below could be one embodiment of determining  1904  if the identified alarmed condition is recoverable. When an alarmed condition is determined to be recoverable, control can be passed to action  1908  in which a determination can be performed to ascertain if quiescing the managed entity and/or subsequent recovery is possible. When quiescence of the managed entity and/or need for later recovery is determined as not possible, control can pass to action  1902  to generate a stay-awake/stay-alive-signal. When quiescence of the managed entity is determined as possible and/or needed in action  1908 , control can pass to action  1910 , to withdraw the stay-awake signal. Thus, quiescing the managed entity functionally extracts the managed entity from an environment upon the occurrence of an alarmed condition. Quiescence can be a less encompassing alternative to withdrawing the stay-awake signal of apoptosis. Method  1900  can allow an agent or craft that is in danger or endangering the mission to be put into a self-sleep mode, then later reactivated or self-destructed. 
       FIG. 20  is a flowchart of a method  2000  for ascertaining the recoverability of an alarmed condition determined at action  1904 . Method  2000  manages autonomous entities that can be functionally extracted from an environment upon the occurrence of a predetermined condition. 
     Method  2000  can begin with action  1802  when receiving one or more alarmed conditions. In action  1802 , a determination is performed as to whether or not an incorrect operation from the managed system has been identified in action  1704  of  FIG. 17 . An incorrect operation can range from not initializing sensors to failing to self-heal when internal decision logic recommends as an appropriate cause of action. In action  1802 , in addition to determining if an incorrect operation has been identified, the number of devices or processes within the entity that registered an incorrect operation can be ascertained. If at least one incorrect operation is determined, the action can transfer the identity of the unit to evaluation block  1808  for further processing. 
     In action  1804 , there can be a determination of emergent behavior from the managed system that has been identified in action  1704  of  FIG. 17 . An emergent behavior or emergent property can appear when a number of entities (agents) operate in an environment forming behaviors that are more complex as a collective. The property itself can often be unpredictable and unprecedented and can represent a new level of the system&#39;s evolution. This complex behavior in the context of control system can be known as non-linearity, chaos, or capacity limits. The complex behavior or properties cannot be properties of any single such entity, nor can they easily be predicted or deduced from behavior in the lower-level entities. One reason why emergent behavior occurs could be that the number of interactions between autonomic components of a system increases combinatorially with the number of autonomic components, thus potentially allowing for many new and subtle types of behavior to emerge. Nothing can directly command the system to form a pattern, but instead the interactions of each part (entities) to its immediate surroundings can cause a complex process that leads to order. Emergent behavior can be identified based on parameters that give rise to the complex behavior in a system such as demands on resources. Once an emergent behavior condition has been identified, the information can be forwarded to evaluation block  1808  for further processing. 
     In action  1806 , a determination can be performed of alarm conditions that can have an impact on the success of the mission or task which all entities are striving to accomplish. The impact could be the ability to accomplish individual tasks or the potential for failure of the overall mission by permitting an entity to stay awake. This impact can be determined through Bayesian belief networks, statistical inference engines, or by any other presently developed or future developed inference engine that can ascertain the impact on a particular task if one or more agent is showing incorrect operation or harmful emergent behavior, Once the impact has been determined, the information can be passed to evaluation block  1808  for further processing. 
     Evaluation block  1808  can marshal the incorrect operation identified in action  1802 , the emergent behavior in action  1804 , and the effect on mission in action  1806  to suggest a course of action that the managed entities should adopt, which in the present arrangement is based on a stay-awake signal. The determination of withdrawing or affirming the stay-awake signal can be based on the occurrence of one or more of the identified alarmed conditions, or a combination of two or more of the identified alarmed conditions. For example, the stay-awake signal could be withdrawn if there is emergent behavior and there would be an effect on the mission. In the alternative, the stay-awake signal could be affirmed if there was only emergent behavior, or incorrect operation. Once the evaluation is determined, control can pass to decision block  2002  for further processing in accordance with the decision made in evaluation block  1808 . 
     In action  2002 , if the desired control instruction is to maintain the stay-awake signal, control can be passed to action  1902  for further processing. In the alternative, a withdrawal of the stay-awake signal can be sent to action  1910  for further processing. Generating a stay-awake signal is equivalent to affirming a stay awake signal, not withdrawing a stay awake signal, or any other condition that can determine if an entity is to perish or to extinguish unless allowed to continue by another entity. The other entity could be a managing entity since the other entity can determine the outcome (life or death) of an entity. 
       FIG. 21  is a flowchart of a method  2100  for ascertaining the recoverability of an alarmed condition determined at action  1904 . Method  2100  manages autonomous entities that can be functionally extracted from an environment upon the occurrence of a predetermined condition. 
     Method  2100  can begin with action  2102  after having received one or more alarmed conditions. In action  2102 , a determination is performed as to whether or not an invalid communication from the managed system has been identified in action  1704  of  FIG. 17 . In action  2102 , in addition to determining if an invalid communication has been identified, the number of devices or processes within the entity that registered an invalid communication can be ascertained. If at least one invalid communication is determined, the identity of the unit can be transferred to evaluation block  1808  for further processing. An invalid communication is a communication handshake that doesn&#39;t match an expected protocol, such as the “rogue” agent didn&#39;t respond in the expected manner, or in the expected time limits, or failed to send a signal in the correct format. 
     In action  2104 , a determination is performed as to whether or not a rogue agent from the managed system that has been identified in action  1704  of  FIG. 17 . A rogue agent can exist when a number of entities (agents) operate in an environment forming behaviors that are more complex as a collective. One cause of a rogue agent could be that the number of interactions between autonomic components of a system increases combinatorially with the number of autonomic components, thus potentially allowing for many new and subtle types of counterproductive behavior to emerge. Nothing can directly command the system to form a pattern, but instead the interactions of each part (entities) to its immediate surroundings can cause a complex process that leads to order. A rogue agent can be identified based on parameters that give rise to the complex behavior in a system such as demands on resources. Once a rogue agent has been identified, the information can be forwarded to evaluation block  1808  for further processing. 
     In action  2106 , a determination can be performed of safety/security issue/concerns that can have an impact on the success of the mission or task which all entities are configured to accomplish. The impact could be the ability to accomplish individual tasks or the potential for failure of the overall mission by permitting an entity to stay awake. This impact can be determined through Bayesian belief networks, statistical inference engines, or by any other presently developed or future developed inference engine that can ascertain the impact on a particular task if one or more agent is showing invalid communication or harmful rogue agent. Once the safety/security issue/concern has been determined, the information can be passed to evaluation block  1808  for further processing. 
     Evaluation block  1808  can marshal the invalid communication identified in action  2102 , the rogue agent in action  2104 , and the safety/security issue/concern in action  2106  to suggest a course of action that the managed entities should adopt, which in the present arrangement is based on a stay-awake signal. The determination of withdrawing or affirming the stay-awake signal can be based on the occurrence of one or more of the identified alarmed conditions, or a combination of two or more of the identified alarmed conditions. For example, the stay-awake signal could be withdrawn if there is rogue agent and there would be a safety/security issue/concern of the mission. In the alternative, the stay-awake signal could be affirmed if there was only rogue agent, or invalid communication. Once the evaluation is determined, control can pass to decision block  2002  for further processing in accordance with the decision made in evaluation block  1808 . 
     In action  2108 , if the desired control instruction is not to transmit an otoacoustic signal, control can be passed to action  1902  for further processing. In the alternative, an otoacoustic signal can be sent in action  1910  for further processing. The self managing autonomous system can self-protect from spurious signals or signals generated by a rogue agent that has failed to engage in a satisfactory ALice exchange by generating an otoacoustic An otoacoustic signal is a counteracting signal to the spurious signals or signals generated by a rogue agent that is intended to stop the self managing autonomous system from receiving, or at least from reacting to, these unwanted signals, effectively having an overriding effect or an equalizing effect on any reflex signal received by the self managing autonomous system. In essence, countersignals can be generated that will render the undesirable signals harmless to the self managing autonomous system. The security and protection of the self managing autonomous system may be improved by the use of the otoacoustic signal. The otoacoustic signal can help ensure that self-managing complex systems operate correctly without human intervention where management by humans is simply not realistic or even feasible. 
     Generating an otoacoustic signal can be equivalent to affirming an otoacoustic signal, not withdrawing an otoacoustic signal, or any other condition that can determine if an entity is to counteract a spurious signal or signal from a rogue agent. The other entity could be a managing entity since the other entity can determine the outcome (life or death) of entity. 
     The present invention may draw inspiration from or have some similarities to the mammalian acoustic or stapedius reflex, although one skilled in the art will recognize that when in danger of exposure to extreme sounds that may damage the ear drum, the mammalian body protects itself. The acoustic reflex, or stapedius reflex, is an involuntary muscle contraction in the middle ear of mammals in response to high-intensity sound stimuli. The mammalian otoacoustic mechanism, called otoacoustic emission, involves the generation of sound from within the inner ear in response to over-activity of the cochlear amplifier. That is, when the body is presented with a sound that is potentially damaging, the inner ear generates a counter-sound, which is benign, and protects the inner ear from hearing it. 
     In some embodiments, all of the agents, components and apparatus of  FIG. 1-6  or  11 - 16  can detect and/or issue the otoacoustic signal, as long as the agents, components and apparatus are “friendly” (i.e., known not to be rogue) agents. In some embodiments, however, only a coordinating agent, such as ruler NBF  608 , can perform method  2100 . 
       FIG. 22  is a flowchart of a method  2200  for providing security requirements according to an embodiment where a ruler entity decides to withdraw or generate a stay alive signal from an anonymous agent. Method  2200  manages autonomous entities that can be functionally extracted from an environment upon the occurrence of a predetermined condition. Method  2200  can begin with action  2202  where an ALice signal is sent to an anonymous agent to ascertain the potential for harm of the agent to a system as shown in  FIG. 22 . After the ALice signal has been sent to the agent, control can be passed to action  2204  for further processing. 
     In action  2204  the response from the agent can be monitored. Monitored as used herein refers to maintaining regular surveillance, or close observation, over an anonymous agent and can include the absence of a signal. For example, not responding with a timeout period is considered, as used herein, as monitor response. After action  2204  is completed, control can be passed to action  2206  for further processing. 
     In action  2206 , the monitored response from action  2204  can be analyzed to determine if the monitored response is in an appropriate format, within a certain timeout period, and with a valid and justified reason for being within the locust of interest or domain of the autonomous system  2204  as shown in  FIG. 22 . Once the potential for causing harm has been ascertained, control can be passed to action  2208  for further processing. 
     In action  2208 , the system can control the future of the anonymous agent based on the potential for harm to the autonomous system. This mimics the mechanism of cell death in the human (and animal) body, and hence makes use of autonomic and other biologically inspired metaphors. The technique would send self-destruct signals to agents that can be compromised, or which cannot be identified as friendly or as having a right to access certain resources. The concept of the ALice signal is to challenge a mobile agent to determine if the mobile agent is friendly and to determine if the mobile agent has permission to access certain resources. If the mobile agent fails to identify itself appropriately following an ALice interrogation, the mobile agent can be blocked from the system and given either a self-destruct signal, or its stay alive reprieve is withdrawn. As an alternative to the ALice signal, a quiesce signal, command or instruction can be sent. The quiesce signal is discussed in more detail in conjunction with  FIGS. 10 ,  19  and  20 . 
       FIG. 23  is a flowchart of a method  2300  of autonomic communication by an autonomic element. Method  2300  can offer a holistic vision for the development and evolution of computer-based systems that brings new levels of automation and dependability to systems, while simultaneously hiding their complexity and reducing their total cost of ownership. 
     Method  2300  can include transmitting self health/urgency data  2302 . Examples of the self health/urgency data can include information describing low battery power and/or failed sensors. Method  2200  can also include transmitting  2304  environment health/urgency data. Examples of the environment health/urgency data can include information describing inaccessible devices, unauthorized access, and/or an unidentified mobile agent sending communication signals. 
     Transmitting  2302  and  2304  can be performed in any order relative to each other. For example, in one embodiment the transmitting  2302  self health/urgency data can be performed before transmitting  2304  environment health/urgency data. In another embodiment, transmitting  2304  environment health/urgency data can be performed before transmitting  2302  self health/urgency data. In yet another embodiment, the self health/urgency data can be transmitted simultaneously with the environment health/urgency data. For example, the environment health/urgency data and the self health/urgency data can be transmitted together. One example of transmitting the environment health/urgency data and the self health/urgency data can include encapsulating the environment health/urgency data and the self health/urgency data in a X.25 packet, although one skilled in the art will readily recognize that any number of alternative packet types can be used that fall within the scope of this invention. The environment health/urgency data and the self health/urgency data can be thought of together as the “lub-dub” of a heartbeat in which the two “beats” or two pieces of data are transmitted simultaneously. The X.25 standard is published by the ITU Telecommunication Standardization Sector at Place des Nations, CH-1211 Geneva 20, Switzerland. 
     An autonomic environment can require that autonomic elements and, in particular, autonomic managers communicate with one another concerning self-* activities, in order to ensure the robustness of the environment. A reflex signal  1620  of  FIG. 16  above can be facilitated through the pulse monitor (PBM). A PBM can be an extension of the embedded system&#39;s heart-beat monitor, or HBM, which safeguards vital processes through the emission of a regular “I am alive” signal to another process with the capability to encode self health/urgency data and environment health/urgency data as a single pulse. HBM is described in greater detail in  FIGS. 14 and 21  above. Together with the standard event messages on an autonomic communications channel, this can provide dynamics within autonomic responses and multiple loops of control, such as reflex reactions among the autonomic managers. Some embodiments of the autonomic manager communications (AM/AM) component  1618  can produce a reflex signal  1620  that includes the self health/urgency data and the environment health/urgency data in addition to the HBM. More concisely, the reflex signal can carry a PBM. A reflex signal that carries a PBM can be used to safe-guard the autonomic element by communicating health of the autonomic element to another autonomic unit. For instance, in the situation where each PC in a LAN is equipped with an autonomic manager, rather than each of the individual PCs monitoring the same environment, a few PCs (likely the least busy machines) can take on this role and alert the others through a change in pulse to indicate changing circumstances. 
     An important aspect concerning the reflex reaction and the pulse monitor is the minimization of data sent—essentially only a “signal” is transmitted. Strictly speaking, this is not mandatory; more information can be sent, yet the additional information should not compromise the reflex reaction. 
     Just as the beat of the heart has a double beat (lub-dub), the autonomic element&#39;s pulse monitor can have a double beat encoded—as described above, a self health/urgency measure and an environment health/urgency measure. These match directly with the two control loops within the AE, and the self-awareness and environment awareness properties. 
       FIG. 24  is a flowchart of a method  2400  of autonomic communication by an autonomic element. Method  2400  can include transmitting  2402  event message data in addition to the self and environment health/urgency data. Event message data can include data describing a change in condition, or a deviation from a normal operation. Event message data is described in more detail above in  FIG. 11 . 
     In some embodiments, the self health/urgency data and environment health/urgency data encoded with the standard event messages on an autonomic communications channel, can provide dynamics within autonomic responses and multiple loops of control, such as reflex reactions among an autonomic manager. 
       FIG. 25  is a flowchart of a method  2500  of autonomic communication by an autonomic element. Method  2500  can include receiving  2502  the self health/urgency data from a self control loop component of the autonomic element. One example of the self control loop component of the autonomic element can be the self awareness control loop  1614  of the autonomic element  1600  of  FIG. 16  above. 
     Method  2500  can also include receiving  2504  the environment health/urgency data from an environment control loop component of the autonomic element. One example of the environment control loop component of the autonomic element can be the environment awareness control loop  1608  of the autonomic element  1600  of  FIG. 16  above. 
       FIG. 26  is a flowchart of a method  2600  of autonomic communication by an autonomic element. Method  2600  can offer a holistic vision for the development and evolution of computer-based systems that brings new levels of automation and dependability to systems, while simultaneously hiding their complexity and reducing processing delays by systems that receive data from the autonomic element. 
     Method  2600  can include transmitting uncompressed self health/urgency data  2602 . Method  2600  can also include transmitting  2604  uncompressed environment health/urgency data. In the absence of bandwidth concerns, the uncompressed data can be acted upon quickly and not incur processing delays. One important aspect can be that the data, whether uncompressed or sent in some other form, should be in a form that can be acted upon immediately and not involve processing delays (such as is the case of event correlation). Transmitting  2602  and  2604  can be performed in any order relative to each other. 
     An otoacoustic component of an autonomic nit can render an incoming potentially harmful signal inert. Self-managing systems, whether viewed from the autonomic computing perspective, or from the perspective of another initiative, can offer a self-defense capability that brings new levels of automation and dependability to systems, while simultaneously hiding their complexity and reducing their total cost of ownership. 
     According to various embodiments, a number of initiatives inspired by concepts from biology have arisen for self-management of a complex system. Biological systems, and in particular, the Autonomic Nervous System (ANS), are capable of performing autonomic, innate or in-built, self-regulation activities requiring no conscious thought. In similar fashion, and according to various embodiments of the present teachings, a software system is provided that manages itself. In some embodiments, the system takes advantage of emergent behavior similar to that in social insect colonies. It has been found that emergent behavior helps an insect colony to collectively solve complex problems without centralized control. Thus, colony (or swarm) behavior appears out of local interactions between individuals with simple rule sets and no global knowledge. In fact, emergent behavior does not “help” insect colonies in the general sense of the word, but instead it is the coordinated behavior of the social insects that collectively solves problems. What is emergent in this example is coherence and cooperation from a global point of view, where at the level of the individual nothing actively pushes for it. 
     According to various embodiments, the self-sacrifice behavior of one or more individual components can be absorbed in se the usefulness or lives of other individual components. Thus, an emergent behavior is provided wherein the individual components sacrifice themselves to jointly solve a complex problem vital to the entire system or swarm. In some embodiments an emergent behavior can be provided by the system whereby one or more components of the system self-sacrifices itself for the greater good of the system. In an example, each component can be programmed to identify at least one condition that would cause the respective component to be detrimental to the greater good of the system. The component could also be programmed to self-sacrifice itself if the condition is determined to exist, thereby benefiting the entire system. As an illustration, such a time-to-self-sacrifice condition can comprise exceeding a risk threshold, for example, a threshold determined by a risk analysis program that is run periodically during operation of the system component. For example, if the continued operation of the component poses a risk of collision with another component of the system, and the risk exceeds a risk threshold, a self-sacrifice operation can be initiated for the greater good of the entire system. Self-sacrifice can comprise, for example, shutting down, self-destruction, or the like. Self-destruction can be, for example, by explosion, by implosion, or be steering into an asteroid, planet, or sun, in the case of a multiple spacecraft system. 
     According to various embodiments of the present teachings, an autonomic nano technology swarm (ANTS) system is provided. With the ANTS system, a swarm of small autonomous exploration vehicles, such as spacecraft, can be used for an exploration mission, thus reducing the costs and risks involved when only a single, larger spacecraft is used. The system further enables exploration missions where a single, large, spacecraft would be impractical, and can offer greater redundancy and increased mission longevity in harsh environments. The ANTS system can exhibit all of the features of a multi-agent autonomic system (AS) wherein the spacecraft, vehicles, or system components themselves are autonomic elements (AEs). 
     According to various embodiments, the software architecture is adaptive in all its attributes and functionality, including its performance, security, fault tolerance, configurability, and the like. Moreover, the system can make decisions to cope with new environmental conditions encountered, and can learn and evolve to become better adapted to what it is supposed to do. Thus, a spacecraft unit can be programmed such that its own self-sacrifice can be used to protect other components or units vital to the system, or programmed to self-sacrifice if the result would be a significant performance gain for the entire system or mission. 
     In some embodiments, the emergent behavior is a complex behavior derived spontaneously from simple rules. Thus, the emergent behavior can enable the production of a high-level, more complex behavior through the interaction of multiple system components, by following simple rules. 
     The self-sacrifice behavior described herein differs from the other approaches mentioned above in at least the following ways. Firstly, in some embodiments, “death” is not always assumed for the individual component. Secondly, in some embodiments, self-sacrifice can comprise making a choice of leaving a critical task to another individual component when the individual&#39;s own performance is not optimal. These and other advantages are apparent from the present teachings. 
     According to various embodiments, an autonomic element in a system comprising numerous autonomic elements can exhibit or be programmed to exhibit self-adapting behavior to improve performance and/or to protect vital parts of the system. According to some embodiments, an autonomic element can comprise a spacecraft in an autonomous space mission. According to some embodiments, the spacecraft can comprise an autonomous worker vehicle or component in an autonomous space mission which mission involves using a plurality of different spacecrafts. According to some embodiments, the autonomic space mission can be performed by an ANTS system as described above. According to some embodiments, each spacecraft in the ANTS system can have a specialized mission. According to some embodiments, individual components, such as spacecraft or units in the ANTS system, can be programmed to exhibit emergent self-adapting behavior. An individual spacecraft unit of the system can perform self-sacrifice as part of its self-adapting behavior, in order to improve the system performance and/or to protect vital parts of the system, for example, based on goals of an exploration mission. 
     According to various embodiments, the system uses Autonomic System Specification Language (ASSL). The ASSL can be used to model the self-sacrifice behavior of the individual spacecraft units. The ASSL can follow simple predefined rules, but can help in the formation of an emergent complex system-level behavior that strives to protect and optimize the system as a whole. It should be understood that by self-sacrifice, “death” is not the only option under consideration for the spacecraft, but rather, in some embodiments another option for self-sacrifice can comprise a voluntary relinquishment from the “social status” of the spacecraft in the swarm. According to some embodiments, the voluntary relinquishment from the “social status” can be achieved by delegating rights from one component to another, for example, from one spacecraft to another spacecraft of the system. 
     The systems, clients, servers, methods, computer-readable media, software, hardware, and operating environments that can be used include those described in U.S. Patent Applications Publications Nos. US 2007/0073631 A1, entitled “Systems, Methods and Apparatus for Quiescence of Autonomic Systems,” and US 2007/0260570 A1, entitled “Systems, Methods and Apparatus for Autonomic Safety Devices,” which are incorporated herein in their entireties, by reference. 
     ANTS Structure 
     According to various embodiments of the present teachings, an ANTS system is provided for use in a sub-mission Prospecting Asteroids Mission (PAM). The PAM can provide a novel approach to asteroid belt resource exploration. ANTS can provide extremely high autonomy, minimal communication requirements to Earth, and a set of very small explorers with few consumables. In some embodiments, the explorers forming the swarm can be pica-class, low-power, and low-weight spacecraft units, yet capable of operating as fully autonomous and adaptable agents. 
       FIG. 27  depicts a PAM sub-mission scenario of the ANTS concept mission, according to various embodiments of the present teachings. As depicted in  FIG. 27 , a transport spacecraft launched from Earth toward an asteroid belt can carry a laboratory that assembles tiny spacecraft. Once the transport spacecraft reaches a certain point in space where gravitational forces are balanced, termed a Lagrangian, and in this case the L1 Lagrangian point, the transport ship can release the assembled swarm, which can head toward the asteroid belt. Each spacecraft can be equipped with a solar sail and thereby can rely primarily on power from the sun, using, for example, tiny thrusters to navigate independently. 
     As  FIG. 271  shows, there can be at least three classes of spacecraft; coordinating autonomic components (rulers); messenger autonomic components (messengers); and autonomic worker components (workers). By grouping them in appropriate ways, the ANTS system can form teams that explore particular asteroids of the asteroid belt. Hence, the ANTS system can exhibit self-organization since there is no external force directing its behavior and no single spacecraft having a global view of the intended macroscopic behavior. According to some embodiments, the internal organization of the swarm can depend on the global task to be performed and on the current environmental conditions. According to some embodiments, the swarm can consist of several sub-swarms, which can be temporal groups organized to perform a particular task. According to some embodiments, each sub-swarm can have a coordinating group leader (ruler), one or more messengers, and a number of workers each carrying at least one specialized instrument. According to some embodiments, the messengers can connect or provide communications between the team members when such team members cannot connect directly to one another. 
     Self-Sacrifice Scenarios in ANTS 
     According to various embodiments, the system can implement self-optimization. In general, the global system optimization can be correlated to the optimization of the individual system elements. Each component of the system can prove its performance on-the-fly. For example, in some embodiments rulers can use experience gained to self-optimize. As an example, rulers can use their experience to improve their ability to identify asteroids. In some embodiments, messengers can strive to find the best position to improve communication among the other components or swarm units. According to some embodiments, workers can self-optimize through learning and experience. 
     Single components can “die” for the good of the entire system or otherwise self-sacrifice, for example, by voluntarily relinquishing their posts. In some embodiments, a spacecraft unit can, for example, voluntarily relinquish its post as an ANTS worker component by delegating tasks to other worker components. While scenarios related to self-sacrifice of ANTS workers are described in great detail herein, it is to be understood that other classes of spacecraft, for example, rulers and/or messengers, can also be configured to self-sacrifice voluntarily. 
     According to various embodiments, a worker can “die” or sacrifice itself voluntarily. For example, a worker can sacrifice itself voluntarily when the worker cannot continue performing its duties as a worker. As an illustration, a worker can self-sacrifice if it cannot continue to support the service-level objectives assigned to it (for example, if it cannot achieve performance). While operating in space, for example, an instrument of a worker can be damaged but not destroyed. For example, the instrument may still be operational, but its performance might be degraded or destroyed. According to some embodiments, a worker with a destroyed or heavily damaged instrument that performs below a performance minimum, can self-sacrifice voluntarily. 
     According to some embodiments, the self-sacrifice can comprise a transformation, for example, a transformation of a worker. If a worker cannot perform its duties anymore, due to a damage or instrument loss, the worker can, according to various embodiments, perform one or more operations. The operations can comprise, for example, asking the ruler to assign a new replacement worker, and/or striving to transform into another category of component useful to the swarm unit. Such transformation can comprise, for example, transforming from a worker to a messenger, from a messenger to a worker, from a worker to a ruler, or the like. According to some embodiments, a worker can try to transform to a ruler or a messenger, but if it is not possible for the worker to transform to a ruler or messenger, the worker can instead transform to a shield component such as a stand-by shield. According to some embodiments, such a shield component can sail nearby and strive to protect the replacement worker from different hazards. For example, a shield unit can, according to some embodiments, take the impact of an incoming small asteroid which is about to hit the replacement worker. The shield unit does not have to spend additional time and resources to recover from this probable impact. This kind of protection can comprise a complete self-sacrifice because the shield unit can serve as such until its full destruction, while increasing the overall performance of the system. 
     According to various embodiments, the self-sacrifice operation can comprise a self-destruction operation. According to some embodiments, when a worker is damaged so badly that it cannot move anymore, the worker can self-destruct, for example, by exploding. This can be used to avoid the risk of collision with another component of the system. Hence, there can be a real self-sacrifice that indirectly leads to higher system performance, due to the reduction in the risk of an impact. 
     According to various embodiments, the self-sacrifice behavior of a single component, such as a spacecraft unit in an ANTS system, can be modeled with Autonomic System Specification Language (ASSL). With ASSL, validation and code generation of specified instructions can be achieved. According to various embodiments, the ASSL can be defined through formalization tiers. According to some embodiments, over the formalization tiers, ASSL can provide a multi-tier specification model that is designed to be scalable and to expose a judicious selection and configuration of infrastructure elements and mechanisms needed by an AS. ASSL can define an AS with interaction protocols and AEs, where the ASSL tiers and their sub-tiers describe different aspects of the AS under consideration, like policies, communication interfaces, execution semantics, actions, and the like. 
     According to various embodiments, the ASSL tiers and their sub-tiers, as shown in  FIG. 28 , can be abstractions of different aspects of the autonomic system under consideration. According to some embodiments, the AS Tier can specify an AS in terms of service-level objectives (AS SLO), self-management policies, architecture topology, actions, events, and metrics. According to some embodiments, the AS SLO can be a high-level form of behavioral specification that establishes system objectives such as performance. According to some embodiments, the self-management policies of an AS can include: 1) self-configuring; 2) self-healing; 3) self-optimizing; and 4) self-protecting (also referred to herein as a self-CHOP of an AS). Other self-management policies can also or instead be included. According to some embodiments, the metrics can constitute a set of parameters and observables controllable by the AEs. 
     According to various embodiments, at the AS Interaction Protocol tier, the ASSL framework can specify an AS-level interaction protocol (ASIP). According to some embodiments, AS EP can be a public communication interface, expressed as communication channels, communication functions, and messages. 
     According to various embodiments, at the AE Tier, the ASSL formal model considers AEs to be analogous to software agents able to manage their own behavior and their relationships with other AEs. According to some embodiments, at the AE Tier, ASSL can describe the individual AEs. 
     According to various embodiments, a worker&#39;s self-sacrifice behavior can be modeled with ASSL. It should be understood that the model presented and described herein is exemplary only. 
     According to various embodiments, the self-sacrifice behavior can comprise a self-management policy, which can be specified at the individual component or spacecraft level (at the AE Tier).  FIG. 28  presents a partial specification of a self-sacrifice policy that can be used according to various embodiments based on the scenarios described herein.  FIG. 28  describes possible choices a worker can make when the worker is no longer minimally or fully operational. According to various embodiments, the definitions that follow can be used to specify exemplary the self-sacrifice policies. 
     Self-sacrifice can be defined as a self-management policy structure. A set of fluents and mappings can be used to specify this policy. With fluents, specific situations can be expressed, in which the policy is interested. With mappings, the situations can be mapped to actions. 
     Actions can be defined as a set of actions that can be undertaken by the worker in response to certain conditions, and according to that policy, as shown in  FIG. 29 . 
     Events can be defined as a set of events that initiate fluents and can optionally be prompted by actions according to that policy. 
     Metrics can be defined as a set of metrics needed by that policy. 
     According to various embodiments, the unableToExplore fluent, shown in  FIG. 28 , can take place when the worker is no longer operational, due to, for example, heavy damage or instrument loss. The fluent can be initiated by an instrIsNonfunctional event and can terminate if one of the events canBeRuler, canBeMessenger, canBeShield, or mustBeDestroyed occurs. In some embodiments, this fluent can be mapped to a checkTransformation action that checks for a possible worker transformation and triggers one of the triggering events that terminate the current fluent. According to some embodiments, each of the terminating events can initiate a new fluent respectively. According to some embodiments, the “transform” fluents, shown in  FIG. 28 , can be mapped to “transformTo” actions, exemplary portions of which are presented in  FIG. 29 . The mapping can transform the worker into a ruler, a messenger, or a shield, according to the example shown. As specified, the transformation attempts can be hierarchically related. Thus, when possible, the transformation process can start with a transformation into a ruler or into a messenger, and then, in case of failure, the algorithm can attempt to perform a transformation into a shield. According to some embodiments, at the end of the hierarchically ordered transformations, self-destruction of the worker can be performed, in case none of the transformations is successful. A self-destruction device can be included in or on the component, for example, an explosive charge and appropriate detonation circuitry mounted in a spacecraft. 
     According to various embodiments. ASSL can allow specification of systems evolving over time. According to some embodiments, the evolution of such systems can take place in the actions of the system. According to some embodiments, via a finite set of change, remove, add, and create statements, the actions of the system can prompt changes in the tiers and sub-tiers of the AS under consideration. 
       FIG. 30  presents a partial specification of some of the actions which can be needed by the self-sacrifice policy. It should be understood that, the “transformTo” actions can change the service-level objectives (SLO) of the worker under consideration. According to some embodiments, these actions can be used to re-specify the component or unit in accordance with the new SLO. According to some embodiments, the transformToShield action can first remove the old worker SLO specifications and next create the new shield SLO, as shown in  FIG. 3 , thus avoiding contradictions between both worker and shield SLO. It should be understood that while the add statements in  FIG. 3  are only partially their use in a more complex algorithm would be apparent to those skilled in the art given the present teachings. 
     According to some embodiments, the transformToShield action can turn off the other worker&#39;s self-management policies to avoid contradictions between both worker and shield SLO. According to some embodiments, the transformToShield action can turn off the other worker&#39;s self-management policies via four change statements, which set the SWITCH flag of the self-management policies to OFF. 
     In some embodiments, the physical transformation can be accomplished by the IMPL routine doShieldTransformation. The IMPL clause states “for further implementation”. This means that the ASSL framework will generate doShieldTransformation as an empty routine for manual implementation. 
     While spacecraft have been exemplified herein as the system components, it is to be understood that other systems and components are also within the scope and spirit of the present teachings. Systems comprising deep sea exploration components, land-based exploration components, atmospheric-based exploration components, or other exploration vehicles, should also be considered to be within the realm of the present teachings. 
     According to the present teachings, a self-managing computer system has been developed based on autonomic computing. The autonomic computing system is analogous to the biological nervous system, which automatically maintains homeostasis (metabolic equilibrium) and controls responsiveness to external stimuli. For example, most of the time a human is not consciously aware of its breathing rate or how fast its heart is beating, although if the human touches a sharp knife with its finger the result is a reflex reaction to move the finger out of danger. If the human cuts itself and starts bleeding, the wound can be treated and the human can then carry on without thinking about it, although pain receptors will induce self-protection and self-configuration to use the other hand. Yet, often the cut will have caused skin cells to be displaced down into muscle tissue. If the cells survive and divide, they have the potential to grow into a tumor. The human body&#39;s solution to this situation is cell self-destruction. There is mounting evidence that some forms of cancer are the result of cells not dying fast enough, rather than multiplying out of control, as previously thought. 
     Biologists believe that cells are programmed to commit suicide through a controlled process known as apoptosis. The term is derived from the Greek word for “to fall off,” in reference to dead leaves falling from trees in autumn. Likewise, cells “fall off” living organisms and die. As  FIG. 31  shows, a cell&#39;s constant receipt of “stay alive” signals turns off the self-destruct sequence. Biological apoptosis is shown in  FIG. 31A , which shows that when a cell constantly receives “stay alive” signals it turns off its programmed self-destruct sequence.  FIG. 31B , on the other hand, shows apoptosis versus necrosis due to an injury. When these signals cease, the cell starts to shrink, internal structures decompose, and all internal proteins degrade; thereafter, the cell breaks into small, membrane-wrapped fragments to be engulfed by phagocytic cells for recycling.  FIG. 31B  contrasts apoptosis, also known as “death by default,” with necrosis, which is the unprogrammed death of a cell due to injury, inflammation, and the accumulation of toxic substances. 
     Autonomic Agents 
     Autonomic computing can depend on many disciplines for its success, including, for example, research in agent technologies. There are no assumptions that an autonomic architecture must use agents, but agent properties complement the objectives of the paradigm. The properties that can be utilized in forming an autonomic computing system according to the present teachings can include, for example, adaptability, autonomy, cooperation, and the like. In addition, complex systems can be formed with multiple agents, and in such embodiments, the systems can comprise inbuilt redundancy and greater robustness, and can be retrofitted in legacy systems with autonomic capabilities that may benefit from an agent-based approach. 
     Referring back to  FIG. 16 , an autonomic computing system is shown according to the present teachings, which comprises a basic autonomic element (AE) that consists of a managed component (MC) and an autonomic manager (AM). The AM can be a stationary agent, for example, a self-managing cell that contains functionality for measurement and event correlation and provides support for policy-based control. The AMs can communicate through an autonomic channel via means such as self-* event messages. The AM AM communications module includes heartbeat monitoring and pulse monitoring. 
     Mobile agents can be utilized in the autonomic systems of the present teachings. Their ability to reduce network load, overcome network latency, encapsulate protocols, execute asynchronously and autonomously, adapt dynamically, reflect natural heterogeneity, and maintain robustness and fault tolerance, can make it easier for AMs within different systems to cooperate. 
     Apoptosis in Agent-Based Autonomic Environments 
     In Greenberg et al., “Mobile Agents and Security,”  IEEE Comm. Magazine , July 1998, pp. 76-85, agent destruction to facilitate security in mobile-agent systems is described, and the publication is incorporated herein in its entirety by reference. The article describes a scenario in which mobile agents—not rogue agents, but instead ones carrying proper authenticated credentials—carried out work that was out of context rather than the result of abnormal procedures or system failure. In this circumstance, the mobile agents could cause substantial damage. For example, the mobile agents could deliver an archaic upgrade to part of a network operating system, bringing down the entire network. These and other misuses involving mobile agents can occur in several forms. Agents can accidentally or unintentionally misuse hosts due to, say, race conditions or unexpected emergent behavior in those agents. In addition, external bodies acting upon agents, either deliberately or accidentally, can lead to their misuse by hosts or other agents. Misuses can result, for example, due to damage, breaches of privacy, harassment, social engineering, event-triggered attacks, or compound attacks. 
     Encryption can prevent situations in which portions of an agent&#39;s binary image could be copied when visiting a host, for example, portions such as monetary certificates, keys, information, and the like. Agent execution, however, requires decryption, which provides a window of vulnerability. This situation is analogous to the body&#39;s vulnerability during cell division. 
       FIG. 32  shows a high-level view of a simple, autonomic environment with three autonomic elements (AEs). It is to be understood, however, that systems having hundreds, thousands, or even millions of AEs are within the scope of the present teachings. Each AE shown in  FIG. 32  is an abstract view of  FIGS. 31A and 3113 , and in this case the MCs represent self-managing computer systems. These AEs can have many other lower-level AEs, for example, an autonomic manager for the disk drive, while at the same time residing within the scope of a higher-level AM: such as a system-wide local area network domain&#39;s AE. In various embodiments, the self-managing computer systems can comprise and autonomic and apoptotic cloud computing system, an autonomic and apoptotic grid computing system, an autonomic and apoptotic highly distributed computing system, a combination thereof, or the like. 
     Within each AM, heartbeat monitors (HBMs) send “I am alive” signals to ensure the continued operation of vital processes in the MC and to immediately indicate if any fail. The AM has a control loop that continually monitors and adjusts, if necessary, metrics within the MC, yet vital processes in the MC can also be safeguarded by an HBM that emits a heartbeat signal as opposed to its being polled by the AM, avoiding lost time (time to next poll) by the AM to notice a failure. Note that in  FIG. 32  the left-hand AE has an HBM between the AM and a process on the MC. Because each AM is aware of its MC&#39;s health via the continuous control loop, it can share this information by sending a pulse signal (“I am tin/healthy”) to another AM, for example, from the left-hand AE to the middle AE as shown in  FIG. 32 . This not only allows self-managing options if the machines are, for example, sharing workload as a cluster, but also protects the AM itself as the pulse signal also acts as an HBM signal from one AM to another. Thus, if an AE&#39;s vital process fails, the neighboring AM will immediately become aware of it and, for example, try to restart the failed AE or initiate a failover to another AM. This pulse signal can also act as a reflex signal between AMs warning of an immediate incident, which is a more direct solution than having the AM process numerous event messages to eventually determine an urgent situation. 
     Because AMs also monitor the external environment (the second control loop), they have a view of the health of their local environment. They can encode such information into the pulse signal along with self-health data (just as our hearts have a double beat). The double-pulse signals between the right-hand and center AEs in  FIG. 32  represent this situation. 
     In some embodiments, AMs can dispatch mobile agents to work on their behalf, for example, to update a set of policies. To help provide self-protection in these situations, AMs can send apoptosis signals (“stay alive/self-destruct”) to such agents by either authorizing continued operation or by withdrawing such authorization. An example of when authorization for continued operation can be withdrawn can include, for instance, when policies become out of date.  FIG. 32  depicts both scenarios. 
     The absence of a “stay alive” signal resulting in agent self-destruction can be referred to as strong apoptotic computing, or programmed death by default, while weak apoptotic computing can involve an explicit self-destruct signal. The differences in these approaches are subtle but important. Only a built-in default death can guarantee true system safety. For example, you would never rely on a self-destruct signal getting through to an agent containing system password updates in a hostile environment. Likewise, a robot with adaptive capabilities could learn to ignore such a signal. Not all circumstances require a death-by-default mechanism, however, many researchers using programmed death under the apoptosis descriptor can use programmed death by default. 
     There is a concern that denial-of-service attacks could prevent “stay alive” signals from reaching their target and thereby induce unintentional agent self-destruction. DoS attacks could likewise interrupt terminate signals, resulting in potentially dangerous scenarios. DoS-immune architectures can thus be useful in the self-managing systems of the present teachings. 
     Swarm Space Exploration Systems 
     Space exploration missions by necessity have become increasingly autonomous and adaptable. To develop more self-sustainable exploration systems, the present teachings provide the use of biologically inspired swarm technologies. Swarms of small spacecraft are used and offer greater redundancy, greater protection of assets, lower costs, lower risks, and the ability to explore more remote regions of space, when compared to a single large craft. Such a new space exploration paradigm calls for missions involving many, for example, thousands of, small spacecraft rather than a single large craft. 
     The Autonomous NanoTechnology Swarm mission, a.k.a. ANTS, (http://ants.gsk.nasa.gov), is a collaboration between NASA&#39;s Goddard Space Flight Center and its Langley Research Center, and exploits swarm technologies and artificial intelligence (AI) techniques to develop revolutionary architectures for both space craft and surface-based rovers. ANTS consists of several submissions: the Saturn Autonomous Ring Array (SARA); the Prospecting Asteroid Mission (PAM); and the Lander Amorphous Rover Antenna (LARA). 
     The Saturn Autonomous Ring Array consists of a swarm of 1,000 pico-class spacecraft, organized as 10 subswarms with specialized instruments, to perform in situ exploration of Saturn&#39;s rings to better understand their constitution and how they were formed. SARA uses self-configuring structures for nuclear propulsion and control as well as autonomous operation for both maneuvering around Saturn&#39;s rings and collision avoidance. 
     The Prospecting Asteroid Mission (PAM) also involves 1,000 pico-class spacecraft but with the aim of exploring the asteroid belt and collecting data on particular asteroids of interest for potential future mining operations. 
     The Lander Amorphous Rover Antenna (LARA) implements new NASA-developed technologies in the field of miniaturized robotics, to form the basis of remote lunar landers launched from remote sites, as well as offering innovative techniques to allow rovers to move in an amoeboid fashion over the moon&#39;s uneven terrain. 
     The ANTS architecture emulates the successful division of labor exhibited by low-level social-insect colonies. In such colonies, with sufficiently efficient social interaction and coordination, a group of specialists usually outperforms a group of generalists. To accomplish their specific mission goals, ANTS systems likewise rely on large numbers of small, autonomous, reconfigurable, and redundant worker craft that act as independent or collective agents. The architecture is self-similar in that ANTS system elements and subelements can be structured recursively, and it is self-managing, with at least one ruler (AM) per ANTS craft. An exemplary system is shown in  FIGS. 27-30 . 
     NASA missions such as ANTS provide a trusted private environment, eliminating many agent security issues and enabling system designers to focus on ensuring that agents are operating in the correct context and exhibiting emergent behavior within acceptable parameters. 
     In considering the role of the self-destruct property inspired by apoptosis, suppose one of the worker craft in the ANTS mission was operating incorrectly and, when coexisting with other workers, was causing undesirable emergent behavior and failing to self-heal correctly. That emergent behavior could put the mission in danger, and ultimately the ruler would withdraw the “stay alive” signal. Likewise, if a worker or its instrument was damaged, either by colliding with another worker or (more likely) an asteroid, or during a solar storm, the ruler would withdraw the “stay alive” signal and request a replacement worker. Another worker would then self-configure to take on the role of the lost worker to ensure optimal balanced coverage of tasks to meet the scientific goals. If a ruler or messenger was similarly damaged, its ruler would withdraw the “stay alive” signal and promote a worker to play its role. 
     The majority of these applications fall into the weak apoptotic computing (programmed death) category, and would likely benefit from, instead, utilizing a strong (programmed death by default) approach. They also highlight a strong need for standards and trust requirements, and a DoS-resistant architecture. 
     The human body regulates vital functions such as heartbeat, blood flow, and cell growth and death, all without conscious effort. The present teachings provide computer-based systems that can perform similar operations on themselves without constant human intervention. 
     The apoptotic computing applications of the present teachings have been developed for data objects, highly distributed systems, services, agent systems, and swarm systems. According to some embodiments, the entire computer-based system is autonomic. In some cases, the entire system can be apoptotic. The apoptotic controls can ver all levels of human-computer interaction from data, to services, to agents, to robotics. With recent headline incidents of credit card and personal data losses by organizations and governments, and scenarios once relegated to science fiction becoming increasingly possible, programmed death by default can be a useful tool toward securing such systems. 
     In some embodiments, the autonomous computer-based systems and robots undergo tests, similar to ethical and clinical trials for new drugs, before they are used. Emerging research from apoptotic computing can be used to guide the safe deployment of such systems. 
     According to various embodiments of the present teachings, the properties of the autonomic, or self-managing, computing system include four objectives that represent broad system requirements, and four attributes that identify basic implementation mechanisms. These objectives and requirements are described, for example, in Sterritt, “Towards Autonomic Computing: Effective Event Management,”  Proc.  27 th    Ann. IEEE/NASA Software Eng. Workshop  (SEW 02), IEEE CS Press, 2002, pp. 40-47, and in Sterritt et al., “Autonomic Computing—A Means of Achieving Dependability?”  Proc.  10 th IEEE Int&#39;l Conf. and Workshop Eng. of Computer Based Systems  (ECBS 03), IEEE CS Press, 2003, pp. 247-251, both of which are incorporated herein in their entireties by reference. 
     According to various embodiments, the autonomic system can have the following objectives: self-configuration; self-healing; self-optimization; and self-protection. By self-configuration, what is meant is that the system can be able to readjust itself automatically, either to support a change in circumstances or to assist in meeting other system objectives. By self-healing, what is meant is that, in a reactive mode, the system can effectively recover when a fault occurs, identify the fault, and, when possible, repair it. In a proactive mode, self-healing can entail a system configured to monitor vital signs to predict and avoid health problems, or to prevent vital signs from reaching undesirable levels. By self-optimization, what is meant is the system can measure its current performance against a known optimum, and can carry out defined policies for attempting improvements. Self-optimization can also encompass a system configured to react to a user&#39;s policy changes within the system. By self-protection, what is meant is that the system can defend itself from accidental or malicious external attacks, which requires an awareness of potential threats and the means to manage them. 
     According to various embodiments of the present teachings, these self-managing objectives can be achieved be configuring the system to be: self-aware, that is, aware of its internal state; self-situated, that is, aware of current external operating conditions and context; self-monitoring, that is, able to detect changing circumstances; and self-adjusting, that is, able to adapt accordingly. Thus, the autonomic systems of the present teachings can be aware of its available resources and components, their ideal performance characteristics, and current status. The system can also be aware of interconnection with other systems, as well as rules and policies for adjusting as required. The system can also operate in a heterogeneous environment, for example, by relying on open standards to communicate with other systems. 
     According to various embodiments, these mechanisms do not exist independently. For example, to successfully survive an attack, the system can exhibit self-healing abilities, with a mixture of self-configuration and self-optimization. This not only ensures the system&#39;s dependability and continued operation but also increases self-protection from similar future attacks. The self-managing mechanisms can also ensure minimal disruption to users. 
     Although specific embodiments have been illustrated and described herein, it will be appreciated by those of ordinary skill in the art that any arrangement which is calculated to achieve the same purpose can be substituted for the specific embodiments shown. This application is intended to cover any adaptations or variations. For example, although described in procedural terms, one of ordinary skill in the art will appreciate that implementations can be performed in an object-oriented design environment or any other design environment that provides the required relationships. 
     In particular, one of skill in the art will readily appreciate that the names of the methods and apparatus are not intended to limit embodiments. Furthermore, additional methods and apparatus can be added to the components, functions can be rearranged among the components, and new components to correspond to future enhancements and physical devices used in embodiments can be introduced without departing from the scope of embodiments. One of skill in the art will readily recognize that embodiments are applicable to future communication devices, different file systems, and new data types. 
     The terminology used in this application is meant to include all environments and alternate technologies which provide the same functionality as described herein.