Patent Publication Number: US-7904396-B2

Title: Systems, methods and apparatus for quiesence of autonomic safety devices with self action

Description:
RELATED APPLICATIONS 
     This application claims the benefit of U.S. Provisional Application Ser. No. 60/789,629 filed Mar. 8, 2006 under 35 U.S.C. 119(e). This application also claims, under 35 U.S.C. 120, the benefit of, and is a continuation-in-part to, co-pending U.S. Original application Ser. No. 11/533,855 filed Sep. 21, 2006, entitled “SYSTEMS, METHODS AND APPARATUS FOR QUIESCENCE OF AUTONOMIC SYSTEMS,” which claims the benefit of U.S. Provisional Application Ser. No. 60/811,149 filed May 15, 2006 under 35 U.S.C. 119(e), which claims, under 35 U.S.C. 120, the benefit of, and is a continuation-in-part to co-pending U.S. Original application Ser. No. 11/426,853, filed Jun. 27, 2006, entitled “SYSTEMS, METHODS AND APPARATUS OF SELF-PROPERTIES FOR AN AUTONOMOUS AND AUTOMATIC COMPUTER ENVIRONMENT” which claims the benefit of U.S. Provisional Application Ser. No. 60/662,990 filed Jun. 27, 2005 under 35 U.S.C. 119(e), which claims, under 35 U.S.C. 120, the benefit of, and is a continuation-in-part to co-pending U.S. Original application Ser. No. 11/251,538, filed Sep. 29, 2005, entitled “SYSTEM AND METHOD FOR MANAGING AUTONOMOUS ENTITIES THROUGH APOPTOSIS,” which claims the benefit of U.S. Provisional Application Ser. No. 60/634,459 filed Dec. 7, 2004 under 35 U.S.C. 119(e). 
    
    
     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 
     Conventional smoke detectors are helpful in alerting people to fire danger and thus saving lives and property. Smoke detectors are generally categorized or described as devices that improve safety for subjects such as humans, animals and equipment by detecting hazardous conditions that are unsafe to the subject and that also improve safety by providing an alert of the condition. 
     One function of conventional smoke detectors is detecting a weak battery source, or other weak power source. Commonly, smoke detectors emit an intermittent high-pitched beep to alert humans to the unreliability of the smoke detector and the need to replace the batteries. Alerting humans to the low battery may provide only the most primitive of status reporting and indication. Most unfortunately, the beeping can be performed only while sufficient power remains in the battery. At some point in time, as the battery power continues to weaken, without refreshed batteries, or connection to an A/C power source, the smoke detector will not receive sufficient power from the batteries 
     In some instances, the operation of a particular smoke detector can be either detrimental to the smoke detector itself or to the facility in which the smoke detector is located. For example, a smoke detector itself might cause a fire in the facility when the electronics in the smoke detector malfunction. However, conventional systems are largely ineffective at preventing such a problem. 
     For the reasons stated above, and for other reasons stated below which will become apparent to those skilled in the art upon reading and understanding the present specification, there is a need in the art to discover and report fault(s) or failure of the smoke detectors when most needed. There is also a need in the art to reduce the possibility that smoke detectors will damage the system of which the smoke detector is a part. There is also a need in the art for smoke detectors that can be functionally extracted from an environment upon the occurrence of a predetermined condition. 
     BRIEF DESCRIPTION OF THE INVENTION 
     The above-mentioned shortcomings, disadvantages and problems are addressed herein, which will be understood by reading and studying the following specification. 
     In at least one embodiment, a method for managing a system of autonomic environmental safety devices based on functioning state and operating status of the system of autonomic environmental safety devices may include processing received signals from the system of autonomic environmental safety devices indicative of the functioning state and the operating status to obtain an analysis of the condition of the system of autonomic environmental safety devices, generating one or more stay awake signals based on the functioning status and the operating state of the system of autonomic environmental safety device, transmitting the stay-awake signal, transmitting self health/urgency data, and transmitting environment health/urgency data of the autonomic environmental safety devices. In some embodiments, an autonomic environmental safety device may be a smoke detector. 
     In other embodiments, an autonomic environmental safety device may include a self-monitor that is operable to receive information from sensors and 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; 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 operable to produce and transmit a pulse monitor signal, the pulse monitor signal including a heart beat monitor signal and a reflex signal, the reflex signal including self health/urgency data and environment health/urgency data. 
     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 of an overview of a system of autonomic environmental safety devices, according to an embodiment; 
         FIG. 2  is a flowchart of a method to construct an environment to satisfy increasingly demanding external requirements, according to an embodiment; 
         FIG. 3  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. 4  is a flowchart for generating a stay alive signal when a warning condition occurs, according to an embodiment; 
         FIG. 5  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. 6  is a flowchart for generating a stay awake signal when a warning condition occurs, according to an embodiment; 
         FIG. 7  is a flowchart for interrogating an anonymous autonomic agent, according to an embodiment; 
         FIG. 8  is a flowchart of a method of autonomic communication by an autonomic element, according to an embodiment; 
         FIG. 9  is a flowchart of a method of autonomic communication by an autonomic element, according to an embodiment; 
         FIG. 10  is a flowchart of a method of autonomic communication by an autonomic element, according to an embodiment; 
         FIG. 11  is a flowchart of a method of autonomic communication by an autonomic element, according to an embodiment. 
         FIG. 12  is a flowchart of a method in which a ruler entity controls a stay alive signal in an autonomic environmental safety device, according to an embodiment; 
         FIG. 13  is a flowchart of a method to determine the recoverability of an alarm or other fault condition in an autonomic environmental safety device, according to an embodiment; 
         FIG. 14  is a flowchart of a method of a ruling autonomic device controlling a stay-awake signal of an autonomic environmental safety device, according to an embodiment; 
         FIG. 15  is a flowchart of a method for determining the recoverability of an alarm condition of an autonomic environmental safety device, according to an embodiment; 
         FIG. 16  is a flowchart of a method of a ruling autonomic device controlling a stay-awake signal of an autonomic environmental safety device, according to an embodiment; 
         FIG. 17  is a flowchart of a method of autonomic communication by an autonomic environmental safety device, according to an embodiment; 
         FIG. 18  is a flowchart of a method of autonomic communication by an autonomic environmental safety device, according to an embodiment; 
         FIG. 19  is a flowchart of a method of autonomic communication by an autonomic environmental safety device, according to an embodiment; 
         FIG. 20  is a flowchart of a method of autonomic communication by an autonomic environmental safety device, according to an embodiment; 
         FIG. 21  is a block diagram of a conventional computer cluster environment in which different embodiments can be practiced; 
         FIG. 22  is a block diagram of a conventional hardware and operating environment in which different embodiments can be practiced; 
         FIG. 23  is a block diagram of a conventional multiprocessor hardware and operating environment in which different embodiments can be practiced; 
         FIG. 24  is a block diagram of a hardware and operating environment which may include a quiesce component, according to an embodiment; 
         FIG. 25  is a block diagram of an apparatus of an evolvable synthetic neural system to manage collective interactions between autonomous entities, according to an embodiment; 
         FIG. 26  is a block diagram of a neural basis function of a worker, according to an embodiment; 
         FIG. 27  is a block diagram of a heuristic neural system, according to an embodiment; 
         FIG. 28  is a block diagram of an autonomous neural system, according to an embodiment; 
         FIG. 29  is a block diagram of a neural basis function of a worker, according to an embodiment; 
         FIG. 30  is a block diagram of a multiple level hierarchical evolvable synthetic neural system, according to an embodiment; 
         FIG. 31  is a block diagram of a plurality of autonomic entities including autonomic environmental safety devices assembled to perform a task, according to an embodiment; 
         FIG. 32  is a block diagram of an autonomous entity management system, according to an embodiment; 
         FIG. 33  is a diagram of a three dimensional hierarchical evolvable synthetic neural system, according to an embodiment; 
         FIG. 34  is a diagram of a heuristic neural system, according to an embodiment, for a single instrument spacecraft to prospect asteroid belts; 
         FIG. 35  is a diagram of an autonomous entity managing a system, according to an embodiment; 
         FIG. 36  is a diagram of autonomous entities interaction, according to an embodiment; 
         FIG. 37  is a block diagram of an autonomous entity management system, according to an embodiment; 
         FIG. 38  is a hierarchical chart of an autonomous entity management system, according to an embodiment; 
         FIG. 39  is a block diagram of an autonomic element, according to an embodiment; 
         FIG. 40  is a block diagram of autonomy and autonomicity at a high system level, according to an embodiment; and 
         FIG. 41  is a block diagram of an architecture of an autonomic element (AE), according to an embodiment that may include reflection and reflex layers. 
     
    
    
     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 may 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 may be utilized and that logical, mechanical, electrical and other changes may be made without departing from the scope of the embodiments. The following detailed description is, therefore, not to be taken in a limiting sense. 
     The detailed description is divided into five sections. In the first section, a system level overview is described. In the second section, embodiments of methods are described. In the third section, hardware and the operating environments in conjunction with which embodiments may be practiced are described. In the fourth section, particular implementations of apparatus are described. Finally, in the fifth section, a conclusion of the detailed description is provided. 
     System Level Overview 
       FIG. 1  is a block diagram of an overview of a system  100  of autonomic environmental safety devices, according to an embodiment. System  100  may solve the need in the art to discover and report fault(s) or failure of the smoke detectors and may solve the need in the art to reduce the possibility that smoke detectors will jeopardize the mission of the system of which the smoke detector is a part. 
     The autonomic smoke detectors  102 ,  104 ,  106 ,  108  and  110  (or other autonomic environmental safety devices), can transmit status data such as environment health/urgency data of the autonomic smoke detectors over a network  112 . The status data may be available for analysis by all of the autonomic smoke detectors  102 ,  104 ,  106 ,  108  and  110  and by other entities that access the network  112 . The status information can be analyzed to identify existing or potential faults of the smoke detectors. Thus, system  100  may provide a mechanism to determine if any of the autonomic environmental safety devices may be faulty. 
     The autonomic smoke detectors  102 ,  104 ,  106 ,  108  and  110  and the network  112  may be organized in a heuristic neural system (HNS) architecture. Each smoke detector can be a member of a swarm of autonomous environmental safety devices and may provide coordination and interaction between each HNS that yields performance of the aggregate of the swarm that exceeds the performance of a group of generalist autonomic environmental safety devices. 
     In some embodiments of system  100 , each of a number of AESDs may be an autonomic smoke detector. Each autonomic smoke detector in a swarm may have a specialized mission, much like ants in an ant colony have a specialized mission. Yet, a heuristic neural system (HNS) architecture of each autonomic smoke detector may provide coordination and interaction between each HNS that yields performance of the aggregate of the AESDs that exceeds the performance of a group of generalist AESDs. 
     A synthetic neural system may be defined as an information processing paradigm that is inspired by biological nervous systems that process information, such as the brain. Biological systems inspire system design in many other ways, such as reflex reaction and health signs, nature inspired systems, hive and swarm behavior, and fire flies, for example. These synthetic systems can provide an autonomic computing entity that can be arranged to manage complexity, continuous self adjustment, adjustment to unpredictable conditions, and prevention and recovery from failures. 
     In some embodiments, a key element of synthetic neural systems may be the general architecture of the synthetic neural system. A synthetic neural system may be 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 may be typically configured for a specific application and sometimes for a limited duration. 
     In some embodiments of system  100 , the autonomic smoke detectors  102 ,  104 ,  106 ,  108  and  110  may be operable to signal a network alarm, and in some embodiments, the autonomic smoke detectors  102 ,  104 ,  106 ,  108  and  110  may also be operable to trip an alarm on the autonomic smoke detector that detected an environmental condition. 
     In some embodiments, the autonomic smoke detectors  102 ,  104 ,  106 ,  108  and  110  may also be operable to transmit a signal indicating failure of the autonomic smoke detectors  102 ,  104 ,  106 ,  108  and  110 . In other embodiments, all of the autonomic smoke detectors  102 ,  104 ,  106 ,  108  and  110  may be operable to signal the failure of one of the other autonomic smoke detectors  102 ,  104 ,  106 ,  108  and  110 . 
     In a situation where all but one of the autonomic smoke detectors  102 ,  104 ,  106 ,  108  and  110  in a building may be operating correctly (i.e., if one autonomic smoke detector is faulty, or to lose power, such as battery failure, etc.) the fault of that one autonomic smoke detector would be detected by the failure of other autonomic smoke detectors  102 ,  104 ,  106 ,  108  and  110  to receive a first “heartbeat” (the “lub”) within a specified period of time, and the second beat (the “dub”) to carry operational data. The heartbeat (the “lub”) can also be known as a heart beat monitor (HBM) signal and second beat can also be known as a pulse monitor (PBM) signal. In some embodiments, if an alarm signal is to be sounded by one of the autonomic smoke detectors, the second beat (the PBM) may convey the alarm information to the other autonomic smoke detectors in the building so that the other autonomic smoke detectors can activate their own alarm. 
     In some embodiments, the autonomic smoke detectors  102 ,  104 ,  106 ,  108  and  110  may be operable to link devices within a building together through wireless connections. 
     System  100  may have applications and embodiments in a large number of areas beyond a smoke detector. Beyond smoke detectors, other types of autonomic environmental safety devices may include burglar alarm systems, sprinkler systems, satellites, and any “sensor network” or monitoring system where a number of simple sensors may be used together in a way that collects information regarding the status of various devices. In some of these applications, the first beat of the lub-dub is used to carry health information (i.e., that the device is functioning correctly and forwarding data readings in a timely fashion) and the second to carry instructions or signals, either trigged by a particular event (e.g., detection of smoke) or to perform various operational functions. 
     The autonomic smoke detectors  102 ,  104 ,  106 ,  108  and  110  may be a particular embodiment of autonomic environmental safety devices that illustrate the principles of how autonomic environmental safety devices in general operate. System  100  may provide a way for all smoke detectors in the house/building to indicate environmental hazards. Wireless embodiments of the network  112  can provide for easy installation of the autonomic smoke detectors  102 ,  104 ,  106 ,  108  and  110 . 
     System  100  is not limited by the number of autonomic environmental safety devices. Some embodiments of system  100  may include one autonomic environmental safety device, some embodiments may include a plurality of autonomic environmental safety devices. 
     Method Embodiments 
     In the previous section, an overview of some system embodiments is described. In this section, embodiments of 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 may also be composed of computer-executable instructions. In some embodiments, method  200  may be performed by a program executing on, or performed by firmware or hardware that is a part of a computer, such as computer  2202  in  FIG. 22 . 
       FIG. 2  is a flowchart of a method  200  to construct an environment to satisfy increasingly demanding external requirements, according to an embodiment. 
     Method  200  may include instantiating  202  an embryonic evolvable neural interface (ENI). In some embodiments, the embryonic ENI lacks a complete specification of the operational characteristics of the ESNS or an ENI. The embryonic ENI can be a neural thread possessing only the most primitive and minimal connectivity. 
     Method  200  can further include evolving  204  the embryonic ENI towards complex complete connectivity. Specifications of the inter-ENI  106  can be developed from the initial embryonic form. Thus, a very complex problem that, in some embodiments, may be represented by a complete specification can be replaced by a simpler specification of the embryonic ENI that is evolved to meet increasingly demanding requirements. Progression from an embryonic state to a more complex state can avoid the necessity of specifying the complex complete connectivity initially, but rather can reduce the problem to one of developing methods to drive the evolution of simple limited connectivity to complex complete connectivity. 
     An adaptive or evolutionary nature of an artificial intelligence construct in method  200  can be predicated on an active revision of the embryonic ENI to meet external action requirements for a sensory input. In particular, the ENI can evolve due to changing conditions that are either driven by training requirements or operational requirements. 
     In some embodiments, method  200  may be implemented as a computer data signal embodied in a carrier wave that represents a sequence of instructions, which, when executed by a processor, such as processor  2204  in  FIG. 22 , causes the processor to perform the respective method. In other embodiments, method  200  may be implemented as a computer-accessible medium having executable instructions capable of directing a processor, such as processor  2204  in  FIG. 22 , to perform the respective method. In varying embodiments, the medium can be a magnetic medium, an electronic medium, or an optical medium. 
       FIG. 3  is a flowchart of a method  300  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  300  may solve the need in the art for management of autonomous entities that can be functionally extracted from an environment upon the occurrence of a predetermined condition. Method  300  can begin with action  302  when receiving a signal from a managed entity. 
     Action  302  can receive a heart beat monitor (HBM) signal and pulse monitor (PBM) signal from a managed entity such as worker entities  3118  or  3120 . The HBM 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 may 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 may 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  304  for further processing. 
     In action  304 , an analysis of the HBM and PBM signal may be performed to determine trends and possible areas of concern. Some purposes of the analysis may be to determine exceedance from a predetermined condition, 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  306  for further processing. 
     In action  306 , an alarmed condition may be determined. In action  306 , the analysis of action  304  may be consulted to determine if there is one or more alarm condition that can trigger the withdrawal of a stay alive signal. If it is determined that there are no alarm conditions, control may be passed to action  308  so as to generate a stay alive signal. In the event that an alarm condition is present, control may be passed to action  310  for further processing. 
     In action  310 , a determination may be made to ascertain whether the identified alarmed condition of action  306  is recoverable by the managed entity. When an alarmed condition is determined to be recoverable, control may be passed to action  308  to generate a stay alive signal. When an alarmed condition is determined not to be recoverable, control may be passed to action  312  to withdraw the stay alive signal. Method  400  below can be one embodiment of determining  310  if the identified alarmed condition is recoverable. 
       FIG. 4  is a flowchart of a method  400  for ascertaining the recoverability of an alarmed condition determined at action  306 , according to an embodiment. Method  400  may solve the need in the art for management of autonomous entities that can be functionally extracted from an environment upon the occurrence of a predetermined condition. Method  400  may be one possible embodiment of the action in  FIG. 3  above of determining  310  if the identified alarmed condition is recoverable. 
     Method  400  may begin with action  402  when receiving one or more alarmed condition. In action  402 , there may be a determination if an incorrect operation from the managed system has been identified in action  304  of  FIG. 3 . An incorrect operation can range from not initializing sensors to failing to self-heal when internal decision logic recommends such as an appropriate cause of action. In action  402 , in addition to determining if an incorrect operation has been identified, it may also be possible to ascertain the number of devices or processes within the entity that registered an incorrect operation. If at least one incorrect operation is determined, the action may transfer the identity of the unit to evaluation block  408  for further processing. 
     In action  404 , there may be a determination whether emergent behavior from the managed system has been identified in action  304  of  FIG. 3 . 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 may be known as non-linearity, chaos, or capacity limits. The complex behavior or properties may not 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 may 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 may directly command the system to form a pattern, but the interactions of each part (entities) to its immediate surroundings may 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 may be forwarded to evaluation block  408  for further processing. 
     In action  406 , a determination may be made 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 may be passed to evaluation block  408  for further processing. 
     Evaluation block  408  may marshal the incorrect operation identified in action  402 , the emergent behavior in action  404 , or the effect on mission in action  406  to suggest a course of action that the managed entities should adopt, which in the illustrated embodiment 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 may be passed to decision block  410  for further processing in accordance to the decision made in evaluation block  408 . 
     In action  410 , if the desired control instruction is to maintain the stay alive signal, control can be passed to action  308  for further processing. In the alternative, a withdrawal of the stay alive signal can be sent to action  312  for further processing. It should be noted that generating a stay alive signal may 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 it can determine the outcome (life or death) of an entity. 
       FIG. 5  is a flowchart of a method  500  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  500  may solve the need in the art to reduce the possibility that an autonomic element will jeopardize the mission of the autonomic element. 
     Method  500  may begin with action  302  when receiving a signal from a managed entity. Action  302  can receive a heart beat monitor (HBM) signal and pulse monitor (PBM) signal from a managed entity such as worker entities  1418  or  1420 . 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 may 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 may 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  304  for further processing. 
     In action  304 , an analysis of the HBM and PBM signal may be performed to determine trends and possible areas of concern. The purpose of the analysis could 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  306  for further processing. 
     In action  306 , an alarmed condition can be determined. In action  306 , the analysis of action  304  may be consulted to determine if there is one or more alarm condition that can trigger the withdrawal of a stay-awake signal. If it is determined that there are no alarm conditions, control may be passed to action  502  so as to generate a stay-alive signal. In the event that an alarm condition is present, control may be passed to action  504  for further processing. 
     In action  504 , a determination can be made to ascertain if the identified alarmed condition of action  306  is recoverable by the managed entity such as worker entities  1418  and  1420  of  FIG. 14 . When an alarmed condition is determined not to be recoverable, control may be passed to action  312  to withdraw the stay-alive signal. Method  600  below could be one embodiment of determining  504  if the identified alarmed condition is recoverable. When an alarmed condition is determined to be recoverable, control may be passed to action  508  in which a determination can be made 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  502  to generate a stay-awake/stay-alive-signal. When quiescence of the managed entity is determined as possible and/or needed in action  508 , control can pass to action  510 , to withdraw the stay-awake signal. Thus, quiescing the managed entity may solve the need in the art to functionally extract the managed entity from an environment upon the occurrence of an alarmed condition. Quiescence may be a less encompassing alternative to withdrawing the stay-awake signal of apoptosis. Method  500  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. 6  is a flowchart of a method  600  for ascertaining the recoverability of an alarmed condition determined at action  504 . Method  600  may solve the need in the art for management of autonomous entities that can be functionally extracted from an environment upon the occurrence of a predetermined condition. Method  600  may begin with action  402  when receiving one or more alarmed condition. 
     In action  402 , there may be a determination if an incorrect operation from the managed system has been identified in action  304  of  FIG. 3 . An incorrect operation can range from not initializing sensors to failing to self-heal when internal decision logic recommends such as an appropriate cause of action. In action  402 , in addition to determining if an incorrect operation has been identified, it may also be possible to ascertain the number of devices or processes within the entity that registered an incorrect operation. If at least one incorrect operation is determined, the action can transfer the identity of the unit to evaluation block  408  for further processing. 
     In action  404 , there may be a determination of emergent behavior from the managed system that has been identified in action  304  of  FIG. 3 . 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 may 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 may not 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 may 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 may be forwarded to evaluation block  408  for further processing. 
     In action  406 , a determination can be made 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 may be passed to evaluation block  408  for further processing. 
     Evaluation block  408  can marshal the incorrect operation identified in action  402 , the emergent behavior in action  404 , and the effect on mission in action  406  to suggest a course of action that the managed entities should adopt, which in the illustrated embodiment 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  602  for further processing in accordance with the decision made in evaluation block  408 . 
     In action  602 , if the desired control instruction is to maintain the stay-awake signal, control can be passed to action  508  for further processing. In the alternative, a withdrawal of the stay-awake signal can be sent to action  506  for further processing. It should be noted that 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 it can determine the outcome (life or death) of an entity. 
       FIG. 7  is a flowchart of a method  700  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  700  may solve the need in the art for management of autonomous entities that can be functionally extracted from an environment upon the occurrence of a predetermined condition. Method  700  may begin with action  702  where an ALice signal is sent to an anonymous agent to ascertain the agent&#39;s potential for harm to a system as shown in  FIG. 4 . After the ALice signal has been sent to the agent, control may be passed to action  704  for further processing. 
     In action  704  the response from the agent may 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  704  is completed, control may be passed to action  706  for further processing. 
     In action  706 , the monitored response from action  704  may be analyzed to determine if it is in an appropriate format, within a certain timeout period, and with a valid and justified reason for being within the locus of interest or domain of the autonomous system. Once the potential for causing harm has been ascertained, control may be passed to action  708  for further processing. 
     In action  708 , the system may 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 it is friendly and has permission to access certain resources. If it fails to identify itself appropriately following an ALice interrogation, it may 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. 5 ,  6  and  10 . 
       FIG. 8  is a flowchart of a method  800  of autonomic communication by an autonomic element. Method  800  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  800  may include transmitting self health/urgency data  802 . Examples of the self health/urgency data may include information describing low battery power and/or failed sensors. Method  800  may also include transmitting  804  environment health/urgency data. Examples of the environment health/urgency data may include information describing inaccessible devices, unauthorized access, and/or an unidentified mobile agent sending communication signals. 
     Transmitting  802  and  804  can be performed in any order relative to each other. For example, in one embodiment the transmitting  802  self health/urgency data may be performed before transmitting  804  environment health/urgency data. In another embodiment, transmitting  804  environment health/urgency data may be performed before transmitting  802  self health/urgency data. In yet another embodiment, the self health/urgency data may be transmitted simultaneously with the environment health/urgency data. For example, the environment health/urgency data and the self health/urgency data may be transmitted together. One example of transmitting the environment health/urgency data and the self health/urgency data may 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 may 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 may 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  4120  of  FIG. 41  below 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  FIG. 35 ,  36  below and  FIG. 3  above. Together with the standard event messages on an autonomic communications channel, this may 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  4118  may produce a reflex signal  4120  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. 
     In some embodiments, an aspect of the reflex reaction and the pulse monitor may be the minimization of data sent—essentially only a “signal” may be transmitted. Strictly speaking, this may not be 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. 9  is a flowchart of a method  900  of autonomic communication by an autonomic element. Method  900  may include transmitting  902  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 below in connection with  FIG. 36 . 
     In some embodiments, the self health/urgency data and environment health/urgency data encoded with the standard event messages on an autonomic communications channel may provide dynamics within autonomic responses and multiple loops of control, such as reflex reactions among an autonomic manager. 
       FIG. 10  is a flowchart of a method  1000  of autonomic communication by an autonomic element. Method  1000  may include receiving  1002  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 may be the self awareness control loop  4114  of the autonomic element  4100  of  FIG. 41  below. 
     Method  1000  may also include receiving  1004  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 may be the environment awareness control loop  4108  of the autonomic element  4100  of  FIG. 41  below. 
       FIG. 11  is a flowchart of a method  1100  of autonomic communication by an autonomic element. Method  1100  may 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  1100  may include transmitting uncompressed self health/urgency data  1102 . Method  1100  may also include transmitting  1104  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 may 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  1102  and  1104  can be performed in any order relative to each other. 
       FIG. 12  is a flowchart of a method  1200  in which a ruler entity controls a stay alive signal in an autonomic environmental safety device, according to an embodiment. Method  1200  may solve the need in the art for management of autonomous entities that can be functionally extracted from an environment upon the occurrence of a predetermined condition. 
     Method  1200  may include the ruler entity receiving  1202  a heart beat monitor (HBM) signal and pulse monitor (PBM) signal from the autonomic environmental safety device (AESD) or other managed entity. The HBM signal may be an indication that the autonomic environmental safety device may be operating. The HBM can be an “ON/OFF” state signal, an indication that a process may be being performed, or any other signal that can convey information that the autonomic environmental safety device is alive or active. The PBM signal may 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 thus can 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 may be a set of abstractions regarding sensors that may 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, fault conditions, and any other summarization that would convey the general health of the system. After the HBM and PBM signals have been received, control may be forwarded to action  1204  for further processing. 
     In action  1204 , an analysis of the HBM and PBM signal may be performed to determine trends and possible areas of concern. The purpose of the analysis may be to determine that a predetermined condition may have been exceeded, generate a projection through simulation and data modeling areas of parameters that can lead to the failure of the autonomic environmental safety device 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. 
     In action  1206 , the existence of an alarm condition may be determined. In action  1206 , the analysis of action  1204  can be referenced to determine existence of one or more alarms or other fault conditions that can trigger the withdrawal of a stay alive signal. If no fault conditions may be determined to exist, control may be passed to action  1208  so as to generate a stay alive signal. 
     In action  1210 , a determination may be made to ascertain if the identified alarm or fault condition of action  1206  is recoverable by the autonomic environmental safety device. When an alarm or fault condition is determined to be recoverable, control may be passed to action  1208  to generate a stay alive signal. When an alarm or fault condition is determined not to be recoverable, control may be passed to action  1212  to withdraw the stay alive signal. Method  1300  below is one embodiment of determining  1210  if the identified fault condition is recoverable. 
       FIG. 13  is a flowchart of a method  1300  to determine the recoverability of an alarm or other fault condition in an autonomic environmental safety device, according to an embodiment. Method  1300  may solve the need in the art for management of autonomic environmental safety devices that can be functionally extracted from an environment upon the occurrence of a predetermined condition. Method  1300  is one embodiment of the action in  FIG. 12  above of determining  1210  if the identified fault condition is recoverable. 
     Method  1300  may begin with determining  1302  if a battery of the autonomic environmental safety device (AESD) is emitting low amounts of power. If the battery is determined to be emitting low power, control can pass to evaluation block  1306  for further processing. 
     If the battery is determined to be not emitting low power, control can pass to determining  1304  if the AESD is operable. After determining if the AESD is operable, control can pass to evaluation block  1306  for further processing. 
     In action  1306 , a determination may be made of fault conditions that can have an impact on the success of the mission or task by which all entities may be 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. The 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 AESDs is showing incorrect operation or alarm conditions. 
     Evaluation block  1306  may reference the battery level determined in action  1302  and the operability of the AESD determined in action  1304  to suggest a course of action that the managed entities should adopt, which in method  1300  is illustrated as being 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 fault conditions, or a combination of two or more of the identified fault 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. After the evaluation is determined, control can pass to decision block  1308  for further processing in accordance to the decision made in evaluation block  1306 . 
     In action  1308 , if the desired control instruction is to maintain the stay alive signal, control can be passed to action  1208  for further processing. In the alternative, a withdrawal of the stay alive signal may be sent to action  1212  for further processing. It should be noted that 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 may be a managing entity since it can determine the outcome (life or death) of an entity. 
       FIG. 14  is a flowchart of a method  1400  of a ruling autonomic device controlling a stay-awake signal, according to an embodiment. Method  1400  may solve the need in the art to reduce the possibility that an autonomic environmental safety device will jeopardize the facility or mission of the autonomic environmental safety device. 
     Method  1400  may include receiving  1202  a signal from an autonomic environmental safety device (AESD) or some other managed entity. Action  1202  may receive a heart beat monitor (HBM) signal and pulse monitor (PBM) signal from the autonomic environmental safety device. The HBM signal may be an indication that the autonomic environmental safety device is operating. The HBM signal 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 autonomic environmental safety device is awake or active. The PBM signal may 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 thus can 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 sensors that may 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, fault conditions, and any other summarization that would convey the general health of the system. After the HBM and PBM signals have been received, control can be forwarded to action  1204  for further processing. 
     In action  1204 , an analysis of the HBM and PBM signal may be performed to determine trends and possible areas of concern. The purpose of the analysis may be to determine that a predetermined condition may have been exceeded, generate a projection through simulation and data modeling areas of parameters that can lead to the failure of the autonomic environmental safety device 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. After the analysis has been performed, control can pass to action  1206  for further processing. 
     In action  1206 , existence or non-existence of an alarm condition in the AESD may be determined. In action  1206 , the analysis of action  1204  may be referenced to determine if one or more alarm conditions exist that can trigger the withdrawal of a stay-awake signal. If it is determined that no alarm conditions exist in the AESD, control can be passed to action  1402  to generate a stay-alive signal and a stay-awake signal. In the event that an alarm condition of the AESD is present, control can pass to action  1404  for further processing. 
     In action  1404 , a determination may be made as to whether or not the identified AESD alarm condition of action  1206  is recoverable by the autonomic environmental safety device. When an alarm condition of the AESD is determined not to be recoverable, control can be passed to action  1406  to withdraw a stay-alive signal. Method  1500  below is one embodiment of determining  1404  if the identified alarm condition is recoverable. When the alarm condition is determined to be recoverable, control can be passed to action  1408  in which a determination may be made as to whether or not quiescing the autonomic environmental safety device is possible and/or and whether or not subsequent recovery is needed. When quiescence of the AESD is determined as not possible and recovery is not needed later, control can pass to action  1402  to generate a stay-awake/stay-alive-signal. When quiescence of the autonomic environmental safety device is determined as possible and/or needed in action  1408 , control can pass to action  1410  to withdraw the stay-awake signal. Thus, quiescing the autonomic environmental safety device may solve the need in the art to functionally extract the AESD from an environment upon the occurrence of an alarm condition. Quiescence may be a less encompassing alternative to withdrawing the stay-awake signal of apoptosis. Method  1400  may allow an AESD that is in danger or endangering the mission to be put into a self-sleep mode, then later reactivated or self-destructed. 
       FIG. 15  is a flowchart of a method  1500  for determining the recoverability of an alarm condition of an autonomic environmental safety device, according to an embodiment. Method  1500  may solve the need in the art for management of AESDs that can be functionally extracted from an environment upon the occurrence of an alarm condition. Method  1500  is one embodiment of determining whether or not an alarm condition is recoverable action  1404  in  FIG. 14  above. 
     Method  1500  may begin with determining  1302  if a battery of the AESD is emitting low amounts of power. If the battery is determined to be emitting low power, control can pass to evaluation block  1306  for further processing. 
     If the battery is determined to be not emitting low power, control can pass to determine  1304  if the AESD is operable. After determining  1304  if the AESD is operable, control can pass to evaluation block  1306  for further processing. 
     In action  1306 , a determination can be made of fault conditions determined in action  1302  and  1304  that can have an impact on the success of the mission or task which all entities may be 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. The 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 AESD is showing incorrect operation or low power. 
     Evaluation block  1306  may reference the battery level determined in action  1302  and the operability of the AESD determined in action  1304  to suggest a course of action that the AESDs should adopt, which in method  1500  is illustrated as being 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 fault conditions, or a combination of two or more of the identified fault conditions. For example, the stay awake signal could be withdrawn if there is low power at the AESD that could affect the safety of occupants of the building. In the alternative, the stay awake signal could be affirmed if there was only fault condition, or incorrect operation. After the evaluation is determined, control can pass to decision block  1502  for further processing in accordance with the decision made in evaluation block  1306 . 
     In action  1502 , if the desired control instruction is to maintain the stay awake signal, control can be passed to action  1408  for further processing. In the alternative, a withdrawal of the stay awake signal may be sent to action  1410  for further processing. It should be noted that generating a stay awake signal may be equivalent to generating a stay awake signal, 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 may be a managing entity since a managing entity can determine the outcome (life or death) of an entity. 
       FIG. 16  is a flowchart of a method  1600  of a ruling autonomic device controlling a stay-awake signal of an autonomic environmental safety device, according to an embodiment. Method  1600  may solve the need in the art for management of smoke detectors that can be functionally extracted from an environment upon the occurrence of a predetermined condition. 
     Method  1600  may begin by sending  1602  an ALice signal to an AESD to ascertain the AESD&#39;s potential for harm to a system. 
     Method  1600  also may include monitoring  1604  the response from the AESD. Monitoring  1604  as used herein may include maintaining regular surveillance, or close observation, over anonymous AESDs and can include the absence of a signal. For example, not responding within a timeout period is considered, as used herein, as monitoring the response. 
     Method  1600  also may include determining  1606  potential for causing harm to the autonomous system by the AESD. 
     In action  1608 , the system may control the AESD based on the potential for harm. This mimics the mechanism of cell death in the human (and animal) organisms, and hence implements autonomic and other biologically inspired metaphors. Method  1600  can send self-destruct signals to AESDs that can be compromised, or which cannot be identified as friendly or as having a right to access certain resources. The ALice signal can challenge an AESD to determine if the AESD is friendly and may have permission to access certain resources. If an AESD fails to identify itself appropriately following an ALice interrogation, the AESD may 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. 14 and 15  above. 
       FIG. 17  is a flowchart of a method  1700  of autonomic communication by an autonomic environmental safety device, according to an embodiment. Method  1700  may offer a holistic vision for the development and evolution of AESDs that brings new levels of automation and dependability to AESDs, while simultaneously hiding their complexity and reducing their total cost of ownership. 
     Method  1700  may include transmitting self health/urgency data  1702  of an AESD. Examples of the self health/urgency data may include information describing low battery power and/or failed sensors. Method  1700  also may include transmitting  1704  environment health/urgency data of the AESD. Examples of the environment health/urgency data may include information describing inaccessible devices, unauthorized access, and/or an unidentified AESD sending communication signals. 
     Transmitting  1702  and  1704  can be performed in any order relative to each other. For example, in one embodiment the transmitting  1702  self health/urgency data may be performed before transmitting  1704  environment health/urgency data. In another embodiment, transmitting  1704  environment health/urgency data may be performed before transmitting  1702  self health/urgency data. In yet another embodiment, the self health/urgency data may 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 may be encapsulating the environment health/urgency data and the self health/urgency data in a X.25 packet. 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 may be transmitted simultaneously. The X.25 standard is published by the ITU Telecommunication Standardization Sector at Place des Nations, CH-1211 Geneva 20, Switzerland. One of skill in the art will know that a variety of other types of packets fall with the bounds of the invention. 
       FIG. 18  is a flowchart of a method  1800  of autonomic communication by an autonomic environmental safety device. Method  1800  may include transmitting  1802  event message data of the AESD 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 below in  FIG. 31 . 
     In some embodiments, the self health/urgency data and environment health/urgency data may be encoded with the standard event messages on an autonomic communications channel, which may provide dynamics within autonomic responses and multiple loops of control, such as reflex reactions among an autonomic manager. 
       FIG. 19  is a flowchart of a method  1900  of autonomic communication by an autonomic environmental safety device, according to an embodiment. Method  1900  may include receiving  1902  the self health/urgency data from a self control loop component of the AESD. Method  1900  also may include receiving  1904  the environment health/urgency data from an environment control loop component of the AESD. 
     Method  1900  may further include transmitting self health/urgency data  1702  of an AESD. Examples of the self health/urgency data may include information describing low battery power and/or failed sensors. Method  1900  also may include transmitting  1704  environment health/urgency data of the AESD. Examples of the environment health/urgency data may include information describing inaccessible devices, unauthorized access, and/or an unidentified AESD sending communication signals. 
       FIG. 20  is a flowchart of a method  2000  of autonomic communication by an autonomic environmental safety device, according to an embodiment. Method  2000  may 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 system complexity and reducing processing delays by systems that receive data from the autonomic environmental safety device. 
     Method  2000  may include transmitting  2002  uncompressed self health/urgency data of the AESD. Method  2000  also may include transmitting  2004  uncompressed environment health/urgency data of the AESD. In the absence of bandwidth concerns, the uncompressed data can be acted upon quickly and will not incur processing delays. In some embodiments, the data, whether uncompressed or sent in some other form, may be in a form that can be acted upon immediately by the receiver and not involve processing delays (such as is the case of event correlation). Transmitting  2002  and  2004  can be performed in any order relative to each other. 
     In some embodiments, methods  200 - 2000  may be implemented as a computer data signal embodied in a carrier wave that represents a sequence of instructions which, when executed by a processor, such as processor  2204  in  FIG. 22 , cause the processor to perform the respective method. In other embodiments, methods  200 - 2000  may be implemented as a computer-accessible medium having executable instructions capable of directing a processor, such as processor  2204  in  FIG. 22 , to perform the respective method. In varying embodiments, the medium can be a magnetic medium, an electronic medium, or an optical medium. 
     Hardware and Operating Environment 
       FIGS. 21 ,  22 ,  23  and  24  are diagrams of hardware and operating environments in which different embodiments can be practiced. The description of  FIGS. 21 ,  22 ,  23  and  24  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 may 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 may be practiced and therefore these examples are given by way of illustration rather than limitation. 
       FIG. 21  is a block diagram of a computer cluster environment  2100  in which different embodiments can be practiced. Apparatus  2500 , as well as apparatus  2600 ,  2700 ,  2800 ,  2900 , and  3000 , and ESNS  3300  and  3400  can be implemented on computer cluster environment  2100 . 
     Computer cluster environment  2100  may include a network  2102 , such as an EtherFast 10/100 backbone, that can be operably coupled to a cluster server  2104  and a plurality of computers  2106 ,  2108 ,  2110  and  2112 . One possible embodiment of the computers may be computer  2202  described below with reference to  FIG. 22 . The plurality of computers can include any number of computers, but some implementations may include 8, 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  2100  may be a Beowolf computer cluster. The computer cluster environment  2100  may 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. 22  is a block diagram of a hardware and operating environment  2200  in which different embodiments can be practiced. Computer  2202  may include a processor  2204 , which may be a microprocessor, commercially available from Intel, Motorola, Cyrix and others. Computer  2202  may also include random-access memory (RAM)  2206 , read-only memory (ROM)  2208 , and one or more mass storage devices  2210 , and a system bus  2212 , that operatively couples various system components to the processing unit  2204 . The memory  2206 ,  2208 , and mass storage devices,  2210 , are illustrated as types of computer-accessible media. Mass storage devices  2210  may 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  2204  can execute computer programs stored on the computer-accessible media. 
     Computer  2202  may be communicatively connected to the Internet  2214  via a communication device  2216 . Internet  2214  connectivity is well known within the art. In one embodiment, a communication device  2216  may 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  2216  may 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., Ti line, etc.). 
     A user may enter commands and information into the computer  2202  through input devices such as a keyboard  2218  or a pointing device  2220 . The keyboard  2218  may permit entry of textual information into computer  2202 , as known within the art, and embodiments are not limited to any particular type of keyboard. Pointing device  2220  may 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  2220 . Such pointing devices may 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  2202  may be operatively coupled to a display device  2222 . Display device  2222  may be connected to the system bus  2212 . Display device  2222  can permit 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  2222 . Such display devices may 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 may typically include other peripheral input/output devices such as printers (not shown). Speakers  2224  and  2226  provide audio output of signals. Speakers  2224  and  2226  may also be connected to the system bus  2212 . 
     Computer  2202  may also include an operating system (not shown) that could be stored on the computer-accessible media RAM  2206 , ROM  2208 , and mass storage device  2210 , and may be executed by the processor  2204 . Examples of operating systems may 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  2202  are not limited to any type of computer  2202 . In varying embodiments, computer  2202  may 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  2202  may be operated using at least one operating system to provide a graphical user interface (GUI) including a user-controllable pointer. Computer  2202  may have at least one web browser application program executing within at least one operating system, to permit users of computer  2202  to access an intranet, extranet or Internet world-wide-web pages as addressed by Universal Resource Locator (URL) addresses. Examples of browser application programs can include Netscape Navigator® and Microsoft Internet Explore®. 
     The computer  2202  may operate in a networked environment using logical connections to one or more remote computers, such as remote computer  2228 . These logical connections may be achieved by a communication device coupled to, or a part of, the computer  2202 . Embodiments are not limited to a particular type of communications device. The remote computer  2228  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. 22  can include a local-area network (LAN)  2230  and a wide-area network (WAN)  2232 . 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  2202  and remote computer  2228  may be connected to the local network  2230  through network interfaces or adapters  2234 , which is one type of communications device  2216 . Remote computer  2228  may also include a network device  2236 . When used in a conventional WAN-networking environment, the computer  2202  and remote computer  2228  may communicate with a WAN  2232  through modems (not shown). The modem, which can be internal or external, is connected to the system bus  2212 . In a networked environment, program modules depicted relative to the computer  2202 , or portions thereof, can be stored in the remote computer  2228 . 
     Computer  2202  may also include power supply  2238 . Each power supply can be a battery. 
       FIG. 23  is a block diagram of a multiprocessor hardware and operating environment  2300  in which different embodiments can be practiced. Computer  2302  may include a plurality of CPUs or microprocessors, such as microprocessor  2204 ,  2304 ,  2306 , and  2308 . The four microprocessors of computer  2302  may be one example of a multi-processor hardware and operating environment; other numbers of microprocessors may be used in other embodiments. 
     Similar to the computer cluster environment  2100  in  FIG. 21  above, the computer  2302  may 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. 24  is a block diagram of a hardware and operating environment  2400  which may include a quiesce component, according to an embodiment. The hardware and operating environment  2400  may solve the need in the art to reduce the possibility that an autonomic element will jeopardize the mission of the autonomic unit. 
     A quiesce component  2402  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  2402  may 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  2402  may reduce the possibility that an autonomic element will jeopardize the mission of the autonomic element by deactivating or inactivating the autonomic element. 
     Quiesce time may be defined as the length of time taken to quiesce a system (to render it inactive), or the length of time between periods of activity (i.e. the length of time of inactivity). The quiescing may be somewhat analogous to the cell lifecycle, were cells may stop dividing and go into a quiescent state. 
     Components of system  100  and apparatus  2500 ,  2600 ,  2700 ,  2800 ,  2900 ,  3000 ,  3100 ,  3200 ,  3300 ,  3400 ,  3500 ,  3600 ,  3700 ,  3800 ,  3900 ,  4000  and  4100 , and methods  200 ,  300 ,  400 ,  500 ,  600 ,  700 ,  800 ,  900 ,  1000 ,  1100 ,  1200 ,  1300 ,  1400 ,  1500 ,  1600 ,  1700 ,  1800 ,  1900  and  2000  can be embodied as computer hardware circuitry or as a computer-readable program, or a combination of both. 
     More specifically, in the 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 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 can execute on as few as one computer as in computer  2202  in  FIG. 22 , or on at least as many computers as there are components. 
     More specifically, in the 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 may execute on as few as one computer as in computer  2202  in  FIG. 22 , or on at least as many computers as there are components. 
     Apparatus Embodiments 
     In the previous section, some embodiments of operating environments are described. In this section, particular apparatus of such embodiments 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, executing the instructions from computer-readable media. 
       FIG. 25  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. Apparatus  2500  may include a first plurality of neural basis functions (NBFs)  2502  and  2504 . NBFs are the fundamental building block of apparatus  2500 . In some embodiments of apparatus  2500 , the plurality of NBFs may include more than the two NBFs  2502  and  2504  shown in  FIG. 25 . In some embodiments, apparatus  2500  may include only one NBF. One embodiment of a NBF is described below with reference to  FIG. 26 . 
     Apparatus  2500  may also include a first inter-evolvable neural interface (ENI)  2506  that is operably coupled to each of the of neural basis functions  2502  and  2504 . The NBFs  2502  and  2504  may be highly integrated, and coupling between the NBFs through the ENI  2506  may provide a three dimensional complexity. Thus, for example, when apparatus  2500  is implemented on microprocessors, such as microprocessor  2204  described above with reference to  FIG. 22 , apparatus  2500  may 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  2506  may be known as an inter-NBF ENI because the inter-ENI  2506  is illustrated as being between or among the NBFs  2502  and  2504  at the same level within a hierarchy. Apparatus  2500  shows only one level  2508  of a hierarchy, although one skilled in the art will recognize that multiple hierarchies may be used within the scope of this invention. 
     Apparatus  2500  may also operate autonomously. A system operates autonomously when it 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  2506  may adapt apparatus  2500  by instantiating new NBFs and ENIs and establishing operable communication paths  2510  to the new NBFs and the ENIs to apparatus  2500 . ENI  2506  may also adapt apparatus  2500  by removing or disabling the operable communication paths  2510  to the new NBFs and ENIs. The adapting, establishing, removing and disabling of the communication paths  2510  may be performed autonomously. Thus, apparatus  2500  may satisfy the need for a synthetic neural system that performs significant tasks with complete autonomy. 
     Apparatus  2500  may be capable of establishing and removing links to other similarly configured systems (not shown). Thus, the apparatus  2500  may be described as self-similar. 
     While the apparatus  2500  is not limited to any particular NBF or ENI, for sake of clarity simplified NBFs and a simplified ENI are described. 
       FIG. 26  is a block diagram of a neural basis function (NBF)  2600  of a worker according to an embodiment. NBF  2600  is illustrated as a bi-level neural system because both high-level functions and low-level functions are performed by NBF  2600 . 
     NBF  2600  may include an intra-evolvable neural interface (intra-ENI)  2602 . The ENI  2602  may be operably coupled to a heuristic neural system (HNS)  2604  and operably coupled to an autonomous neural system (ANS)  2606 . The HNS  2604  may perform high-level functions and the ANS  2606  performs low-level functions that are often described as “motor functions” such as “motor”  4010  in  FIG. 40  below. In NBF  2600 , the HNS  2604  and the ANS  2606  in aggregate may provide a function of a biological neural system. The intra-ENI  2602  shown in  FIG. 26  is an ENI that is wholly contained within an NBF, and is therefore prefixed with “intra.” 
     The intra-ENI  2602  may send action messages to and receive request messages from the HNS  2604  and the ANS  2606  during learning and task execution cycles, as well as during interfacing operations between the intra-ENI and the HNS  2604  and the ANS  2606  when the HNS  2604  and the ANS  2606  need to be modified as a result of other system failures or modification of objectives. NBF  2600  is illustrated as a worker NBF because this NBF performs functions, but does not provide instructions commands to other NBFs. 
       FIG. 27  is a block diagram of a heuristic neural system  2700 , according to an embodiment. 
     The heuristic neural system (HNS)  2700  may be composed of a neural net  2702  for pattern recognition and a fuzzy logic package  2704  to perform decisions based on recognitions. Taken together the neural net  2702  and the fuzzy logic package  2704  may form a basis for a higher level heuristic intelligence. 
       FIG. 28  is a block diagram of an autonomous neural system  2800  according to an embodiment. 
     The autonomous neural system (ANS)  2800  may include a non-linear dynamics simulation  2802  that represents smart servo system behavior. 
       FIG. 29  is a block diagram of a neural basis function (NBF)  2900  of a worker, according to an embodiment. NBF  2900  is shown as a bi-level neural system. 
     In some embodiments, NBF  2900  may include a self assessment loop (SAL)  2902  at each interface between autonomic components. Each SAL  2902  may continuously gauge efficiency of operations of the combined HNS  2604  and ANS  2606 . The standards and criteria of the efficiency may be set or defined by objectives of the NBF  2900 . 
     In some embodiments, NBF  2900  may also include genetic algorithms (GA)  2904  at each interface between autonomic components. The GAs  2904  may modify the intra-ENI  2602  to satisfy requirements of the SALs  2902  during learning, task execution or impairment of other subsystems. 
     Similarly, the HNS  2604  may have a SAL  2902  interface and a GA  2904  interface to a core heuristic genetic code (CHGC)  2906 , and the ANS  2606  may have a SAL  2902  interface and a GA  2904  interface to a core autonomic genetic code (CAGC)  2908 . The CHGC  2906  and CAGC  2908  may allow modifications to a worker functionality in response to new objectives or injury. The CHGC  2906  and the CAGC  2908  autonomic elements may not be part of an operational neural system, but rather may store architectural constraints on the operating neural system for both parts of the bi-level system. The CHGC  2906  and the CAGC  2908  may both be modifiable depending on variations in sensory inputs via GAs  2904 . 
     In some embodiments, the CHGC  2906  and the CAGC  2908  in conjunction with SALs  2902  and GAs  2904  may 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  2900  may provide a form of evolution possible only over generations of NBF workers. 
     In some embodiments, NBF  2900  may also include genetic algorithms  2910  and  2912  that provide process information to the CHGC  2906  and the CAGC  2908 , respectively. HNS  2604  and ANS  2606  may receive sensory input  2914  and  2916 , respectively, process the sensory input and generate high level actions  2918  and low level actions  2920 , respectively. 
       FIG. 30  is a block diagram of a multiple level hierarchical evolvable synthetic neural system (ESNS)  3000 , according to an embodiment. 
     The multiple level hierarchical ESNS  3000  may include a first level of hierarchy  3002  that includes a NBF  3004  and inter-ENI  3006  and a ruler NBF  3008 . A ruler NBF, such as ruler NBF  3008  may perform functions and also provide instructions commands to other subordinate NBFs. 
     The ruler NBF  3008  of the first hierarchical level  3002  is illustrated as being operably coupled to a ruler NBF  3010  in a second hierarchical level  3012 . Ruler NBF  3010  may perform functions, receive instructions and commands from other ruler NBFs that are higher in the hierarchy of the ESNS  3000  and also provide instructions commands to other subordinate NBFs. 
     The second hierarchical level  3012  may also include an inter-ENI  3014 . The second hierarchical level  3012  of  FIG. 30  shows the embodiment of an ESNS  3000  having one NBF operably coupled to an ENI. The ruler NBF  3010  of the second hierarchical level  3012  may be operably coupled to a ruler NBF  3016  in a third hierarchical level  3018 . 
     The third hierarchical level  3016  may also include an inter-ENI  3020 . The third hierarchical level  3016  of  FIG. 30  shows the embodiment of an ESNS  3000  having more than two NBFs (e.g.  3016 ,  3022  and  3024 ) operably coupled to an ENI. 
     In some embodiments, the NBFs  3004 ,  3008 ,  3010 ,  3016 ,  3022  and  3024  may include the aspects of NBFs  2502  and  2504  in  FIG. 25  above, and/or NBF  2600  in  FIG. 26  above. One skilled in the art will appreciate that many combinations exist that fall within the purview of this invention. 
       FIG. 31  is a block diagram of a plurality of autonomic entities including autonomic environmental safety devices assembled to perform a task, according to an embodiment. As a general rule these entities may 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 environmental safety devices  3118  and  3120 , autonomic computing may have a self-aware layer and an environment aware layer. The self-aware layer of the autonomic entity (agent or other) may 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 may allow the autonomic entities to be self aware and to self manage. A similar scheme may facilitate environment awareness—allowing self management if necessary, but without the immediate control to change the environment; control may be affected through communication with other autonomic managers that have the relevant influence, through reflex or event messages. 
     The autonomic entities may be arranged or assigned distinctive roles such as worker entities (e.g. autonomic environmental safety device), 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. Furthermore, the communication between the ruler and the worker may be facilitated through the message entity. The message entity may have the additional task of communicating with a remote system. In the case 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 environmental safety devices. It is foreseeable that the remote system could be an autonomic entity acting like the project manager for the facility or building. Communication with a ruler entity may be limited to the download of science data and status information. An example of such a grouping is shown in  FIG. 31  where autonomic entity  3102  is shown as a ruler entity, autonomic entity  3110  is a message entity, and autonomic environmental safety devices  3118  and  3120  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 benefit to having identical hardware can be replacing failed entities, which can be accomplished by activating software code found in the autonomic entity. If hardware differences exist, the differences can be based on specialized equipment suitable for a particular task. However, in some embodiments, certain functions or roles, such as ruler and messenger, may be expected to be within the skill set of all the autonomic entities. In some embodiments, autonomic entity  3110  may be an AESD. 
     As shown in  FIG. 31 , ruler autonomic entity  3102  may comprise a program or process executing in ruler entity  3102 . Ruler entity  3102  can be implemented using a data processing system, 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 stationary factory ship for exploring planets, asteroids, or comets. Further, analysis module  3106  or agent, as executed by ruler entity  3102 , can be used to monitor process  3104  and to receive pulse monitor and heart beat monitor signals from worker entities through the messenger entity. When analysis module  3106  is used to monitor process  3104 , it may be to detect errors or problems with the operation of process  3104 . In some embodiments, ruler autonomic entity  3102  may be an AESD. 
     As shown in  FIG. 31 , analysis environmental sensor  3106  can include an evaluator or other monitoring engine used to monitor the operation of process  3104 . Analysis environmental sensor  3106  may be executed in response to some event. This event can be a periodic event, such as the passage of some period of time, or data received from one or more of the worker entities. Further, the event can be the initialization of internal procedures in process  3104  or the starting or restarting of ruler entity  3102 . Depending on the particular implementation, analysis environmental sensor  3106  can continuously run in the background monitoring process  3104  and analyzing the worker entity signals. See method  1200  in  FIG. 12  above for actions taken by analysis agent module  3106  in formulating a strategy for the worker entities. Further, analysis agent  3104  may be subject to any self-healing routines found in ruler entity  3102 . 
     This monitoring by analysis agent  3106  may be based on rules stored in behavior storage  3108 , which can be used to compare the actual behavior of the received data to an expected behavior as defined in behavior storage  3108 . In the some embodiments, behavior storage  3108  (ruler entity  3102 ) 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  3108  can be narrowly tailored based on the use and purpose of the autonomic entity, such as messenger entity  3110  and have only those procedures needed to perform its programming. 
     When messenger entity connects to a remote computer at a command and control station, database  3116  can be updated with information that can later be used to program ruler entity  3102  or messenger entity  3110 . In some embodiments, a copy of the rules in database  3116  may contain 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 attempts to contact message entity  3110  to see if more recent or up-to-date information is available. If updates are available, these updates may be sent to the requesting entity for processing. 
     The information in behavior storage  3108  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 may 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 may be data gathering, processing of information, controlling machinery, or any other operation where data processing systems may be employed. These expected values may be compared to determine if an error condition may have occurred in the operation of the entity. An error condition may be analyzed to determine its causes and possible correction. In the case of a worker entity, the error may 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  3106  using the rules in behavior storage  3108 . Based on the analysis, the ruler entity can elect to either withdrawal 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 can be disconnected from the operation and the task be assigned to another entity or partially performed by the remaining entity to insure its completion. 
       FIG. 32  is a block diagram of an autonomous entity management system  3200 , according to an embodiment. The system  3200  may be a generalized system because the system can represent a myriad of devices, processes, or device and process that perform a task in accordance to its programming or design. The system can represent a situation in which an autonomous system  3204  encounters an anonymous autonomic environmental sensor  3202 . An anonymous autonomous agent may be a visiting agent, a mobile agent that can enter the sphere of influence of the autonomous system  3204 , or any device for which the autonomous system  3204  may have no established relationship. Example encounters may be wireless device (agent) and communication tower (system), a client and server, a video subscriber and video provider, a process and an operating system, and so forth. System  3200  may solve the need in the art for management of 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  3204  may comprise one or more autonomic environmental safety devices  3208 ,  3210 , and  3212 , all performing assigned functions and roles. As noted earlier, roles can be a combination of ruler, messenger, and worker. Functions may include data gathering, communication functions, scheduling, controlling, security, and so forth. Upon detecting the presence of the anonymous autonomic environmental sensor  3202 , the assigned autonomous agent for performing security functions for autonomous system  3204  may interrogate the anonymous autonomic environmental sensor  3202 , requesting production of valid credentials. It should be noted at this point that detection can occur by employing various schemes such as when the anonymous autonomic environmental sensor  3202  requests resources from the system  3204  or from any autonomic entity that forms part of the system, response to polling signals from the autonomous system  3204 , or through a friend or foe signal that indicates the presence of an anonymous entity  3202  in proximity to the autonomous system  3204 . 
     To the autonomous system  3204  security may 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, by way of example, damage, denial-of-service, breach-of-privacy, harassment, social engineering, event-triggered attacks, and compound attacks. To prevent security breaches it may be important to ensure that visiting agents 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, an asynchronous ALice signal (Autonomic license)  3206  may be sent to the visiting environmental sensor  3202  requiring valid credentials from the environmental sensor  3202 . The anonymous environmental sensor  3202  may need to work within the autonomic system  3204  to facilitate self-management, and as such the anonymous environmental sensor  3202  and its host may need to be able to identify each other&#39;s credentials through such as an ALice signal. The autonomic system  3204  can establish certain response characteristics for the returned signal from the environmental sensor  3202 . For example, the autonomic system  3204  can require a response in an appropriate format, within a certain timeout period, and with a valid and justified reason for being within the locus of interest or domain of the autonomous system  3204 . For protection the autonomic system  3204  can make an assessment of the quality of the response from the anonymous environmental sensor  3202  to ascertain the potential of the agent for causing harm to the autonomous system  3204 . Based on this determination, the autonomous system  3204  can control the type of interaction with the environmental sensor  3202 . The agent can be destroyed, blocked, partially blocked, stay alive signal withdrawn, or allowed to communicate with other agents within the autonomous system  3204 . The protection can be triggered at any level of infraction or by a combination of infractions by the anonymous autonomous environmental sensor  3202  when responding to the ALice signal. Failure to identify itself appropriately, following an ALice interrogation, it can be blocked from the system and given either a self-destruct signal, or its “stay alive” reprieve can be withdrawn. The consequence of an unacceptable response to an anonymous environmental sensor  3202 , should it fail to respond within a timeout period, may be that the environmental sensor  3202  is deemed to be an intruder or other invalid agent (process) and consequently may be destroyed and/or excluded from communicating with other autonomic environmental safety devices  3208 ,  3210 ,  3212  in the system. 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. 14 and 15  above. 
     Referring to  FIG. 33 , a particular three-dimensional implementation is described in conjunction with  FIG. 25  and the apparatus described in  FIG. 30 . 
       FIG. 33  is a diagram of a three dimensional (3D) hierarchical evolvable synthetic neural system (ESNS)  3300 , according to an embodiment. 
     Some embodiments of the 3D hierarchical ESNS  3300  may include a ruler subsystem  3302  and four worker subsystems  3304 ,  3306 ,  3308  and  3310 . Each subsystem in the 3D hierarchical ESNS  3300  may include one or more ESNSs such as system  2500  or ESNS  3000 . 
     The three dimensional architecture of 3D hierarchical ESNS  3300  may provide a three dimensional complexity. An implementation of ESNS  3000  on a microprocessor, such as microprocessor  2204 , described above with reference to  FIG. 22 , may provide a synthetic neural system that reconciles the two dimensional nature of microprocessor technologies to the three dimensional nature of biological neural systems. 
     Referring to  FIG. 34 , a particular three-dimensional implementation for asteroid prospecting is described in conjunction with the system overview in  FIG. 25  and the apparatus described in  FIG. 30 . 
       FIG. 34  is a diagram of a heuristic neural system (HNS)  3400 , according to an embodiment, for a single instrument spacecraft to prospect asteroid belts. 
     Each spacecraft may be controlled by a subset of NBFs (SNBF) which in aggregate may provide the behavior of a subsystem of the mission. For example, a solar sailing SNBF  3402  may control sail deployment and subsequent configuration activity much as terrestrial sailors perform navigation and manage propulsion. A spacecraft inter communication subsystem SNBF  3404  may control communication with other workers and rulers. Also, a housekeeping SNBF  3406  may control the spacecraft housekeeping. HNS  3400  may also include a ruler subsystem  3408  to coordinate all activities. Similarly, a spacecraft navigation and propulsion subsystem  3410  may control the navigation and propulsion, matching the navigation and propulsion to the current objectives. 
     In at least one embodiment, each spacecraft could be a worker in a totally autonomous space mission. The space mission may be configured as an autonomous nanotechnology swarm (ANTS). Each spacecraft in an ANTS may be assigned a specialized mission, much like ants in an ant colony have a specialized mission. Yet, the HNS architecture of each worker in an ANTS may provide 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, the SNBFs within HNS  3400  may 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 may coordinate all of the SNBFs to assure that spacecraft objectives are met. Additionally, to have redundancy for the mission, inactive workers and rulers may only participate if a member of their type is lost. In addition, a hierarchical worker node can collapse to a non-hierarchical one, if all of the available sub-rulers for that node are lost. 
     In one particular application of an ANTS, a prospecting asteroid mission (PAM) may survey a large population or surface area targets, such as main-belt asteroids. The primary objective of a PAM could be exploration of the asteroid belt in search of resources and material with astrobiologically relevant origins and signatures. The PAM may include a swarm of approximately 1000 spacecraft that includes approximately 10 types of specialist workers (e.g. HNS  3400 ) with a common spacecraft bus that is organized into 10 subswarms of approximately 100 spacecraft each, having approximately 10 specialist HNSs. 
     In some embodiments, each individual spacecraft in a PAM may weigh 1 kilogram or less with a one meter diameter body and 100 meter 2  sails when fully deployed. Each spacecraft may be packaged into a 10 cm 2  sided cube. A swarm of 1000 of these spacecraft may fit into 1 meter 3  weighing 1000 kilograms in deployment. Each spacecraft may also include a solar sail propulsion system that requires no expendable supplies and a small nuclear battery that provides sufficient power to each worker. Thus, the prospecting asteroid mission may be self-directed and can possibly be self-sustaining for tens of years. 
       FIG. 35  is a block diagram of an autonomic entity management system  3500 , according to an embodiment. The system  3504  may be a generic system because it can represent a myriad of devices, processes, or device and process that perform a task in accordance to its programming or design. The system  3504  could be software for updating or for synchronizing a file, could be a worker craft containing unique instruments for data gathering in an autonomous nano-technology swarm, or the system  3504  could be an application for managing resources in networks or general purpose computers. The system  3504  can have multiple applications and capabilities such as self healing and self monitoring, and in some embodiments, the system  3504  may be required to have a way for communicating a functional status and operating state signal to the managing autonomous entity manager  3502 . The system  3504  may exhibit the properties of being self managing and self governing, often termed autonomic, pervasive, sustainable, ubiquitous, biologically inspired, organic or with similar such terms. 
     While the autonomic entity management system  3500  is shown as discrete autonomic components it should be understood that autonomic computing is dependent on many disciplines for its success; not least of these is research in agent technologies. The autonomic manager  3502  or system  3504  can be agents themselves containing functionality for measurement and event correlation and support for policy based control. 
     The functional status may be represented by a heart beat monitor (HBM) signal that indicates that the system  3504  is still functioning as designed. The HBM signal is fundamentally an “I am alive” signal to the autonomic manager, such as autonomic entity  3502 , indicating that the system is functioning. These signals can be communicated from system  3504  through an appropriate bi-directional communication link  3506 . The response from the autonomous manager  3502  can use the same link to influence system  3504 . The communication link can be one or more radio link, data bus, a call procedure when implemented as software, or any other link presently existing or to be developed for facilitating communication between autonomic elements. 
     The operating state signal may be represented by a pulse monitor (PBM) signal. The PBM signal can be used by the autonomic entity  3502  or autonomic manager to infer potential warning conditions so preparations can be made to handle changing processing loads, impact on mission objectives, planning for correction, and possible substitute or reassignment of role or functionality to perform the desired objectives of system  3504 . Autonomous manager  3502  has the additional option of generating a stay alive signal that can be used to safeguard resources, safeguard the completion of the objectives, and safeguard the system  3504  by removing the offending agent or the sub-component of system  3504 . The stay alive signal borrows from the process of apoptosis in biological systems for cell self-destruction to maintain growth and protect the biological system from catastrophe. In biological systems, self-destruct is an intrinsic property that is delayed due to the continuous receipt of biochemical reprieves. The process is referred to as apoptosis, meaning drop out due to the origin of the word derived from dropping of leaves from trees; i.e., loss of cells that ought to die in the midst of the living structure. This process has also been nicknamed death by default, where cells are prevented from putting an end to themselves due to constant receipt of biochemical stay alive signals. In the present arrangement, self-destruction is usable in preventing race conditions and undesirable emergent behavior that have been shown to influence system performance and thus mission objectives. While self-destruction can be viewed as a last resort situation to prevent further damage, in other situations, such as security of the agent or system  3504 , self-destruction can be used as an intrinsic part of the process, such as blocking the autonomic entity from communicating or using the resources of the system. 
       FIG. 36  is a diagram representation of a plurality of autonomic entities that have been assembled to perform a task. These entities may 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  3618  and  3620 , autonomic computing may have a self-aware layer and an environment aware layer. The self-aware layer of the autonomic entity (agent or other) may 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 may allow the autonomic entities to be self aware and to self manage. A similar scheme may 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 may 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 may be facilitated through the message entity. The message entity could have the additional task of communicating with a remote system. In the case 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. It is foreseeable that the remote system could be an autonomic entity acting like the project manager for the mission. Communication with mission control may be limited to the download of science data and status information. An example of such a grouping is shown in  FIG. 36  where autonomic entity  3602  is shown as a ruler entity, autonomic entity  3610  as a message entity, and autonomic entities  3618  and  3620  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 may be 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, may be expected to be within the skill set of all the autonomic entities. 
     As shown in  FIG. 36 , ruler autonomic entity  3602  may comprise a program or process  3604  executing in ruler entity  3602 . Ruler entity  3602  can be implemented using a data processing system, such as data processing system  2302  in  FIG. 23 , 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 may be launched from a factory ship for exploring planets, asteroids, or comets. Further, analysis module  3606  or agent as executed by ruler entity  3602  can be used to monitor process  3604  and to receive pulse monitor and heart beat monitor signals from worker entities through the messenger entity. When analysis module  3606  is used to monitor process  3604 , it may be to detect errors or problems with the operation of process  3604 . 
     As shown in  FIG. 36 , analysis agent  3606  can include an evaluator or other monitoring engine used to monitor the operation of process  3604 . Analysis agent  3606  may be executed in response to some event. This event can be a periodic event, such as the passage of some period of time or data received from one or more of the worker entities. Further, the event can be the initialization of internal procedures in process  3604  or the starting or restarting of ruler entity  3602 . Depending on the particular implementation, analysis agent  3606  can continuously run in the background monitoring process  3604  and analyzing the worker entity signals. See method  300  in  FIG. 3  above for actions taken by analysis agent module  3606  in formulating a strategy for the worker entities. Further, analysis agent  3606  may be subject to any self-healing routines found in ruler entity  3602 . 
     This monitoring by analysis agent  3606  may be based on rules stored in behavior storage  3608 , which could be used to compare the actual behavior of the received data to an expected behavior as defined in behavior storage  3608 . In the present arrangement, behavior storage  3608  (ruler entity  3602 ) may 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  3608  can be narrowly tailored based on the use and purpose of the autonomic entity, such as messenger entity  3610  and have only those procedures needed to perform its programming. 
     When messenger entity connects to remote computer at a command and control station, database  3616  can be updated with information that can later be used to program ruler entity or worker entity. In some embodiments, a copy of the rules in database  3616  may contain 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 may attempt to contact message entity  3610  to see if more recent or up-to-date information is available. If updates are available, these updates may be sent to the requesting entity for processing. 
     The information in behavior storage  3608  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 may 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 of operations may 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  3606  using the rules in behavior storage  3608 . 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 assigned to another entity or partially performed by the remaining entity to insure its completion. 
       FIG. 37  is a block diagram of an autonomous entity management system  3700  according to an embodiment. The system  3700  may be a generic system because it represents a myriad of devices, processes, or device and process that perform a task in accordance to its programming or design. The illustrated system  3700  represents an instance when an autonomous system  3704  encounters an anonymous autonomic agent  3702 . An anonymous autonomous agent can be a visiting agent, a mobile agent that can enter the sphere of influence of the autonomous system  3704 , or any device for which the autonomous system  3704  has no established relationship. Example encounters may 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  3700  may solve the need in the art for management of 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  3704  may comprise one or more autonomic agents  3708 ,  3710 , and  3712 , all performing assigned functions and roles. As noted earlier, roles can be a combination of ruler, messenger, and worker. Functions may be data gathering, communication functions, scheduling, controlling, security, and so forth. Upon detecting anonymous autonomic agent  3702 , the assigned autonomous agent for performing security functions for autonomous system  3704  may interrogate the anonymous autonomic agent  3702 , requesting production of valid credentials. It should be noted at this point that detection can occur by employing various schemes such as when the anonymous autonomic agent  3702  requests resources from the system  3704  or from any autonomic entity that forms part of the system, response to polling signals from the autonomous system  3704 , or through a friend or foe signal that indicates the presence of an anonymous entity  3702  in proximity to the autonomous system  3704 . 
     To the autonomous system  3704 , security may 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 may be damage, denial-of-service, breach-of-privacy, harassment, social engineering, event-triggered attacks, or compound attacks. To prevent security breaches, it may be important to ensure that visiting agents 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  3702  may be sent an asynchronous ALice signal (Autonomic license)  3706  requiring valid credentials from the agent  3702 . The anonymous agent  3702  may need to work within the autonomic system  3704  to facilitate self-management, and as such the anonymous agent  3702  and its host may need to be able to identify each other&#39;s credentials through such as an ALice signal. The autonomic system  3704  can establish certain response characteristics for the returned signal from the agent  3702 . For example, the autonomic system  3704  can require a response in an appropriate format, within a certain timeout period, and with a valid and justified reason for being within the locus of interest or domain of the autonomous system  3704 . For protection, the autonomic system  3704  may make an assessment of the quality of the response from the anonymous agent  3702  to ascertain the potential of the agent for causing harm to the autonomous system  3704 . Based on this determination the autonomous system  3704  can control the type of interaction with the agent  3702 . The agent can be destroyed, blocked, partially blocked, stay alive signal withdrawn, or allowed to communicate with other agents within the autonomous system  3704 . The protection can be triggered at any level of infraction or by a combination of infractions by the anonymous autonomous agent  3702  when responding to the ALice signal. If the agent  3702  fails to identify itself appropriately following an ALice interrogation, the agent  3702  may be blocked from the system and given either a self-destruct signal, or its “stay alive” reprieve is withdrawn. The consequence of an unacceptable response by an anonymous agent  3702 , should it fail to do so within a timeout period, may be that the agent  3702  may be determined to be an intruder or other invalid agent (process) and consequently it is destroyed and/or excluded from communicating with other agents  3708 ,  3710 ,  3712  in the system. 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. 5 ,  6  and  24 . 
       FIG. 38  is a hierarchical chart of an autonomous entity management system  3800 , according to an embodiment. Properties that a system may possess in order to constitute an autonomic system are depicted in the autonomous entity management system  3800 . 
     General properties of an autonomic (self-managing) system may include four objectives defined by International Business Machines  3802 : self-configuring  3804 , self-healing  3806 , self-optimizing  3808  and self-protecting  3810 , and four attributes  3812 : self-awareness  3814 , environment-awareness  3816 , self-monitoring  3818  and self-adjusting  3820 . One skilled in the art will recognize that other properties also exist, such as self-quiescing  3825 . Essentially, the objectives  3802  could represent broad system requirements, while the attributes  3812  identify basic implementation mechanisms. 
     Self-configuring  3804  may represent an ability of the system  3800  to re-adjust itself automatically; this can simply be in support of changing circumstances, or to assist in self-healing  3806 , self-optimization  3808  or self-protection  3810 . Self-healing  3806 , 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  3806  objective may monitor vital signs in an attempt to predict and avoid “health” problems (i.e. reaching undesirable situations). 
     Self-optimization  3808  may mean that the system  3800  is aware of ideal performance of the system  3800 , can measure current performance of the system  3800  against that ideal, and has defined policies for attempting improvements. The system  3800  can also react to policy changes within the system as indicated by the users. A self-protecting  3810  system  3800  can defend the system  3800  from accidental or malicious external attack, which necessitates awareness of potential threats and a way of handling those threats. 
     Self-managing objectives  3802  may require awareness of an internal state of the system  3800  (i.e. self-aware  3814 ) and current external operating conditions (i.e. environment-aware  3816 ). Changing circumstances can be detected through self-monitoring and adaptations are made accordingly (i.e. self-adjusting  3820 ). Thus, system  3800  may 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 of therein can be adjusted. Such ability to operate in a heterogeneous environment may 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 may 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-*  3822  may 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  3804 , Self-Healing  3806 , Self-Optimisation  3808  and Self-Protection  3810 ). 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. 39  is a block diagram of an autonomic element  3900 , according to an embodiment. Autonomic element  3900  may include an element  3902  that is operably coupled to sensors and  3904  and effectors  3906 . 
     Autonomic element  3900  may also include components that monitor  3908 , execute  3910 , analyze  3912  and plan  3914 ; those components may access knowledge  3916 . Those components can interact with sensors  3918  and effectors  3920 . 
       FIG. 40  is a block diagram of autonomy and autonomicity  4000  at a high system level, according to an embodiment. A high level perspective for an intelligent machine design is depicted in  FIG. 40 . This diagram of autonomy and autonomicity  4000  includes intelligent machine design and system level autonomy and autonomicity. 
       FIG. 40  describes three levels for the design of intelligent systems: 
     1) Reaction  4002 —the lowest level, where no learning occurs but there is immediate response to state information coming from sensory systems  4004 . 
     2) Routine  4006 —middle level, where largely routine evaluation and planning behaviors take place. Input is received from sensory system  4004  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  4008 —top level, receives no sensory  4004  input or has no motor  4010  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, and so forth. 
     As illustrated, input from, and output to, the environment may only take place within the reaction  4002  and routine  4006  layers. One can consider that reaction  4002  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  4002  level can reside within an artificial domain utilizing its techniques to consider the behavior of the system and learn new strategies. The routine  4006  level can be a cooperative mixture of both. The high-level intelligent machine design may be appropriate for autonomic systems as depicted here in  FIG. 40 , in consideration of the dynamics of responses including reaction  4002  and also for reflection  4008  of self-managing behavior. 
     As depicted autonomic computing can reside within the domain of the reaction  4002  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.) may provide such higher level cognitive approaches for instance as in swarm intelligence. Within the autonomic computing research community, autonomicity may not 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 may 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 element&#39;s self-directed task. From this perspective, it would appear that the 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  4002 , routine  4006 , and reflection  4008 ) and accepts the narrower view of autonomicity, there is a potential correlation between the levels. That is, the reaction  4002  level correlates with autonomicity, and the reflection  4008  level correlates with autonomy; autonomy as in self-governing of the self-managing policies within the system. 
       FIG. 41  is a block diagram of an architecture of an autonomic element (AE)  4100  according to an embodiment that includes reflection and reflex layers. The autonomic element  4100  may include a managed component (MC)  4102  that is managed, and the autonomic element  4100  may further include an autonomic manager (AM), not shown. The AM may be responsible for the MC  4102  within the AE  4100 . 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  4100  can occur with remote (external) autonomic managers (cf. the autonomic communications channel  4106 ) 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. 39 . A control loop  4108  can be created by monitoring  4110  behavior through sensors, comparing this with expectations (knowledge  3916 , as in historical and current data, rules and beliefs), planning  4112  what action is necessary (if any), and then executing that action through effectors. The closed loop of feedback control  4108  can provide a basic backbone structure for each system component.  FIG. 41  describes at least two control loops in the autonomic element  4100 , one for self-awareness  4114  and another  4108  for environmental awareness. 
     In some embodiments, the self-monitor/self-adjuster control loop  4114  can be substantially similar to the monitor, analyze, plan and execute (MAPE) control loop described in  FIG. 39 . The monitor-and-analyze parts of the structure can perform a function of processing information from the sensors to provide both self-awareness  4114  and an awareness  4108  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 it through the knowledge repository  3916  within the AM  4104 . 
     A reflection component  4116  may perform analysis computation on the AE  4100  (cf. the reflection component  4116  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 may be important since autonomicity involves self-adaptation to the changing circumstances in the environment. An autonomic manager communications (AM/AM) component  4118  can also produce a reflex signal  4120 . A self adjuster  4122  can be operably coupled to a self-monitor  4124  in the self control loop  4114 . 
     CONCLUSION 
     The systems, methods and apparatus disclosed herein provide autonomic environmental safety devices, such as smoke detectors, that may be associated in a heuristic neural system (HNS) architecture in which a heart beat monitor signal and a reflex signal may be exchanged between devices in the HNS so that stay-alive signals and stay-awake signals to the devices can be controlled in ways that provide more reliable alerts of environmental danger. 
     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 may 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 made in an object-oriented design environment or any other design environment that may provide the required relationships. 
     In particular, one of skill in the art will readily appreciate that the names of the methods and apparatus may be 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 environmental safety devices. 
     The terminology used in this application is meant to include all environments and alternate technologies which provide the same functionality as described herein.