Patent Publication Number: US-7899760-B2

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

Description:
RELATED APPLICATIONS 
     This application claims the benefit of U.S. Provisional Application Ser. No. 60/811,149 filed May 15, 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. 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), and which 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 
     A synthetic neural system is an information processing paradigm that is inspired by the way biological nervous systems, such as the brain, process information. Biological systems inspire system design in many other ways as well, for example reflex reaction and health signs, nature inspired systems (NIS), hive and swarm behavior, and fire flies. These synthetic systems provide an autonomic computing entity that can be arranged to manage complexity, continuous self-adjustment, adjustment to unpredictable conditions, and prevention and recovery for failures. 
     One key element is the general architecture of the synthetic neural system. A synthetic neural system is composed of a large number of highly interconnected processing autonomic elements that are analogous to neurons in a brain working in parallel to solve specific problems. Unlike general purpose brains, a synthetic neural system is typically configured for a specific application and sometimes for a limited duration. 
     In one application of autonomic elements, each of a number of spacecrafts could be a worker in an autonomous space mission. The space mission can be configured as an autonomous nanotechnology swarm (ANTS). Each spacecraft in an ANTS has a specialized mission, much like ants in an ant colony have a specialized mission. Yet, a heuristic neural system (HNS) architecture of each worker in an ANTS provides coordination and interaction between each HNS that yields performance of the aggregate of the ANTS that exceeds the performance of a group of generalist workers. 
     More specifically, subset neural basis functions (SNBFs) within a HNS can have a hierarchical interaction among themselves much as the workers do in the entire ANTS collective. Hence, although many activities of the spacecraft could be controlled by individual SNBFs, a ruler SNBF could coordinate all of the SNBFs to assure that spacecraft objectives are met. Additionally, to have redundancy for the mission, inactive workers and rulers may only participate if a member of their type is lost. 
     In some situations, the ANTS encounters a challenging situation. For example, in some instances, the operation of a particular autonomic spacecraft can be detrimental either to the autonomic spacecraft or to the mission. 
     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 reduce the possibility that an autonomic element will jeopardize the mission of the autonomic element. 
     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 of the invention, a method for managing a system based on functioning state and operating status of the system includes processing received signals from the system, which are indicative of the functioning state and the operating status, to obtain an analysis of the condition of the system, generating one or more stay-awake signals based on the functioning status and the operating state of the system, transmitting the stay-awake signal, transmitting self health/urgency data, and transmitting environment health/urgency data. 
     In still yet other embodiments, an autonomic element includes a self-monitor that is operable to receive information from sensors and is operable to monitor and analyze the sensor information and access a knowledge repository, a self-adjuster operably coupled to the self-monitor in a self-control loop, the self-adjuster operable to access the knowledge repository, the self-adjuster operable to transmit data to effectors, and the self-adjuster operable to plan and execute; an environment monitor that is operable to receive information from sensors and operable to monitor and analyze the sensor information and access the knowledge repository; and an autonomic manager communications component operably coupled to the environment monitor in an environment control loop, the autonomic manager communications component operable to access the knowledge repository, the autonomic manager communications component also operable to produce and transmit a pulse monitor signal, and 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. In exemplary embodiments, for example, a computer-accessible medium in a first autonomic element has executable instructions of autonomic communication for directing a processor of the first autonomic element to perform receiving a quiesce instruction from a second autonomic element; and 
     invoking a function of a quiesce component of the first autonomic element, and then, if the first autonomic element does not receive a stay-alive reprieve signal after a predetermined period of time, the first autonomic element to self-destruct; or an autonomic element includes a self-monitor, a self-adjuster operably coupled to the self-monitor in a self control loop, an environment-monitor, an autonomic manager communications component operably coupled to the environment-monitor in an environment control loop, and a quiescing component, operably coupled to the self-monitor where the quiescing component receives a quiescing instruction from another autonomic element and withdraws a stay-awake signal, and then, if the autonomic element does not receive a reprieve signal after a predetermined period of time, the autonomic element self-destructs; or a computer-accessible medium has executable instructions to construct an environment to satisfy increasingly demanding external requirements capable of directing a processor to perform instantiating a first embryonic evolvable neural interface; and evolving the first embryonic evolvable neural interface towards complex connectivity, wherein the evolvable neural interface receives one or more heart beat monitor signal, pulse monitor signal, and quiesce signals, wherein the evolvable neural interface generates one or more heart beat monitor signal, pulse monitor signal, and quiesce signals, and wherein the first evolvable neural interface receives a quiesce signal from a second evolvable neural interface, and then, if the first evolvable neural interface does not receive a stay-alive reprieve signal after a predetermined period of time, the first evolvable neural interface self-destructs; or a computer-accessible medium has executable instructions to protect an autonomic system when encountering one or more autonomic agent capable of directing a processor of an autonomic agent to perform sending a quiesce signal to the autonomic agent; monitoring the response of the autonomic agent to the quiesce signal; and determining the autonomic agent potential for causing harm to the autonomic system, and then, if the autonomic agent does not receive a stay-alive reprieve signal after a predetermined period of time, the autonomic agent self-destructs. In an exemplary method, a method for protecting an autonomic system when encountering one or more autonomic agents includes determining the potential harm of the one or more autonomic agent; sending a quiesce signal to the one or more autonomic agent; and monitoring the response of the one or more autonomic agent to the quiesce signal, and then, if the autonomic agent does not receive a stay-alive reprieve signal after a predetermined period of time, the autonomic agent self-destructs. 
     Systems, clients, servers, methods, and computer-readable media of varying scope are described herein. In addition to the aspects and advantages described in this summary, further aspects and advantages will become apparent by reference to the drawings and by reading the detailed description that follows. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         FIG. 1  is a block diagram that provides an overview of an evolvable synthetic neural system to manage collective interactions between autonomous entities, according to an embodiment of the invention; 
         FIG. 2  is a block diagram of a neural basis function of a worker, according to an embodiment; 
         FIG. 3  is a block diagram of a heuristic neural system, according to an embodiment; 
         FIG. 4  is a block diagram of an autonomous neural system, according to an embodiment; 
         FIG. 5  is a block diagram of a neural basis function of a worker, according to an embodiment; 
         FIG. 6  is a block diagram of a multiple level hierarchical evolvable synthetic neural system, according to an embodiment; 
         FIG. 7  is a block diagram of a conventional computer cluster environment in which different embodiments can be practiced; 
         FIG. 8  is a block diagram of a conventional hardware and operating environment in which different embodiments can be practiced; 
         FIG. 9  is a block diagram of a conventional multiprocessor hardware and operating environment in which different embodiments can be practiced; 
         FIG. 10  is a block diagram of a hardware and operating environment, which includes a quiese component, according to an embodiment. 
         FIG. 11  is a diagram of a three dimensional hierarchical evolvable synthetic neural system, according to an embodiment; 
         FIG. 12  is a diagram of a heuristic neural system, according to an embodiment, for a single instrument spacecraft to prospect asteroid belts; 
         FIG. 13  is a diagram of an autonomous entity managing a system, according to an embodiment; 
         FIG. 14  is a diagram of autonomous entities&#39; interaction, according to an embodiment; 
         FIG. 15  is a block diagram of an autonomous entity management system, according to an embodiment; 
         FIG. 16  is a hierarchical chart of an autonomous entity management system, according to an embodiment; 
         FIG. 17  is a block diagram of an autonomic element, according to an embodiment; 
         FIG. 18  is a block diagram of autonomy and autonomicity at a high system level, according to an embodiment; 
         FIG. 19  is a block diagram of an architecture of an autonomic element, according to an embodiment, that includes reflection and reflex layers; 
         FIG. 20  is a flowchart of a method to construct an environment to satisfy increasingly demanding external requirements, according to an embodiment; 
         FIG. 21  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. 22  is a flowchart for a generating stay-alive signal when a warning condition occurs, according to an embodiment; 
         FIG. 23  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. 24  is a flowchart for generating a stay-awake signal when a warning condition occurs, according to an embodiment; 
         FIG. 25  is a flowchart for interrogating an anonymous autonomic agent, according to an embodiment; 
         FIG. 26  is a flowchart of a method of autonomic communication by an autonomic element, according to an embodiment; 
         FIG. 27  is a flowchart of a method of autonomic communication by an autonomic element, according to an embodiment; 
         FIG. 28  is a flowchart of a method of autonomic communication by an autonomic element, according to an embodiment; and 
         FIG. 29  is a flowchart of a method of autonomic communication by an autonomic element, according to an embodiment. 
     
    
    
     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 six sections. In the first section, a system level overview is described. In the second section, apparatus 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 are described. In the fifth section, embodiments of methods are described. Finally, in the sixth section, a conclusion of the detailed description is provided. 
     System Level Overview 
       FIG. 1  is a block diagram that provides an overview of an evolvable synthetic neural system to manage collective interactions between autonomous entities, according to an embodiment. System  100  may include a first plurality of neural basis functions (NBFs)  102  and  104 . NBFs are the fundamental building block of system  100 . In some embodiments of system  100 , the plurality of NBFs includes more than the two NBFs  102  and  104  shown in  FIG. 1 . In some embodiments, system  100  includes only one NBF. One embodiment of a NBF is described below with reference to  FIG. 2 . 
     System  100  may also include a first inter-evolvable neural interface (ENI)  106  that is operably coupled to each of the first plurality of neural basis functions. The NBFs  102  and  104  may be highly integrated, and coupling between the NBFs through the ENI  106  may provide a three dimensional complexity. Thus, for example, when system  100  is implemented on microprocessors such as microprocessor  804  described below with reference to  FIG. 8 , system  100  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  106  may be known as an inter-NBF ENI because the inter-ENI  106  is illustrated as being between or among the NBFs  102  and  104  at the same level within a hierarchy. System  100  shows only one level  108  of a hierarchy, although one skilled in the art will recognize that multiple hierarchies may be used within the scope of this invention. 
     System  100  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  106  may adapt system  100  by instantiating new NBFs and ENIs and establishing operable communication paths  110  to the new NBFs and the ENIs to system  100 . ENI  106  may also adapt system  100  by removing or disabling the operable communication paths  110  to the new NBFs and ENIs. The adapting, establishing, removing and disabling of the communication paths  110  may be performed autonomously. Thus, system  100  may satisfy the need for a synthetic neural system that performs significant tasks with complete autonomy. 
     System  100  may be capable of establishing and removing links to other similarly configured systems (not shown). Thus, the system  100  may be described as self-similar. 
     The system level overview of the operation of an embodiment is described in this section of the detailed description. Some embodiments may operate in a multi-processing, multi-threaded operating environment on a computer, such as computer  802  in  FIG. 8 . 
     While the system  100  is not limited to any particular NBF or ENI, for sake of clarity simplified NBFs and a simplified ENI are described. 
     Apparatus Embodiments 
     In the previous section, a system level overview of the operation of an embodiment is described. In this section, particular apparatus of such an embodiment are described by reference to a series of block diagrams. Describing the apparatus by reference to block diagrams enables one skilled in the art to develop programs, firmware, or hardware, including such instructions to implement the apparatus on suitable computers, executing the instructions from computer-readable media. 
     In some embodiments, apparatus  200 - 600  are implemented by a program executing on, or performed by firmware or hardware that is a part of a computer, such as computer  802  in  FIG. 8 . 
       FIG. 2  is a block diagram of a neural basis function (NBF)  200  of a worker according to an embodiment. NBF  200  is illustrated as a bi-level neural system because both high-level functions and low-level functions are performed by NBF  200 . 
     NBF  200  may include an intra-evolvable neural interface (intra-ENI)  202 . The ENI  202  may be operably coupled to a heuristic neural system (HNS)  204  and operably coupled to an autonomous neural system (ANS)  206 . The HNS  204  may perform high-level functions and the ANS  206  may perform low-level functions that are often described as “motor functions” such as “motor”  1810  in  FIG. 18  below. In NBF  200 , the HNS  204  and the ANS  206  in aggregate may provide a function of a biological neural system. The intra-ENI  202  shown in  FIG. 2  is an ENI that is wholly contained within an NBF, and is therefore prefixed with “intra.” 
     The intra-ENI  202  may send action messages to and receive request messages from the HNS  204  and the ANS  206  during learning and task execution cycles, as well as during interfacing operations between the intra-ENI and the HNS  204  and the ANS  206  when the HNS  204  and the ANS  206  need to be modified as a result of other system failures or modification of objectives. NBF  200  is illustrated as a worker NBF because this NBF performs functions, but does not provide instructions commands to other NBFs. 
       FIG. 3  is a block diagram of a heuristic neural system  300  according to an embodiment. 
     The heuristic neural system (HNS)  300  may be composed of a neural net  302  for pattern recognition and a fuzzy logic package  304  to perform decisions based on recognitions. Taken together the neural net  302  and the fuzzy logic package  304  may form a basis for a higher level heuristic intelligence. 
       FIG. 4  is a block diagram of an autonomous neural system  400  according to an embodiment. 
     The autonomous neural system (ANS)  400  may include a non-linear dynamics simulation  402  that represents smart servo system behavior. 
       FIG. 5  is a block diagram of a neural basis function (NBF)  500  of a worker according to an embodiment. NBF  500  is shown as a bi-level neural system. 
     In some embodiments, NBF  500  may include a self assessment loop (SAL)  502  at each interface between autonomic components. Each SAL  502  may continuously gauge efficiency of operations of the combined HNS  204  and ANS  206 . The standards and criteria of the efficiency may be set or defined by objectives of the NBF  500 . 
     In some embodiments, NBF  500  may also include genetic algorithms (GA)  504  at each interface between autonomic components. The GAs  504  may modify the intra-ENI  202  to satisfy requirements of the SALs  502  during learning, task execution or impairment of other subsystems. 
     Similarly, the HNS  204  may have a SAL  502  interface and a GA  504  interface to a core heuristic genetic code (CHGC)  506 , and the ANS  206  may have a SAL  502  interface and a GA  504  interface to a core autonomic genetic code (CAGC)  508 . The CHGC  506  and CAGC  508  may allow modifications to a worker functionality in response to new objectives or injury. The CHGC  506  and the CAGC  508  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  506  and the CAGC  508  may both be modifiable depending on variations in sensory inputs via GAs  504 . 
     In some embodiments, the CHGC  506  and the CAGC  508  in conjunction with SALs  502  and GAs  504  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  500  may provide a form of evolution possible only over generations of BNF workers. 
     In some embodiments, NBF  500  may also include genetic algorithms  510  and  512  that provide process information to the CHGC  506  and the CAGC  508 , respectively. HNS  204  and ANS  206  may receive sensory input  514  and  516 , respectively, process the sensory input and generate high level actions  518  and low level actions  520 , respectively. 
       FIG. 6  is a block diagram of a multiple level hierarchical evolvable synthetic neural system (ESNS)  600  according to an embodiment. 
     The multiple level hierarchical ESNS  600  may include a first level of hierarchy  602  that includes a NBF  604  and inter-ENI  606  and a ruler NBF  608 . A ruler NBF, such as ruler NBF  608  may perform functions and also provide instructions commands to other subordinate NBFs. 
     The ruler NBF  608  of the first hierarchical level  602  is illustrated as being operably coupled to a ruler NBF  610  in a second hierarchical level  612 . Ruler NBF  610  may perform functions, receive instructions and commands from other ruler NBFs that are higher in the hierarchy of the ESNS  600  and also provide instructions commands to other subordinate NBFs. 
     The second hierarchical level  612  may also include an inter-ENI  614 . The second hierarchical level  612  of  FIG. 6  shows an embodiment of an ESNS  600  having one NBF operably coupled to an ENI. The ruler NBF  610  of the second hierarchical level  612  may be operably coupled to a ruler NBF  616  in a third hierarchical level  618 . 
     The third hierarchical level  616  may also include an inter-ENI  620 . The third hierarchical level  616  of  FIG. 6  shows an embodiment of an ESNS  600  having more than two NBFs (e.g.  616 ,  622  and  624 ) operably coupled to an ENI. 
     In some embodiments, the NBFs  604 ,  608 ,  610 ,  616 ,  622  and  624  may include the aspects of NBFs  102  and  104  in  FIG. 1  above, and/or NBF  200  in  FIG. 2  above. One skilled in the art will appreciate that many combinations exist that fall within the purview of this invention. 
     Hardware and Operating Environments 
       FIGS. 7 ,  8 ,  9  and  10  are diagrams of hardware and operating environments in which different embodiments can be practiced. The description of  FIGS. 7 ,  8 ,  9  and  10  provide an overview of computer hardware and suitable autonomic computing environments in conjunction with which some embodiments can be implemented. Embodiments are described in terms of a computer executing computer-executable instructions. However, some embodiments can be implemented entirely in computer hardware in which the computer-executable instructions are implemented in read-only memory. Some embodiments can also be implemented in client/server autonomic computing environments where remote devices that perform tasks are linked through a communications network. Program modules 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. 7  is a block diagram of a computer cluster environment  700  in which different embodiments can be practiced. System  100 , apparatus  200 ,  300 ,  400 ,  500 ,  600 , method  2000  and ESNS  1100  and  1200  can be implemented on computer cluster environment  700 . 
     Computer cluster environment  700  may include a network  702 , such as an EtherFast 10/100 backbone, that is operably coupled to a cluster server  704  and a plurality of computers  706 ,  708 ,  710  and  712 . One possible embodiment of the computers is computer  802  described below with reference to  FIG. 8 . The plurality of computers can include any number of computers, but some implementations may include 7, 16, 32 and as many as 512 computers. The ESNSs and NBFs described above can be distributed on the plurality of computers. 
     One example of the computer cluster environment  700  is a Beowolf computer cluster. The computer cluster environment  700  provides an environment in which a plurality of ESNSs and NBFs can be hosted in an environment that facilitates cooperation and communication between the ESNSs and the NBFs. 
       FIG. 8  is a block diagram of a hardware and operating environment  800  in which different embodiments can be practiced. Computer  802  may include a processor  804 , which may be a microprocessor, commercially available from Intel, Motorola, Cyrix and others. Computer  802  may also include random-access memory (RAM)  806 , read-only memory (ROM)  808 , and one or more mass storage devices  810 , and a system bus  812 , that operatively couples various system components to the processing unit  804 . The memory  806 ,  808 , and mass storage devices,  810 , are illustrated as types of computer-accessible media. Mass storage devices  810  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  804  can execute computer programs stored on the computer-accessible media. 
     Computer  802  may be communicatively connected to the Internet  814  via a communication device  816 . Internet  814  connectivity is well known within the art. In one embodiment, a communication device  816  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  816  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., T1 line, etc.). 
     A user may enter commands and information into the computer  802  through input devices such as a keyboard  818  or a pointing device  820 . The keyboard  818  may permit entry of textual information into computer  802 , as known within the art, and embodiments are not limited to any particular type of keyboard. Pointing device  820  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  820 . 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  802  may be operatively coupled to a display device  822 . Display device  822  may be connected to the system bus  812 . Display device  822  permits the display of information, including computer, video and other information, for viewing by a user of the computer. Embodiments are not limited to any particular display device  822 . Such display devices 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  824  and  826  provide audio output of signals. Speakers  824  and  826  may also be connected to the system bus  812 . 
     Computer  802  may also include an operating system (not shown) that could be stored on the computer-accessible media RAM  806 , ROM  808 , and mass storage device  810 , and may be and executed by the processor  804 . Examples of operating systems include Microsoft Windows®, Apple MacOS®, Linux®, UNIX®. Examples are not limited to any particular operating system, however, and the construction and use of such operating systems are well known within the art. 
     Embodiments of computer  802  are not limited to any type of computer  802 . In varying embodiments, computer  802  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  802  may be operated using at least one operating system to provide a graphical user interface (GUI) including a user-controllable pointer. Computer  802  may have at least one web browser application program executing within at least one operating system, to permit users of computer  802  to access an intranet, extranet or Internet world-wide-web pages as addressed by Universal Resource Locator (URL) addresses. Examples of browser application programs include Netscape Navigator® and Microsoft Internet Explorer®. 
     The computer  802  may operate in a networked environment using logical connections to one or more remote computers, such as remote computer  828 . These logical connections may be achieved by a communication device coupled to, or a part of, the computer  802 . Embodiments are not limited to a particular type of communications device. The remote computer  828  could be another computer, a server, a router, a network PC, a client, a peer device or other common network node. The logical connections depicted in  FIG. 8  include a local-area network (LAN)  830  and a wide-area network (WAN)  832 . Such networking environments are commonplace in offices, enterprise-wide computer networks, intranets, extranets and the Internet. 
     When used in a LAN-networking environment, the computer  802  and remote computer  828  may be connected to the local network  830  through network interfaces or adapters  834 , which is one type of communications device  816 . Remote computer  828  may also include a network device  836 . When used in a conventional WAN-networking environment, the computer  802  and remote computer  828  may communicate with a WAN  832  through modems (not shown). The modem, which can be internal or external, is connected to the system bus  812 . In a networked environment, program modules depicted relative to the computer  802 , or portions thereof, can be stored in the remote computer  828 . 
     Computer  802  may also include power supply  838 . Each power supply can be a battery. 
       FIG. 9  is a block diagram of a multiprocessor hardware and operating environment  900  in which different embodiments can be practiced. Computer  902  may include a plurality of microprocessors, such as microprocessor  804 ,  904 ,  906 , and  908 . The four microprocessors of computer  902  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  700  in  FIG. 7  above, the computer  902  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. 10  is a block diagram of a hardware and operating environment  1000  which may include a quiese component, according to an embodiment. The hardware and operating environment  1000  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  1002  of an autonomic unit can render the autonomic unit inactive for a specific amount of time or until a challenging situation has passed. The quiesce component  1002  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  1002  may reduce the possibility that an autonomic element will jeopardize the mission of the autonomic element by deactivation 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, where cells may stop dividing and go into a quiescent state. 
     Components of the system  100 , apparatus  200 ,  300 ,  400 ,  500 ,  600 ,  1000 ,  1100 ,  1200 ,  1300 ,  1400 ,  1500 ,  1600 ,  1700 ,  1800  and  1900  and methods  2000 ,  2100 ,  2200 ,  2300 ,  2400 ,  2500 ,  2600 ,  2700 ,  2800  and  2900  may be embodied as computer hardware circuitry or as a computer-readable program, or a combination of both. 
     More specifically, in one computer-readable program embodiment, the programs can be structured in an object-orientation using an object-oriented language such as Java, Smalltalk or C++, and the programs can be structured in a procedural-orientation using a procedural language such as COBOL or C. The software components may communicate in any of a number of ways that are well-known to those skilled in the art, such as application program interfaces (API) or interprocess communication techniques such as remote procedure call (RPC), common object request broker architecture (CORBA), Component Object Model (COM), Distributed Component Object Model (DCOM), Distributed System Object Model (DSOM) and Remote Method Invocation (RMI). The components execute on as few as one computer as in computer  802  in  FIG. 8 , or on at least as many computers as there are components. 
     Implementation of an Evolvable Synthetic Neural System in a Tetrahedral Architecture 
     Referring to  FIG. 11 , a particular three-dimensional implementation is described in conjunction with the system overview in  FIG. 1  and the apparatus described in  FIG. 6 . 
       FIG. 11  is a diagram of a three dimensional (3D) hierarchical evolvable synthetic neural system (ESNS)  1100  according to an embodiment. 
     The 3D hierarchical ESNS  1100  may include a ruler subsystem  1102  and four worker subsystems  1104 ,  1106 ,  1108  and  1110 . Each subsystem in the 3D hierarchical ESNS  1100  may include one or more ESNSs such as system  100  or ESNS  600 . 
     The three dimensional architecture of 3D hierarchical ESNS  1100  may provide a three dimensional complexity. An implementation of ESNS  600  on a microprocessor such as microprocessor  804  described below with reference to  FIG. 8 , may provide a synthetic neural system that reconciles the two dimensional nature of microprocessor technologies to the three dimensional nature of biological neural systems. 
     Implementation of Single Instrument Spacecraft to Prospect Asteroid Belts 
     Referring to  FIG. 12 , a particular three-dimensional implementation for asteroid prospecting is described in conjunction with the system overview in  FIG. 1  and the apparatus described in  FIG. 6 . 
       FIG. 12  is a diagram of a heuristic neural system (HNS)  1200  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  1202  may control sail deployment and subsequent configuration activity much as terrestrial sailors perform navigation and manage propulsion. A spacecraft inter communication subsystem SNBF  1204  may control communication with other workers and rulers. Also a housekeeping SNBF  1206  may control the spacecraft housekeeping. HNS  1200  may also include a ruler subsystem  1208  to coordinate all activities. Similarly, a spacecraft navigation and propulsion subsystem  1210  may control the navigation and propulsion, matching the navigation and propulsion to the current objectives. 
     In 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  1200  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 s/c 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 mainbelt 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  1200 ) 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 bodies 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. 13  is a block diagram of an autonomic entity management system  1300  according to an embodiment. The system  1304  is 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 system  1304  could be software for updating or for synchronizing a file, could be a worker craft containing unique instrument for data gathering in an autonomous nano-technology swarm, or the system  1304  could be an application for managing resources in networks or general purpose computers. The system  1304  can have multiple applications and capabilities such as self healing and self monitoring, but as a minimum, the system  1304  is required to have a way for communicating a functional status and operating state signal to the managing autonomous entity manager  1302 . The system  1304  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  1300  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  1302  or system  1304  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  1304  is still functioning as designed. The HBM signal is fundamentally an “I am alive” signal to the autonomic manager, such as autonomic entity  1302 , indicating that the system is functioning. These signals can be communicated from system  1304  through an appropriate bidirectional communication link  1306 . The response from the autonomous manager  1302  can use the same link to influence system  1304 . 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  1302  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  1304 . Autonomous manager  1302  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  1304  by removing the offending agent or the sub-component of system  1304 . 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 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  1304 , 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. 14  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  1418  and  1420  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 will be limited to the download of science data and status information. An example of such a grouping is shown in  FIG. 14  where autonomic entity  1402  is shown as a ruler entity, autonomic entity  1410  as a message entity, and autonomic entities  1418  and  1420  are examples of worker entities. In terms of hardware, these entities can be all identical with the discernable difference being programming to accomplish assigned tasks. An added advantage to having identical hardware is replacing failed entities, which can be accomplished by activating software code found in the autonomic entity. If hardware differences exist they can be based on specialized equipment suitable for a particular task. However, at a minimum, certain functions or roles, such as ruler and messenger, may be expected to be within the skill set of all the autonomic entities. 
     As shown in  FIG. 14 , ruler autonomic entity  1402  may comprise a program or process  1404  executing in ruler entity  1402 . Ruler entity  1402  can be implemented using a data processing system, such as data processing system  902  in  FIG. 9 , or in the form of an autonomous agent compiled by a data processing system. In the alternative, the ruler entity could be an autonomous nano-technology swarm that is launched from a factory ship for exploring planets, asteroids, or comets. Further, analysis module  1406  or agent as executed by ruler entity  1402  can be used to monitor process  1404  and to receive pulse monitor and heart beat monitor signals from worker entities through the messenger entity. When analysis module  1406  is used to monitor process  1404  it may be to detect errors or problems with the operation of process  1404 . 
     As shown in  FIG. 14 , analysis agent  1406  can include an evaluator or other monitoring engine used to monitor the operation of process  1404 . Analysis agent  1406  may be executed in response to some event. This event can be a periodic event, such as the passage of some period of time, data received from one or more of the worker entities. Further, the event can be the initialization of internal procedures in process  1404  or the starting or restarting of ruler entity  1402 . Depending on the particular implementation, analysis agent  1406  can continuously run in the background monitoring process  1404  and analyzing the worker entity signals. See method  2100  in  FIG. 21  below for actions taken by analysis agent module  1406  in formulating a strategy for the worker entities. Further, analysis agent  1406  may be subject to any self-healing routines found in ruler entity  1402 . 
     This monitoring by analysis agent  1406  may be based on rules stored in behavior storage  1408 , which could be used to compare the actual behavior of the received data to an expected behavior as defined in behavior storage  1408 . In the present arrangement, behavior storage  1408  (ruler entity  1402 ) 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  1408  can be narrowly tailored based on the use and purpose of the autonomic entity, such as messenger entity  1410  and have only those procedures needed to perform its programming. 
     When messenger entity connects to remote computer at a command and control station, database  1416  can be updated with information that can later be used to program ruler entity or worker entity. In most cases a copy of the rules in database  1416  contains the most up-to-date information. If the objective changes or a solution to a problem requires an updated version not found within the autonomic entity, the entities may attempt to contact message entity  1410  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  1408  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 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  1406  using the rules in behavior storage  1408 . Based on the analysis, the ruler entity can elect to either withdraw the stay alive signal to the malfunctioning worker entity or wait a selected period to generate one or more stay alive signal, withdrawal of a stay alive signal, or a self-destruct signal. If the stay alive signal is withdrawn, the malfunctioning entity could be disconnected from the operation and the assigned to another entity or partially performed by the remaining entity to insure its completion. 
       FIG. 15  is a block diagram of an autonomous entity management system  1500  according to an embodiment. The system  1500  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  1500  represents an instance when an autonomous system  1504  encounters an anonymous autonomic agent  1502 . An anonymous autonomous agent can be a visiting agent, a mobile agent that can enter the sphere of influence of the autonomous system  1504 , or any device for which the autonomous system  1504  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  1500  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  1504  may comprise one or more autonomic agents  1508 ,  1510 , and  1512  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  1502  the assigned autonomous agent for performing security functions for autonomous system  1504  may interrogate the anonymous autonomic agent  1502 , 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  1502  requests resources from the system  1504  or from any autonomic entity that forms part of the system, response to polling signals from the autonomous system  1504 , or through a friend or foe signal that indicates the presence of an anonymous entity  1502  in proximity to the autonomous system  1504 . 
     To the autonomous system  1504 , 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  1502  may be sent an asynchronous ALice signal (Autonomic license)  1506  requiring valid credentials from the agent  1502 . The anonymous agent  1502  may need to work within the autonomic system  1504  to facilitate self-management, as such the anonymous agent  1502  and its host may need to be able to identify each other&#39;s credentials through such as an ALice signal. The autonomic system  1504  can establish certain response characteristics for the returned signal from the agent  1502 . For example, the autonomic system  1504  can require a response in an appropriate format, within a certain timeout period, and with a valid and justified reason for being within the locust of interest or domain of the autonomous system  1504 . For protection the autonomic system  1504  may make an assessment of the quality of the response from the anonymous agent  1502  to ascertain the potential of the agent for causing harm to the autonomous system  1504 . Based on this determination the autonomous system  1504  can control the type of interaction with the agent  1502 . The agent can be destroyed, blocked, partially blocked, stay alive signal withdrawn, or allowed to communicate with other agents within the autonomous system  1504 . The protection can be triggered at any level of infraction or by a combination of infractions by the anonymous autonomous agent  1502  when responding to the ALice signal. If the agent  1502  fails to identify itself appropriately following an ALice interrogation, the agent  1502  may be blocked from the system and given either a self-destruct signal, or its “stay alive” reprieve is withdrawn. The consequence of unacceptable response to an anonymous agent  1502 , should it fail to do so within a timeout period, the agent  1502  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  1508 ,  1510 ,  1512  in the system. As an alternative to the ALice signal, a quiese signal, command or instruction can be sent. The quiesce signal is discussed in more detail in conjunction with  FIGS. 10 ,  23  and  24 . 
       FIG. 16  is a hierarchical chart of an autonomous entity management system  1600  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  1600 . 
     General properties of an autonomic (self-managing) system may include four objectives defined by International Business Machines  1602 : self-configuring  1604 , self-healing  1606 , self-optimizing  1608  and self-protecting  1610 , and four attributes  1612 : self-awareness  1614 , environment-awareness  1616 , self-monitoring  1618  and self-adjusting  1620 . One skilled in the art will recognize that other properties also exist, such as self-quiescing  1625 . Essentially, the objectives  1602  could represent broad system requirements, while the attributes  1612  identify basic implementation mechanisms. 
     Self-configuring  1604  may represent an ability of the system  1600  to re-adjust itself automatically; this can simply be in support of changing circumstances, or to assist in self-healing  1606 , self-optimization  1608  or self-protection  1610 . Self-healing  1606 , 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  1606  objective may monitor vital signs in an attempt to predict and avoid “health” problems (i.e. reaching undesirable situations). 
     Self-optimization  1608  may mean that the system  1600  is aware of ideal performance of the system  1600 , can measure current performance of the system  1600  against that ideal, and has defined policies for attempting improvements. The system  1600  can also react to policy changes within the system as indicated by the users. A self-protecting  1610  system  1600  can defend the system  1600  from accidental or malicious external attack, which necessitates awareness of potential threats and a way of handling those threats. 
     Self-managing objectives  1602  may require awareness of an internal state of the system  1600  (i.e. self-aware  1614 ) and current external operating conditions (i.e. environment-aware  1616 ). Changing circumstances can be detected through self-monitoring and adaptations are made accordingly (i.e. self-adjusting  1620 ). Thus, system  1600  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-* 1622  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  1604 , Self-Healing  1606 , Self-Optimisation  1608  and Self-Protection  1610 ). 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. 17  is a block diagram of an autonomic element  1700  according to an embodiment. Autonomic element  1700  may include an element  1702  that is operably coupled to sensors and  1704  and effectors  1706 . 
     Autonomic element  1700  may also include components that monitor  1708 , execute  1710 , analyze  1712  and plan  1714 ; those components may access knowledge  1716 . Those components can interact with sensors  1718  and effectors  1720 . 
       FIG. 18  is a block diagram of autonomy and autonomicity  1800  at a high system level, according to an embodiment. A high level perspective for an intelligent machine design is depicted in  FIG. 18 . This diagram of autonomy and autonomicity  1800  includes intelligent machine design and system level autonomy and autonomicity. 
       FIG. 18  describes three levels for the design of intelligent systems: 
     1) Reaction  1802 —the lowest level, where no learning occurs but there is immediate response to state information coming from sensory systems  1804 . 
     2) Routine  1806 —middle level, where largely routine evaluation and planning behaviors take place. Input is received from sensory system  1804  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  1808 —top level, receives no sensory  1804  input or has no motor  1810  output; input is received from below. Reflection is a meta-process, whereby the mind deliberates about itself. Essentially, operations at this level look at the system&#39;s representations of its experiences, its current behavior, its current environment, etc. 
     As illustrated, input from, and output to, the environment only takes place within the reaction  1802  and routine  1806  layers. One can consider that reaction  1802  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  1802  level can reside within an artificial domain utilizing its techniques to consider the behavior of the system and learn new strategies. The routine  1806  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. 18 , in consideration of the dynamics of responses including reaction  1802  and also for reflection  1808  of self-managing behavior. 
     As depicted autonomic computing can reside within the domain of the reaction  1802  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  1802 , routine  1806 , and reflection  1808 ) and accepts the narrower view of autonomicity, there is a potential correlation between the levels. That is, the reaction  1802  level correlates with autonomicity, and the reflection  1808  level correlates with autonomy; autonomy as in self-governing of the self-managing policies within the system. 
       FIG. 19  is a block diagram of an architecture of an autonomic element (AE)  1900  according to an embodiment that includes reflection and reflex layers. The autonomic element  1900  may include a managed component (MC)  1902  that is managed, and the autonomic element  1900  may further include an autonomic manager (AM), not shown. The AM may be responsible for the MC  1902  within the AE  1900 . 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  1900  can occur with remote (external) autonomic managers (cf. the autonomic communications channel  1906 ) 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. 17 . A control loop  1908  can be created by monitoring  1910  behavior through sensors, comparing this with expectations (knowledge  1716 , as in historical and current data, rules and beliefs), planning  1912  what action is necessary (if any), and then executing that action through effectors. The closed loop of feedback control  1908  can provide a basic backbone structure for each system component.  FIG. 19  describes at least two control loops in the autonomic element  1900 , one for self-awareness  1914  and another  1908  for environmental awareness. 
     In some embodiments, the self-monitor/self-adjuster control loop  1914  can be substantially similar to the monitor, analyze, plan and execute (MAPE) control loop described in  FIG. 17 . The monitor-and-analyze parts of the structure can perform a function of processing information from the sensors to provide both self-awareness  1914  and an awareness  1908  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  1716  within the AM  1904 . 
     A reflection component  1916  may perform analysis computation on the AE  1900  (cf. the reflection component  1916  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  1918  can also produce a reflex signal  1920 . A self adjuster  1922  can be operably coupled to a self-monitor  1924  in the self control loop  1914 . 
     Method Embodiments 
     In the previous section, apparatus embodiments are described. In this section, the particular methods of such embodiments are described by reference to a series of flowcharts. Describing the methods by reference to a flowchart enables one skilled in the art to develop such programs, firmware, or hardware, including such instructions to carry out the methods on suitable computers, executing the instructions from computer-readable media. Similarly, the methods performed by the server computer programs, firmware, or hardware can also be composed of computer-executable instructions. In some embodiments, method  2000  may be performed by a program executing on, or performed by firmware or hardware that is a part of a computer, such as computer  802  in  FIG. 8 . 
       FIG. 20  is a flowchart of a method  2000  to construct an environment to satisfy increasingly demanding external requirements according to an embodiment. 
     Method  2000  may include instantiating  2002  an embryonic evolvable neural interface (ENI), such as inter-ENI  106 . In one embodiment, 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  2000  can further include evolving  2004  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 more simple 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  2000  can be predicated on an active revision of the embryonic ENI to meet external action requirements for a sensory input. In particular, the ENI, which handles both the intra-NBF and inter-NBF connectivity, can evolve due to changing conditions that are either driven by training requirements or operational requirements. 
     In some embodiments, method  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  804  in  FIG. 8 , causes the processor to perform the respective method. In other embodiments, method  2000  may be implemented as a computer-accessible medium having executable instructions capable of directing a processor, such as processor  804  in  FIG. 8 , to perform the respective method. In varying embodiments, the medium can be a magnetic medium, an electronic medium, or an optical medium. 
       FIG. 21  is a flowchart of a method  2100  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  2100  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  2100  can begin with action  2102  when receiving a signal from a managed entity. 
     Action  2102  can receive a heart beat monitor (HBM) signal and pulse monitor (PBM) signal from a managed entity such as worker entities  1418  or  1420 . 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  2104  for further processing. 
     In action  2104 , 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  2106  for further processing. 
     In action  2106 , an alarmed condition may be determined. In action  2106 , the analysis of action  2104  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  2108  so as to generate a stay alive signal. In the event that an alarm condition is present, control may be passed to action  2110  for further processing. 
     In action  2110 , a determination may be made to ascertain whether the identified alarmed condition of action  2106  is recoverable by the managed entity, such as worker entities  1418  and  1420  of  FIG. 14 . When an alarmed condition is determined to be recoverable, control may be passed to action  2108  to generate a stay alive signal. When an alarmed condition is determined not to be recoverable, control may be passed to action  2112  to withdraw the stay alive signal. 
       FIG. 22  is a flowchart of a method  2200  for ascertaining the recoverability of an alarmed condition determined at action  2106  according to am embodiment. Method  2200  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  2200  is one possible embodiment of the action in  FIG. 21  above of determining  2110  if the identified alarmed condition is recoverable. 
     Method  2200  may begin with action  2202  when receiving one or more alarmed condition. In action  2202 , there may be a determination if an incorrect operation from the managed system has been identified in action  2104  of  FIG. 21 . An incorrect operation can range from not initializing sensors to failing to self-heal when internal decision logic recommends as an appropriate cause of action. In action  2202  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  2208  for further processing. 
     In action  2204 , there may be a determination whether emergent behavior from the managed system has been identified in action  2104  of  FIG. 21 . 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  2208  for further processing. 
     In action  2206 , 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  2208  for further processing. 
     Evaluation block  2208  may marshal the incorrect operation identified in action  2202 , the emergent behavior in action  2204 , or the effect on mission in action  2206  to suggest a course of action that the managed entities should adopt, which in the present arrangement is based on a stay alive signal. The determination of withdrawing or affirming the stay alive signal can be based on the occurrence of one or more of the identified alarmed conditions, or a combination of two or more of the identified alarmed conditions. For example, the stay alive signal could be withdrawn if there is emergent behavior and there would be an effect on the mission. In the alternative, the stay alive signal could be affirmed if there was only emergent behavior, or incorrect operation. Once the evaluation is determined, control may be passed to decision block  2210  for further processing in accordance to the decision made in evaluation block  2208 . 
     In action  2210 , if the desired control instruction is to maintain the stay alive signal, control can be passed to action  2108  for further processing. In the alternative, a withdrawal of the stay alive signal can be sent to action  2112  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. 23  is a flowchart of a method  2300  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  2300  may solve the need in the art to reduce the possibility that an autonomic element will jeopardize the mission of the autonomic element. 
     Method  2300  may begin with action  2102  when receiving a signal from a managed entity. Action  2102  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  2104  for further processing. 
     In action  2104 , 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  2106  for further processing. 
     In action  2106 , an alarmed condition can be determined. In action  2106 , the analysis of action  2104  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  2302  so as to generate a stay-alive signal. In the event that an alarm condition is present, control may be passed to action  2304  for further processing. 
     In action  2304 , a determination can be made to ascertain if the identified alarmed condition of action  2106  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  2112  to withdraw the stay-alive signal. Method  2400  below could be one embodiment of determining  2304  if the identified alarmed condition is recoverable. When an alarmed condition is determined to be recoverable, control may be passed to action  2308  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  2302  to generate a stay-awake/stay-alive-signal. When quiesence of the managed entity is determined as possible and/or needed in action  2308 , control can pass to action  2310 , 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  2300  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. 24  is a flowchart of a method  2400  for ascertaining the recoverability of an alarmed condition determined at action  2304 . Method  2400  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  2400  may begin with action  2202  when receiving one or more alarmed condition. 
     In action  2202 , there may be a determination if an incorrect operation from the managed system has been identified in action  2104  of  FIG. 21 . An incorrect operation can range from not initializing sensors to failing to self-heal when internal decision logic recommends as an appropriate cause of action. In action  2202 , 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  2208  for further processing. 
     In action  2204 , there may be a determination of emergent behavior from the managed system that has been identified in action  2104  of  FIG. 21 . 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  2208  for further processing. 
     In action  2206 , 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  2208  for further processing. 
     Evaluation block  2208  can marshal the incorrect operation identified in action  2202 , the emergent behavior in action  2204 , and the effect on mission in action  2206  to suggest a course of action that the managed entities should adopt, which in the present arrangement is based on a stay-awake signal. The determination of withdrawing or affirming the stay-awake signal can be based on the occurrence of one or more of the identified alarmed conditions, or a combination of two or more of the identified alarmed conditions. For example, the stay-awake signal could be withdrawn if there is emergent behavior and there would be an effect on the mission. In the alternative, the stay-awake signal could be affirmed if there was only emergent behavior, or incorrect operation. Once the evaluation is determined, control can pass to decision block  2402  for further processing in accordance with the decision made in evaluation block  2208 . 
     In action  2402 , if the desired control instruction is to maintain the stay-awake signal, control can be passed to action  2302  for further processing. In the alternative, a withdrawal of the stay-awake signal can be sent to action  2306  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. 25  is a flowchart of a method  2500  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  2500  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  2500  may begin with action  2502  where an ALice signal is sent to an anonymous agent to ascertain the agents potential for harm to a system as shown in  FIG. 22 . After the ALice signal has been sent to the agent, control may be passed to action  2504  for further processing. 
     In action  2504  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  2504  is completed, control may be passed to action  2506  for further processing. 
     In action  2506 , the monitored response from action  2504  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 locust of interest or domain of the autonomous system  2204  as shown in  FIG. 22 . Once the potential for causing harm has been ascertained, control may be passed to action  2508  for further processing. 
     In action  2508 , 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 conduction with  FIGS. 10 ,  23  and  24 . 
       FIG. 26  is a flowchart of a method  2600  of autonomic communication by an autonomic element. Method  2600  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 their total cost of ownership. 
     Method  2600  may include transmitting self health/urgency data  2602 . Examples of the self health/urgency data may include information describing low battery power and/or failed sensors. Method  2500  may also include transmitting  2604  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  2602  and  2604  can be performed in any order relative to each other. For example, in one embodiment the transmitting  2602  self health/urgency data may be performed before transmitting  2604  environment health/urgency data. In another embodiment, transmitting  2604  environment health/urgency data may be performed before transmitting  2602  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  1920  of  FIG. 19  above can be facilitated through the pulse monitor (PBM). A PBM can be an extension of the embedded system&#39;s heart-beat monitor, or HBM, which safeguards vital processes through the emission of a regular “I am alive” signal to another process with the capability to encode self health/urgency data and environment health/urgency data as a single pulse. HBM is described in greater detail in  FIGS. 13 ,  14  and  21  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  1918  may produce a reflex signal  1920  that includes the self health/urgency data and the environment health/urgency data in addition to the HBM. More concisely, the reflex signal can carry a PBM. A reflex signal that carries a PBM can be used to safe-guard the autonomic element by communicating health of the autonomic element to another autonomic unit. For instance, in the situation where each PC in a LAN is equipped with an autonomic manager, rather than each of the individual PCs monitoring the same environment, a few PCs (likely the least busy machines) can take on this role and alert the others through a change in pulse to indicate changing circumstances. 
     An important aspect concerning the reflex reaction and the pulse monitor is the minimization of data sent—essentially only a “signal” is transmitted. Strictly speaking, this is not mandatory; more information can be sent, yet the additional information should not compromise the reflex reaction. 
     Just as the beat of the heart has a double beat (lub-dub), the autonomic element&#39;s pulse monitor can have a double beat encoded—as described above, a self health/urgency measure and an environment health/urgency measure. These match directly with the two control loops within the AE, and the self-awareness and environment awareness properties. 
       FIG. 27  is a flowchart of a method  2700  of autonomic communication by an autonomic element. Method  2700  may include transmitting  2702  event message data in addition to the self and environment health/urgency data. Event message data can include data describing a change in condition, or a deviation from a normal operation. Event message data is described in more detail above in  FIG. 14 . 
     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. 28  is a flowchart of a method  2800  of autonomic communication by an autonomic element. Method  2800  may include receiving  2802  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  1914  of the autonomic element  1900  of  FIG. 19  above. 
     Method  2800  may also include receiving  2804  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  1908  of the autonomic element  1900  of  FIG. 19  above. 
       FIG. 29  is a flowchart of a method  2900  of autonomic communication by an autonomic element. Method  2900  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  2900  may include transmitting uncompressed self health/urgency data  2902 . Method  2500  may also include transmitting  2904  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  2902  and  2904  can be performed in any order relative to each other. 
     CONCLUSION 
     A quiesce component of an autonomic unit can render the autonomic unit inactive for a specific amount of time or until a challenging situation has passed. Self-managing systems, whether viewed from the autonomic computing perspective, or from the perspective of another initiative, can offer a holistic vision for the development and evolution of computer-based systems that aims to bring new levels of automation and dependability to systems, while simultaneously hiding their complexity and reducing their total cost of ownership. 
     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 provides the required relationships. 
     In particular, one of skill in the art will readily appreciate that the names of the methods and apparatus are not intended to limit embodiments. Furthermore, additional methods and apparatus can be added to the components, functions can be rearranged among the components, and new components to correspond to future enhancements and physical devices used in embodiments can be introduced without departing from the scope of embodiments. One of skill in the art will readily recognize that embodiments are applicable to future communication devices, different file systems, and new data types. 
     The terminology used in this application is meant to include all environments and alternate technologies which provide the same functionality as described herein.