Abstract:
A method for performing combinations of functions which can heuristically modify the functional components of the machine, separate such functional components down to the device level, and optimize the distribution of information between the functional components, by defining the domains for the network, acquiring data for each domain at each of a plurality of points in time, creating repetition clusters by looking for combinations of data that recur, identifying action to be performed based on the repetition clusters; and acquiring data from the consequences of actions performed.

Description:
COMPACT-DISC APPENDIX 
     Two Compact Disc-Recordables (CD-Rs, Copy 1 and Copy 2), containing a computer program listing, constitute a part of the specification of this invention pursuant to 37 C.F.R. 1.77 and 1.96, and is incorporated by reference herein for all purposes. Each CD-R includes an identical single file named APPEND.TXT.;1 which conforms to the ISO 9660 standard, was created on the CD-R on Jul. 9, 2001, and contains 338 kilobytes on the CD-R (actual size of the file is 345,469 bytes). 
     FIELD OF THE INVENTION 
     The invention relates generally to a method and system for managing complex telecommunications networks and, more particularly, to performing extremely complex combinations of functions in which the functions can be modified by the system in response to experience. 
     BACKGROUND OF THE INVENTION 
     There are severe constraints on the architecture of systems which perform complex combinations of functionality using large numbers of devices. These constraints derive from the need to construct, repair and modify the system. Such needs require a means to relate functionality at a high level to the functionality of individual devices and groups of devices. Such systems are therefore constrained to adopt a simple functional architecture. In a functional architecture, functionality is partitioned into components and the components into subcomponents through a number of levels down to the device level. In a simple functional architecture, the components on one level perform roughly equal proportions of functionality, and the necessary information exchange between components is minimized. 
     The predominant functional architecture is the instruction architecture in which the information exchanged between functional components is unambiguous to the receiving component. Use of unambiguous information results in the memory/processing separations observed in commercial electronic systems. The von Neumann architecture is a special case of the instruction architecture in which functionality is coded as unambiguous information. A drawback with the instruction architecture, and with the von Neumann architecture in particular, is that functionality must be performed sequentially and, furthermore, it is difficult to construct systems which heuristically modify their own functionality. 
     Neural networks have attempted to overcome the drawbacks of the instruction architecture, but they make the use of unambiguous information from instruction architecture design approaches. As a result, neural networks cannot scale up from the device level to a higher level of heuristically modified, complex functionality. 
     Therefore, what is needed is an architecture which can scale up to complex functionality, and which is not difficult to construct, repair, and modify. 
     SUMMARY OF THE INVENTION 
     The invention includes a method and system for performing combinations of functions which can heuristically modify the functional components of the machine, separate such functional components down to the device level, and optimize the distribution of information between the functional components, by defining the domains for the network, acquiring data for each domain at each of a plurality of points in time, creating repetition clusters by looking for combinations of data that recur, identifying action to be performed based on the repetition clusters; and acquiring data from the consequences of actions performed. 
    
    
     BRIEF DESCRIPTION OF THE DRAWINGS 
     The following Detailed Description makes reference to FIGS. 1 through 13, which are briefly described as follows: 
     FIG. 1 is a schematic diagram showing an architecture embodying features of the present invention; 
     FIG. 2 is a schematic diagram showing the architecture of a clustering function shown in FIG. 1; 
     FIG. 3 is a schematic diagram showing the architecture of a supercluster shown in FIG. 2; 
     FIG. 4 is a schematic diagram showing the architecture of a cluster shown in FIG. 3; 
     FIGS. 5 and 5A is a schematic diagram showing the architecture of a device shown in FIG. 4; 
     FIG. 6 is a flow chart illustrating a preferred method for generally implementing the architecture of the present invention; 
     FIG. 7 is a flow chart illustrating an active mode utilized by the system of the present invention; 
     FIG. 8 is a flow chart illustrating the operation of the clustering functions during the active mode shown in FIG. 7; 
     FIG. 9 is a flow chart illustrating the operation of a portion of the operation shown in FIG. 8; 
     FIGS.  10 ( a ),  10 ( b ), and  11  are flow charts illustrating the operation of clusters referred to in FIG. 9; 
     FIGS. 12-13 are flow charts illustrating a sleep mode utilized by the system of the present invention; 
     FIG. 14 is a table depicting the initial condition of a cluster in which all of the devices within the cluster are virgin devices; and 
     FIG. 15 is a table depicting a subsequent condition of a cluster in which there are a number of regular devices and relatively fewer virgin devices are present. 
    
    
     DESCRIPTION OF THE PREFERRED EMBODIMENTS 
     Referring to FIG. 1 of the drawings, the reference numeral  10  generally designates a network, such as a telephone and/or data network, which performs a complex combination of functionality using a large number of devices. The network  10  includes three information domains  12 ,  14 , and  16 , which provide information such as node location data, routing table data, traffic measurement data on selected routes between nodes, physical hardware layout, quality of service, combinations thereof, and the like, for monitoring the network. The domains  12 ,  14 , and  16  may provide information which is unique to a respective domain, or the information may overlap, and the domains may be demarcated based upon the function of the information provided, the type of information provided, and/or by geography, and the like. The number of domains may also vary from three domains. 
     The network  10  is connected in a manner well known to those skilled in the art via a bus  18  connecting the information domains  12 ,  14 , and  16  to a control system  20  comprising a recommendation architecture embodying features of the present invention. The control system  20  includes a first action clustering function  22 , described below, connected via the bus  18  for receiving input from the information domains  12 ,  14 , and  16  about the network  10 . The clustering function  22  is connected for generating a plurality of output “recommendation” signals, each of which may comprise a plurality of bits, via a plurality of lines  24  (only four of which are shown) to a first competition function  26 , described below, and to gates  28  configured for permitting a single recommendation signal selected by the competition function  24  to pass via a line  30  to a second action clustering function  32 . In a similar manner, a second action clustering function  32  is connected for generating a plurality of output recommendation signals via a plurality of lines  34  to a second competition function  36  and to gates  38  configured for permitting a single recommendation signal selected by the competition function  34  to pass via a line  40  to a third action clustering function  42 . Similarly, the third clustering function  42  is connected for generating a plurality of output recommendation signals via a plurality of lines  44  to a second competition function  46  and to gates  48  configured for permitting a single recommendation signal selected by the competition function  44  to pass via a line  50  to the network  10  for controlling the network  10 . 
     The system  20  includes an action type priority clustering function  52  which is connected via the bus  18  to the information domains  12 ,  14 , and  16 . The action type priority clustering function  52  is configured for identifying internal needs of the system  20  such as cost effectiveness, labor redeployment, and the like, and for generating signals via a line  54  to the new second action clustering function  32  to bias the probability of action recommendations in response to the internal needs identified from the incoming data. 
     A resource management clustering function  56  is connected via the line  58  to the second action clustering function  32  for managing resources, such as clusters and devices discussed below, of the second action clustering function  32 . Such a function is required by any action clustering function which heuristically modifies its own functionality. 
     An action analysis function  60  is connected via the line  62  to the gates  38  for receiving and analyzing a recommendation selected by the second competition function  36 . The action analysis function  60  generates signals on the line  64  which simulate a condition in which the recommended action has been taken. The signals generated by the action analysis function  60  compete in the first competition function  26  for access through the gates  28 , where they combine with signals from the first action clustering function  22 , also received through the gates  28 , to generate a modified recommendation to the second action clustering function  32 . 
     The system  20  also includes three feedback functions,  70 ,  72 , and  74 , described further below, which are connected via the bus  18  to the information domains  12 ,  14 , and  16 . The function  70  is an attention management function which is connected via the line  84  to modulate the relative probability of recommendations on the lines  24  to pay attention to a different set of inputs gaining access through the gates  28  to the second action clustering function  32 . The function  72  is a selection management function connected via a line  82  to modulate the relative probability of recommendations on the lines  34  to perform different actions on the network  10  gaining access through the gates  38  to the third action clustering function  62 . The function  74  is an integration management function connected via a line  80  to modulate the relative probability of recommendations to integrate different action elements in different ways to perform the accepted action recommendation. 
     A consequences management function  76  is a clustering function connected to the bus  18  for receiving information indicating the state of the network  10  after an action has been taken. The function  76  generates signals on lines  86 ,  88  and  90  which modulate the probability of future input information similar of input information being similar to recent inputs generating actions through the gates  28 ,  38 , and  48  similar to recently accepted action. 
     The general architecture of each of the clustering functions  22 ,  32 ,  42 ,  52 ,  56 ,  20   70 ,  72 ,  74 , and  76  is substantially similar to each other and, for the sake of conciseness, is depicted representatively by the clustering function  22  in FIG.  2 . As exemplified therein, the clustering function  22  includes four superclusters  202 ,  204 ,  206  and  208 , described further below. While only four superclusters are shown in FIG. 2, the number of superclusters included within a clustering function may vary. Each of the clustering functions  22 ,  32 , 42 ,  52 ,  56 ,  70 ,  72 ,  74 , and  76  generate recommendations which may perform an action on the network  10  or the system  20 , and such recommendations pass through a separate competition function (not shown with respect to the clustering functions  52 ,  56 ,  70 ,  72 ,  74 , and  76 ) before being accepted. 
     The general architecture of each of the superclusters  202 ,  204 ,  206 , and  208  is substantially similar to each other and, for the sake of conciseness, is depicted representatively by the superclusters  202  in FIG.  3 . As exemplified therein, the supercluster  202  includes four levels  302 ,  304 ,  306 , and  308 , each of which levels comprises a plurality of clusters  310 , described in further detail below. While four levels of clusters are shown, the number of clusters included within the supercluster  202  may vary. In the operation of the clustering function  202 , the clusters  310  of one level, such as the level  302 , are interconnected with clusters on a next generally lower level (as viewed in FIG.  3 ), such as the level  304 , as exemplified by the lines  312 . Furthermore, a feedback loop  314  provides for interconnecting the output of the level  308  of clusters to a generally higher level (as viewed in FIG. 3) of devices, described below, of the level  302  of clusters  310 . As indicated by the dashed lines  316 ,  318 , and  320 , the feedback loop  314  may also direct the output of the level  308  of clusters  310  to a level of devices of the level  304 ,  306 , and/or  308  of clusters. 
     FIG. 4 shows the architecture of a cluster  310 . As exemplified therein, the cluster  310  preferably includes five functional layers, namely, an “alpha” layer  402 , an “epsilon one” (referred to hereinafter as “ε1”) layer  404 , an “epsilon two” (referred to hereinafter as “ε2”) layer  406 , a “beta” layer  408 , and a “gamma” layer  410 , each of which layers preferably comprises a large number of “repetition” devices (six of which are shown in FIG. 4) for each layer, wherein the devices are either “virgin” devices such as device  412  (shown with a “V” imprinted on the device) or are “regular” devices such as device  414 . While five functional layers of six devices each are shown, the number of layers and devices in each layer may vary. While not shown, all devices initially virgin devices  412  and there is no interconnection between them. Subsequent to the operation of the cluster  310 , the virgin devices  412  become regular devices  414 , as shown in FIG. 4, and are interconnected in a manner described below. 
     FIG. 5 shows details of a virgin device  412 , which is representative of a regular device  414  as well. The device  412  includes a body  500 , a plurality of input lines  502 ,  504 ,  506 ,  508 , and  510 , and a single output line  512 . The input lines  502  are connected to the outputs of devices  414  in the gamma layer  410  of another, generally previous, cluster  310  or layer of regular devices  414 . The input lines  504  are connected for receiving beta output from the same cluster, as described below, and the input lines  506  are connected for receiving gamma output from the same cluster. The input lines  508  are preferably used only in connection with devices in the ε 1  layer  404  and are connected for receiving feedback via the feedback line  314  shown in FIG. 3 from the gamma outputs from higher clusters. 
     The line  510  is connected for receiving data on the line  54  to modulate a threshold which determines whether input received on the input lines  502  is sufficient to generate a signal on the output line  512 . The output signal generated on the line  512  may be any of a number of different types of signals, such as, for example, a signal spike, a series of spikes occurring at a regular rate over an interval, a series of spikes with a regularly modulated rate of occurrence over an interval, or the like. The output signal generated on the line  512  is distributed to many of the devices  412  on a subsequent layer. 
     In one implementation of the device  412 , signals on the input lines  502 ,  504 ,  506 ,  508 , and  510  are received on different buses containing lines that are each divided into time slots. The presence and nature of all possible inputs to the lines  502 , for example, are indicated by the contents of a particular time slot. Memory  516  indicates which time slots correspond to which inputs connected to devices  412  and also contains the value of the threshold value to be used. The logic module  518  determines each event of the number of inputs activated, plus a weighted number of beta signals received, less a weighted number of gamma signals received during the predefined time interval controlled by timer exceeding the threshold value stored in the memory. When such an event is identified by the logic module  518 , an output on the line  512  is generated. A timer  520  is positioned in the body  500  for providing a time interval for receiving input and to compare with the threshold value stored in the memory  516  to determine whether the threshold value is exceeded. Such a timer  520  is well known, for example, in the implementation of neural networks, and will therefore not be described in further detail. 
     Referring again to FIG. 4, the input lines  502  (FIG. 5) of each virgin device  412  in a layer, such as the layer  404 , are provisionally connected to one or more output lines  512  (FIG. 5) of a device in a previous layer, such as the layer  402 . Subsequent to the operation of the cluster  310 , the input line  512  (FIG. 5) of each regular device  414  in a layer such as the layer  404  (FIG. 4) is permanently connected, or “imprinted,” to one or more output lines  512  of a device in a previous layer such as the layer  402 . As discussed above, the input lines  502  of the devices in the alpha layer  402  may be connected to the output lines  512  of devices in previous clusters  310  (FIG.  3 ). 
     A beta output line  420  is connected to the output  512  of each regular device  414  on the beta layer  408  of the cluster  310 , and also to the beta input lines  504  of each virgin device  412 . Similarly, a gamma output line  422  is connected to the output  512  of each regular device  414  on the gamma layer  410  of the cluster  310 , and also to the gamma input lines  506  of each virgin device  412 . An external line  424  is connected to the output  512  of each regular device  414  on the ε 2  layer  406  of the cluster  310 . The external line  424  is also externally connected (i.e., outside of the cluster  310 ) as described below for inhibiting the formation of new clusters  310 . The ε 1  level receives inputs from the gamma output level of higher level clusters in FIG. 3 via lines  314 ,  316 ,  318  illustrated. These inputs are received by devices in ε 1  which were imprinted at the same time as the gamma devices which are the source of the outputs. If an ε 1  device is already producing an output and receives input of this type, it will continue to produce output. If it is producing output and does not receive this type of input it will stop producing output. This mechanism is only required if devices are programmed with multiple repetitions, and the function of the mechanism is to converge to the device activations within a supercluster on a set which have tended to be imprinted at similar times in the past. The functionality of a system with one repetition per device can be equivalent to one with multiple repetitions per device, the reason for multiple repetitions per device is to achieve more economical use of device resources. 
     An epsilon  2  output line  424  is connected for receiving the outputs generated by the devices  412  and  414  positioned in the epsilon to layer  406 . The epsilon output line  424  is also connected to the resource management cluster and function  56 . If the value on the line  424  exceeds a predetermined threshold value, then the resource management cluster function  56  inhibits the configuration of the virgin clusters when the system  20  is in the sleep mode. 
     While not shown, the layers of devices depicted in the cluster  310  of FIG. 4, may be subdivided into sublayers wherein the output  512  of devices  412  on one sublayer provide the input to devices on another sublayer within the same layer. 
     FIG. 5A schematically depicts the operation and interaction of the competition functions  26 ,  36 , and  46 . Because the competition functions  26 ,  36 , and  46  are substantially similar, for the sake of conciseness, they will be described representatively by reference to the competition function  26 . Accordingly, the competition function  26  comprises a plurality of channels  530 , three of which are shown in FIG.  5 A. Each channel  530  in me competitive function  26  is connected for receiving an input, representative of an action recommendation, from only one supercluster of a clustering function. As exemplified in FIG. 5A, the channels  530  are connected for receiving inputs via the lines  24  from a supercluster  202  of the clustering functions  22 . While not shown, additional channels  530  within the competition function  26  may be connected for receiving additional inputs from additional superclusters of the same or additional clustering functions. 
     Each channel  530  of the competition function  26  comprises a plurality of layers of devices  412  and  414  interconnected similarly to the clusters depicted in FIG. 4 for stimulating the generation of output from forward layers within a respective channel and, additionally, for inhibiting, via inhibitive connections  534 , the generation of output in all other channels. If, for example, inputs enter from three superclusters  202  of the first clustering function  22 , corresponding with three alternative action recomendations, device outputs will be produced through several layers in each channel  530 . The inhibitive connections  534  between channels  530  reduce the level of device activity in successive layers and, as shown by an arrow  532 , a modulation function (not shown) applies a general adjustment to the level of all device thresholds until one and only one channel generates an output. Such generated output is used to open a corresponding gate  28  (FIG. 1) controlled by the competitive function  26  to allow all output from one corresponding supercluster (e.g., the supercluster  80 ) to proceed as input to the next clustering function (i.e., the second action clustering function  32  in the present example). 
     The consequences management function  76  clusters input generated from the system  20  after an action has been taken on the network  10 , and recommends either increasing or decreasing the strength of inhibitive connections which were active in the aforementioned competitive selection, thereby effectively modulating the probability that a similar action will be selected under similar circumstances in the subsequent competitions. The construction of a competitive function, such as the competitive function  26 , may employ conventional neural network techniques which are well-known in the art, and will therefore not be discussed in further detail herein 
     The foregoing embodiment of the control system  20  may be implemented in either hardware, software, or a combination thereof Because the implementation details of the control system  20  are considered to be known to those skilled in the art based upon a review of the present description of the invention, the control system  20  will not be described in further detail herein. 
     Connection of the outputs  512  with the inputs  502  of the devices  412  and  414  within the cluster  310  is depicted in FIG. 4 is shown in FIGS. 14 and 15. FIG. 14 depicts the initial condition of the cluster  310  in which all of the devices within the cluster  310  are virgin devices  412 . Since all devices are virgin devices  412 , there are no beta or gamma signals (i.e., signals output from regular devices  414  in the beta or gamma layers  408  or  410 , respectively) generated to either stimulate or inhibit the making of connections. Therefore, when a threshold of, for example, half, or three out of six inputs are activated, then virgin devices  412  are imprinted. 
     As shown m FIG. 14, there are 20 inputs  502  which may possibly be imprinted. Six inputs,  2 ,  7 ,  12 ,  14 ,  18 , and  19  from the alpha layer  402  of a cluster  310  in the first action clustering function  22  are selected at random to be provisionally connected to data received from the bus  18 . In the first two presentations shown less than three of the six selected inputs are activated and hence there is no imprinting. In the third presentation, three inputs,  2 ,  12 , and  14 , are activated; therefore, those three inputs are permanently connected, i.e., imprinted, and the virgin device  412  becomes a regular device  414 . In presentations  4  and  5 , there are three inputs which are activated, but since the activated inputs are not the imprinted inputs, a signal is not generated to the output line  512 . In presentation  6 , the three imprinted inputs  2 ,  12 , and  14  are activated and, as a result, a signal is generated onto the output line  512 , i.e., the device  414  fires. In presentation  7 , the three imprinted inputs are activated and a fourth provisionally connected input  19  is also activated; therefore, the device  414  fires and the fourth input is also imprinted, thereby increasing the probability that the device will fire in the future when any three of the four imprinted inputs are activated. In presentation  9 , three of four imprinted inputs are activated thereby causing the device  414  to fire. And in presentation  10 , no three of the four imprinted inputs are activated, thereby resulting in the device  414  not firing. It is noted that because the device fires when imprinted inputs are activated with the same data that resulted in them being imprinted, such data is referred to herein as a repetition of such data. 
     FIG. 15 depicts a subsequent condition of the cluster  310  in which there are a number of regular devices  414  and relatively fewer virgin devices  412  are present. As shown, eight inputs are provisionally connected to a virgin device  412 . In presentation  1 , a strong gamma signal received on the line  506  and a strong beta signal received on the line  504  are present with seven active inputs. The strong beta signal stimulates the imprinting of the provisional connections is offset by the strong gamma signal which inhibits imprinting; therefore, notwithstanding the seven activated inputs, there is no imprinting and no firing from the virgin device  412 . In presentation  2 , there is a weak or no beta signal to the device, with a strong gamma signal to inhibit imprinting; therefore, there is no imprinting. As noted in FIG. 15, notwithstanding the number of activated inputs, if the gamma signal to inhibit imprinting is strong, then no imprinting occurs. In presentation  3 , there is a strong beta signal to stimulate imprinting, and a weak gamma signal to inhibit imprinting, and four activated inputs; therefore, the four activated inputs are imprinted. A threshold is also set within the memory  516  of the device to be less than or equal to the number of inputs imprinted, such as a threshold of three, and the device becomes a regular device  414 . Subsequent to presentation  3 , wherein the activated inputs were imprinted, the strength of the beta and gamma signals is irrelevant to whether the device fires. Accordingly, in presentations  4  and  6 , less than three inputs are imprinted and so there is no firing. And in presentation  5 , regardless of the beta and gamma signals, the device fires because three inputs are activated, the device fires. 
     It is noted with respect to the foregoing in FIG. 15, that if the output of a virgin device  412  in the alpha layer  402  is provisionally connected to the input of a regular device  414  in the next layer of devices, and the regular device  414  is firing at the same time the virgin device  412  is imprinted, then the provisional connection becomes permanent. Alternatively, if the regular device  414  does not fire at the same time the virgin device  412  is imprinted, then the provisional connection is deleted. 
     FIGS. 6-13 are flow charts of a method of implementing the system  20  in accordance with the above-described features of the present invention. Referring to FIG. 6, operation of the control system  20  generally alternates between an “active” mode and a “sleep” mode. As depicted in FIG. 6, operation of the system  20  may be considered to begin at step  600  by entering into the active mode. During the active mode, operation proceeds to step  602  which is shown in greater detail in FIG.  7 . 
     Referring to FIG. 7, operation in the active mode proceeds to steps  700  and  702  which are performed in parallel. Beginning with step  702 , information about the network  10  from the information domains  12 ,  14 , and  16 , is presented via the bus  18  to the first action clustering function  22 . The presentation of such information will be referred to herein as a data repetition. Recommendations performed by the system  20  will occur, based upon a repetition of the information. 
     As shown more clearly in FIG. 8, the step  702  is performed in parallel by steps  802 ,  804 ,  806 , and  808 , wherein the information set is presented to each of the superclusters of a clustering function, such as the four superclusters  202 ,  204 ,  206  and  208  shown in FIG.  2 . Execution of the steps  802 ,  804 ,  806 , and  808  is shown in greater in FIG.  9 . 
     With reference to FIG. 9, execution proceeds from a respective step  802 ,  804 ,  806 , or  808  to step  906  wherein repetition input received from the bus  18  is presented to all clusters  310  in the first cluster level  302  (FIG. 3) of a supercluster  202  (FIG.  2 ). The clusters in the first cluster level  302  then produce repetition output which, in step  908 , is input to all clusters  310  in the second cluster level  304  (FIG.  3 ). The clusters in the second cluster level  304  then produce repetition output which, in step  910 , is input to all clusters  310  in the third cluster level  306  (FIG.  3 ). The clusters in the third cluster level  306  then produce repetition output which, in step  912 , is input to all clusters  310  in the fourth cluster level  308  (FIG.  3 ). 
     Upon completion of step  912 , execution proceeds to step  904 , wherein a determination is made whether each of the regular (non-virgin) devices  414  in the supercluster  202  contains multiple repetitions. If it is determined that there each of the devices  412  contains multiple repetitions, then execution proceeds to step  914 ; otherwise, execution proceeds to step  922 . 
     In step  914 , consistency output is presented from the gamma layer  410  of the fourth level  308  to the ε 1  layer of the first level  302  of the supercluster  202  shown in FIGS. 3-4. In step  916 , a determination is made whether consistency input is present in the first level  302  or if no output is present in any cluster in the fourth level  308 . If in step  916 , a determination is made that consistency input is not present in the first level  302  and that output is present in at least one cluster in the fourth level  308 , then execution returns to step  914 . If in step  916 , a determination is made either that consistency input is present in he first level  302  or that no output is present in at least one cluster in the fourth level  308 , then execution proceeds to step  922  to imprint at every level of the supercluster  202  (FIG. 3) until a minimum output is present at the fourth level  308  of the supercluster  202 . The step  922  is described in greater detail below with respect to FIG.  10 A. 
     Referring to FIG. 10A, execution of step  922  begins at step  1000  and proceeds to step  1001  wherein a determination is made whether the supercluster  202  has generated any output. If it is determined that output has been generated by the supercluster  202 , then execution proceeds to step  1002 , wherein the generated output is sent to the next competitive function (described above with respect to FIG.  5 A). Upon completion of step  1002 , execution returns to step  922  and then proceeds to step  926  of FIG.  9 . 
     If, in step  1000 , it is determined that no output has been generated by the supercluster  202 , then execution proceeds to step  1004 , wherein a determination is made whether the virgin device  412  minimum thresholds have been reached. If it is determined that the minimum virgin device thresholds have been reached, then no output is sent to the next competitive function (FIG.  5 A), and execution returns to step  927  and then proceeds to step  926  of FIG.  9 . 
     If, in step  1004 , it is determined that the virgin device  412  minimum thresholds have not been reached, then execution proceeds to step  1008 , wherein the minimum threshold is lowered on all virgin devices  412 . Upon completion of step  1008 , execution proceeds to step  1010  wherein all levels of the supercluster  202  are imprinted. The step  1010  is described in greater detail below with respect to FIG.  10 B. 
     Referring to FIG. 10B, execution of step  1010  begins at step  1020  and proceeds to step  1022  wherein a level pointer is set to the first level  302  of the supercluster  202 . In step  1024 , a determination is made whether the beta output from one or more clusters  310  in the current level of the supercluster  202  exceeds the cluster similarity criterion. If the beta output does not exceed the cluster similarity criterion, then execution proceeds to step  1026  wherein a determination is made whether the alpha or the epsilon output exceed the familiarity criterion in one or more clusters in the current level within the supercluster  202 . If the alpha or the epsilon output exceed the familiarity criterion, then execution proceeds to step  1028  wherein a record is made that one instance of cluster inhibitions has occurred for resource programming phase. In step  1030 , the level pointer is incremented and execution returns to step  1024 . 
     If, in step  1026 , it is determined that no alpha or the epsilon output exceeds the familiarity criterion in one or more clusters in the current level within the supercluster  202 , then execution proceeds to step  1032 . In step  1032 , a determination is made whether the input to the current level contains any bias information from any available new cluster. If the input does not contain any such bias information then execution proceeds to step  1034 , wherein the level input data is recorded for future clusters. Execution then proceeds from the step  1034  to the step  1030 , discussed above. 
     If, in step  1032 , it is determined that the input does contain bias information, then execution proceeds to step  1036 , wherein the output from the new cluster is imprinted and produced. Execution then proceeds to step  1040 , discussed below. 
     If, in step  1024 , it is determined that the beta output from one or more clusters  310  in the current level of the supercluster  202  exceeds the cluster similarity criterion, then execution proceeds to step  1038 , wherein the clusters are imprinted to produce larger outputs. The step  1038  is described in greater detail below with respect to FIG.  11 . 
     Referring to FIG. 11, execution of step  1010  begins at step  1100  wherein the thresholds of the virgin devices  412 . 
     Referring to FIG. 11, at step  1100 , the thresholds of the virgin devices  412  are reduced, and in step  1102 , input is presented to devices  412  in the alpha layer  402 . At step  1104 , a determination is made whether the threshold value stored in the memory  516  of the virgin devices  412  in the alpha layer  402  is below a notional (effective) infinity. If at the step  1104 , a determination is made that the threshold value stored in the memory  516  of the virgin devices  412  in the alpha layer  402  is not below a notional infinity, then no output is delivered and operation returns to step  906  in FIG.  9 . If at the step  1104 , a determination is made that the threshold value stored in the memory  516  of the virgin devices  412  in the alpha layer  402  is below a notional infinity, then operation proceeds to step  1108 , wherein a determination is made whether the number of active inputs is less than the current threshold in the devices  412  on the alpha layer  402 . If a determination is made that the number of active inputs is less than the current threshold in the devices  412 , then operation proceeds to step  1106  wherein no output is delivered and operation returns to step  906  in FIG.  9 . If a determination is made that the number of active inputs is not less than the current threshold in the devices  412 , then operation proceeds to step  1110  and output is generated from the virgin devices  412  at the alpha layer  402 . Operation then proceeds to step  1112 , in which all inactive inputs at the alpha layer  402  are deleted, and to step  1114  in which the alpha threshold is set slightly below the current total inputs. 
     Upon execution of step  1114 , operation proceeds to step  1122  wherein input is presented to devices  412  in the beta layer  408 . At step  1124 , a determination is made whether the threshold value stored in the memory  516  of the virgin devices  412  in the beta layer  408  is below a notional infinity. If at the step  1124 , a determination is made that the threshold value stored in the memory  516  of the virgin devices  412  in the beta layer  408  is not below a notional infinity, then no output is delivered and operation returns to step  906  in FIG.  9 . If at the step  1124 , a determination is made that the threshold value stored in the memory  516  of the virgin devices  412  in the beta layer  408  is below a notional infinity, then operation proceeds to step  1128  wherein a determination is made whether the number of active inputs is less than the current threshold in the devices  412  on the beta layer  402 . If a determination is made that the number of active inputs is less than the current threshold in the devices  412 , then operation proceeds to step  1126  wherein no output is delivered and operation returns to step  906  in FIG.  9 . If a determination is made that the number of active inputs is not less than the current threshold in the devices  412 , then operation proceeds to step  1130  and output is generated from the virgin devices  412  at the beta layer  408 . Operation then proceeds to step  1132  in which all inactive inputs at the beta layer  408  are deleted, and to step  1134  in which the beta threshold is set slightly below the current total inputs. 
     Upon execution of step  1134 , operation proceeds to step  1142  wherein input is presented to devices  412  in the gamma layer  410 . At step  1144 , a determination is made whether the threshold value stored in the memory  516  of the virgin devices  412  in the gamma layer  410  is below a notional infinity. If at the step  1144 , a determination is made that the threshold value stored in the memory  516  of the virgin devices  412  in the gamma layer  410  is not below a notional infinity, then no output is delivered and operation returns to step  906  in FIG.  9 . If at the step  1144 , a determination is made that the threshold value stored in the memory  516  of the virgin devices  412  in the beta layer  408  is below a notional infinity, then operation proceeds to step  1148  wherein a determination is made whether the number of active inputs is less than the current threshold in the devices  412  on the gamma layer  410 . If a determination is made that the number of active inputs is less than the current threshold in the devices  412 , then operation proceeds to step  1146  wherein no output is delivered and operation returns to step  906  in FIG.  9 . If a determination is made that the number of active inputs is not less than the current threshold in the devices  412 , then operation proceeds to step  1150  and output is generated from the virgin devices  412  at the gamma layer  410 . Operation then proceeds to step  1152  in which all inactive inputs at the gamma layer  410  are deleted, and to step  1154  in which the gamma threshold is set slightly below the current total inputs. 
     It is noted that, while the foregoing steps  1102 - 1114 ,  1122 - 1134 , and  1142 - 1154  have been performed with respect to the alpha, beta, and gamma layers, they may also be performed with respect to the s 1  and s 2  layers, 404  and  406 , respectively, shown in FIG.  4 . 
     Upon execution of the step  1154 , execution proceeds to step  1156  wherein a determination is made whether gamma output is generated. If in the step  1156 , it is determined that gamma output is generated, then execution proceeds to step  1008  of FIG.  10 . If in the step  1156 , it is determined that gamma output is not generated, then execution proceeds to step  1158  in which a determination is made whether virgin device thresholds have reached a minimum value. If in step  1158 , a determination is made that virgin device thresholds have not reached a minimum value, then execution returns to step  1100 . If in step  1158 , a determination is made that virgin device thresholds have reached a minimum value, then execution returns to step  906  of FIG.  9 . 
     Upon return from step  1156  to step  1039  in FIG. 10B, execution proceeds to step  1040 , wherein the larger output is sent to the next level of the supercluster  202 . Execution then proceeds to step  1042  wherein a determination is made whether the current level is the last level. If it is determined that the current level is not the last level, then execution proceeds to step  1044 , wherein a determination is made whether the beta output exceeds the cluster similarity criterion in one or more current clusters in the last level. If it is determined that the beta output exceeds such similarity criterion, then execution returns to step  1038 ; otherwise execution returns to step  1026 . If, in step  1042 , it is determined that the current level in the supercluster is the last level, then execution proceeds to step  1010  and returns to step  1001  of FIG.  10 . 
     Upon completion of step  922 , execution proceeds to step  926 , in which output generated by the fourth level  308  is presented to the first competition function  26  shown in FIG.  1 . Upon execution of step  926 , execution proceeds to a respective step  812 ,  814 ,  816 , or  818  (FIG. 8) corresponding to the step  802 ,  804 ,  806 , or  808  (FIG. 8) from which entry was made to the step  906  of FIG.  9 . 
     Upon execution of steps  812 ,  814 ,  816 , and  818 , execution proceeds to step  820  wherein output from all superclusters  202 - 208  is presented to its respective next competition function. At step  822 , the competition function  26  selects the output from one supercluster and presents that output to the next clustering function. Upon execution of the step  822 , execution proceeds to the step  704  of FIG.  7 . 
     Referring to FIG. 7, at step  704 , competition between outputs resulting in one output set (corresponding to one condition) id presented to the second clustering function. In step  706 , selected output from the first clustering function  26  is presented to the second clustering function  32 . In step  708 , competition between outputs from the second action clustering function  32  results in one output set (corresponding with one action recommendation) proceeding to the third clustering function  42 . While not shown in FIG. 7, steps  706 - 708  are performed in a similar manner that steps  702 - 704  were performed by executing steps depicted in FIG.  8 . 
     As discussed above, step  700  is executed in parallel with the step  702  to present one information set from preselected information, such as, to the action type priority clustering module  52 . Upon execution of step  700 , execution proceeds to step  710  wherein competition between outputs results in one output set (corresponding with a recommendation of a type of action) presented to the second action clustering function  32 . The output set generated in the step  710  is used in the steps  704 ,  706 , and  708  to modulate the relative probability of the type of conditions being selected, the superclusters producing strong recommendations to reduce the thresholds of devices in superclusters targeted by the output of action type priority clustering functions, and the action type being accepted. 
     Upon execution of steps  708  and  710 , execution proceeds to step  712  wherein a recommendation is made whether to the analyze the action recommended by the second clustering function  32 . If, in step  712 , a recommendation is made to the analyze the action recommended by the second clustering function  32 , then execution proceeds to step  714  wherein the recommendation to analyze is accepted, and to step  716  wherein devices  414  are activated if they have frequently been active in the past after the current recommendation type has been previously activated and accepted. Execution then proceeds to step  718 . If, in step  712 , a recommendation is not made to the analyze the action recommended by the second clustering function  32 , then execution proceeds to step  718 . 
     In step  718 , the selected output set is presented to the third clustering function  42  and, in step  720 , competition between outputs results in one output set (corresponding with an integrated action) which is presented as a system output (i.e., a system action) to the network  10 . While not shown in FIG. 7, steps  718 - 720  are performed in a similar manner that steps  702 - 704  were performed by executing steps depicted in FIG.  8 . 
     In step  722 , an information set (corresponding to the effectiveness and consequences of an action) is presented to the competition management clustering function  76 . In step  724 , the competition management functions are adjusted to modulate the probabilities of similar output sets winning subsequent competitions. Execution then proceeds to step  604  in FIG.  6 . 
     Referring to FIG. 6, in step  604 , a determination is made whether the number of virgin devices have been depleted to, for example, less than 10 percent of their value at the beginning of the current active mode. If, in step  604 , a determination is made that the number of virgin devices have not been depleted to less than 10 percent of their value at the beginning of the current active mode, then execution returns to step  602 . If, in step  604 , a determination is made that the number of virgin devices have been depleted to less than 10 percent of their value at the beginning of the current active mode, then execution proceeds to step  606 . 
     In step  606 , the system  20  enters into a sleep mode in which it, in step  608 , prepares resources for another active mode. The sleep mode is executed in FIGS. 12-13. 
     Referring to FIG. 12, in step  1200 , execution proceeds to the first supercluster  202  as the selected supercluster. In step  1202 , execution proceeds to the first level of clusters  310  in the selected supercluster  202 . In step  1204 , a determination is made whether there are a significant number of presentations in the series of presentations experienced in the previous active mode with ow alpha output and no available new clusters. If, in step  1204 , it is determined that there were a significant number of presentations in the series of presentations experienced in the previous active mode with low alpha output and no available new clusters, then execution proceeds to step  1206  wherein a new cluster is configured with random connectivity but with the alpha inputs biased toward inputs which frequently occurred together in such previous presentations. Execution then proceeds to step  1208  where attention is directed to the first cluster  310  of the selected supercluster  202 . If, in step  1204 , it is determined that there were not a significant number of presentations, in the series of presentations experienced in the previous active mode, with low alpha output and no available new clusters, then execution proceeds to step  1208 . 
     Upon execution of step  1208 , execution proceeds to step  1210  wherein a determination is made whether there are a significant number of presentations in the previous series of presentations with high alpha output, no gamma output from any cluster on the same level, and enough beta to trigger imprinting in any cluster. If, in step  1210 , it is determined that a significant number of presentations in the previous series of presentations have with high alpha output, no gamma output from any cluster on the same level, and enough beta to trigger imprinting in any cluster, then execution proceeds to step  1212  wherein the similarity criterion for the current cluster is lowered. Execution then proceeds to step  1214 . If, in step  1210 , it is determined that a significant number of presentations in the previous series of presentations did not have high alpha output, gamma output from any cluster on the same level, or not enough beta to trigger imprinting in any cluster, then execution proceeds to step  1214 . 
     At step  1214 , a determination is made whether the current cluster is the last cluster. If, at the step  1214 , it is determined that the current cluster is not the last cluster, then, in step  1216 , attention is directed to the next cluster, and execution returns to step  1210 . If, at the step  1214 , it is determined that the current cluster is the last cluster, then execution proceeds to step  1300  on FIG.  13 . 
     Referring to FIG. 13, at step  1300 , a determination is made of the number of virgin devices to be configured based on the number of virgin devices imprinted during the previous active mode. In step  1302 , a determination is made whether there are any clusters in one level  302 ,  304 , or  306  which frequently produced outputs at the same time that a cluster in the next level  304 ,  306 , or  308  produced outputs. If, in step  1302 , it is determined that there are clusters in one level  302 ,  304 , or  306  which frequently produced outputs at the same time that a cluster in the next level  304 ,  306 , or  308  produced outputs, then execution proceeds to step  1304  wherein the outputs from the cluster in potential virgin device inputs are included in clusters at the next level. Execution then proceeds to step  1306 . If, in step  1302 , it is determined that there are not clusters in one level  302 ,  304 , or  306  which frequently produced outputs at the same time that a cluster in the next level  304 ,  306 , or  308  produced outputs, then execution proceeds to step  1306 . 
     At step  1306 , a determination is made whether there are any clusters in one level which have outputs to a cluster in a next level and which rarely produce outputs at the same time that the target cluster produces outputs. If, in step  1306 , it is determined that there are clusters in one level which have outputs to a cluster in a next level and which rarely produce outputs at the same time that the target cluster produces outputs, then in step  1308 , the outputs are disconnected. Execution proceeds to step  1310 . If, in step  1306 , it is determined that there are no clusters in one level which have outputs to a cluster in a next level and which rarely produce outputs at the same time that the target cluster produces outputs, then execution proceeds to step  1310 . 
     At step  1310 , virgin devices  412  are configured with randomly selected inputs with a bias towards inputs which frequently contributed to firing regular devices in the previous active mode. Execution then proceeds to step  1312  in which a determination is made whether the current cluster is the last cluster. If, in step  1312 , it is determined that the current cluster is not the last cluster, then execution proceeds to step  1314  in which attention is directed to the next cluster, and then execution returns to step  1300 . If, in step  1312 , it is determined that the current cluster is the last cluster in the level of clusters, then execution proceeds to step  1316 , in which a determination is made whether the current level is the last level in the current supercluster. If, in step  1316 , it is determined that the current level is not the last level, then execution proceeds to step  1318  in which attention is directed to the next level, and then execution returns to step  1208  on FIG.  12 . If, in step  1316 , it is determined that the current level is the last level, then execution proceeds to step  1320  in which a determination is made whether the current supercluster is the last supercluster. If, in step  1320 , it is determined that the current supercluster is not the last supercluster in the level of clusters, then execution proceeds to step  1322  in which attention is directed to the next supercluster and execution proceeds to step  1202  on FIG.  12 . If, in step  1320 , it is determined that the current supercluster is the last supercluster, then execution returns to step  600  and the system re-enters into the active mode. 
     By the use of the present invention, managing complex telecommunications networks may be performed for extremely complex combinations of functions in which the functions can be modified by the system in response to experience. 
     It is understood that the present invention can take many forms and embodiments. Accordingly, several variations may be made in the foregoing without departing from the spirit or the scope of the invention. For example, the present invention may be used with any system that requires handling of large amounts of data such as searching the internet or large databases. It may also respond appropriately to voice communications, language translation, and operate machinery in a rapidly changing environment such as driving a car or operating weapons in battle. 
     In addition to the foregoing discussion and description, the present invention is further described and disclosed in an article entitled “The Pattern Extraction Architecture: A Connectionist Alternative to the Von Neumann Architecture” in Biological and Artificial Computation: from Neuroscience and Technology (1997), pp. 634-43, Berlin: Springer, which article is authored by L. Andrew Coward, the inventor of the present invention, and is hereby incorporated by reference in its entirety. 
     Having thus described the present invention by reference to certain of its preferred embodiments, it is noted that the embodiments disclosed are illustrative rather than limiting in nature and that a wide range of variations, modifications, changes, and substitutions are contemplated in the foregoing disclosure and, in some instances, some features of the present invention may be employed without a corresponding use of the other features. Accordingly, it is appropriate that the appended claims be construed broadly and in a manner consistent with the scope of the invention.