METHOD AND APPARATUS FOR ARCHITECTURE OF A KNOWLEDGE SYSTEM FOR MATHEMATIZATION OF KNOWLEDGE REPRESENTATION AND INTELLIGENT TASK PROCESSING

An apparatus including a computer processor; a computer memory; an input sensor; a device, wherein the device includes a motor; and a set of software controllable vote switches, wherein an end user can vote in a judicial matter. The computer processor communicates with the computer memory, the input sensor or vote switches, and the device. The computer memory includes computer software for controlling the motor of the device in response to one or more inputs from the input sensor or controlling a set of vote switches in response to end user entries in a judicial matter. The computer program may cause the computer processor to select random data which is a subset of known data, and based on this random data and the one or more inputs from the sensor or vote switches, the computer processor controls the motor of the device or makes a decision on a judicial matter.

FIELD OF THE INVENTION

This invention relates to artificial intelligence systems.

BACKGROUND OF THE INVENTION

A knowledge system (hereinafter KS) may be defined as a computer program for extending and querying a knowledge base. The knowledge base typically possesses specific information applicable to a particular domain but might also be constructed in a manner that affords domain independence.

Knowledge systems have evolved over time and with a variety of types and application areas with most knowledge systems having domain specific features. As discussed in U.S. Pat. No. 5,257,185, incorporated by reference herein, “Interactive, Cross-referenced Knowledge Systems” some examples of KS with domain specific features includes U.S. Pat. No. 4,591,983, incorporated by reference herein, describing a hierarchical KS for inventory control and processing orders and U.S. Pat. No. 4,648,037, incorporated by reference herein for end-user information retrieval and personal problem solving. However, the technique in U.S. Pat. No. 5,257,185, incorporated by reference herein, still requires a knowledge engineer to enter knowledge content into a knowledgebase.

All knowledge schemes mentioned above also lack a well defined knowledge representational scheme in addition to being restricted by the use of a human expert for knowledge entry.

More recent knowledge based systems employing state-of-the art real-time cortical learning algorithms such as in U.S. Pat. No. 2006/0184462, incorporated by reference herein “METHODS, ARCHITECTURE, AND APPARATUS FOR IMPLEMENTING MACHINE INTELLIGENCE AND HIERARCHICAL MEMORY SYSTEMS”, use data bits for knowledge representation and processing. In the aforementioned, knowledge is stored or memorized based on the requirement that an accumulation of active bits within several overlapping columns is a maximum at one or more winning columns meeting a threshold criteria.

Data bits however, have only two possible states which is insufficient for representing knowledge in its entirety—as knowledge is not simply true/false, 1/0 or ON/OFF states. This problem of knowledge definition and consequently its generation and representation remains a challenge for knowledge theorists, scientists and philosophers alike. Research in knowledge systems including its formulation and representations have also been interpreted in different ways and described using different theories. However, till this present date, no attempt has been made at devising a standard way of representing knowledge in a KS, not to mention its mathematization for real time applications.

More recently, an idea due to Rumsfeldian reasoning proposes a new school of thought that tries to capture the true essence of knowledge. For knowledge representation based on Rumsfeldian reasoning, a set of Known Known (KK), Known Unknown (KU) and Unknown Unknown (UU) things are defined. To further extend Rumsfeldian reasoning is to add an Unknown Known (UK) thing property giving a 4 state representation for knowledge.

In order to further the state-of-the-art in knowledge systems, a suitable KS representation would be very helpful. The current use of the bits as a unit for data representation in a KS is insufficient to deal with the true complexities inherent in knowledge and the many challenges faced by most KS warrants a better way of representing knowledge. More so, the bits representation though useful in most standard tasks, lacks depth and rigor for more complex tasks and cannot adequately and even approximately represent knowledge in sufficient detail. More recent research in knowledge theory has shown that knowledge exhibit several properties beyond simply true/false, ON/OFF or 1/0 states. It is therefore imperative to advance the state of knowledge representation in order to overcome the obvious limitations of current KS.

SUMMARY OF THE INVENTION

In at least one embodiment of the present invention, a knowledge system is provided with an invariant architecture trainable by a real-time learning Artificial Intelligence (AI) expert program for diverse domain independent problem solving tasks. Specifically, the present invention discloses novel methods and architecture for representing knowledge in its entirety, reducing the dimensionality in data, encoding, storing and retrieving granular knowledge patterns continually learnt by an AI expert.

The present invention provides a Knowledge System (KS) for constructing a set of representative knowledge units herein called “granules”. The granules can be integrated as base units in real-time artificial (machine) intelligence (AI) expert systems learning framework obviating the use of binary (data bit) representation in online (continual) learning systems.

At least one embodiment of the present invention may be provided in two parts: The knowledge representation part which involves assigning numeric values to KS units and transformation to granules via a hierarchical granular decomposition operation.

The AI (artificial intelligence) learning part which learns samples of sequences from knowledge sources based on rules imposed on the aforementioned part.

In at least one embodiment, an apparatus is provided comprising a computer processor; a computer memory; an input sensor; and a device, wherein the device includes a motor.

The computer processor is typically in communication with the computer memory, the input sensor, and the device. The computer memory typically includes computer software for controlling the motor of the device in response to one or more inputs from the input sensor, and in accordance with a computer program stored in the computer memory. In at least one embodiment, the computer program causes the computer processor to select random data which is a subset of known data, and based on the random data and the one or more inputs from the sensor, the computer processor controls the motor of the device.

In at least one embodiment, the device is an exercise treadmill, and the motor controls the speed of the exercise treadmill; and wherein the input sensor is a heart rate sensor which measures the heart rate of a person or a walking speed sensor that measures the walking speed of a person.

In at least one embodiment, a method is provided which may include selecting random data as a subset of known data from a computer memory using a computer processor; receiving one or more inputs from an input sensor; and controlling a motor of a device based on the random data and based on the one or more inputs from the input sensor The device may be an exercise treadmill, and the motor controls the speed of the exercise treadmill; and wherein the input sensor is a heart rate sensor which measures the heart rate of a person or a walking speed sensor that measures the walking speed of a person.

DETAILED DESCRIPTION OF THE DRAWINGS

Architecture for developing a new form of Knowledge System (KS), mathematization of KS representations and intelligent task processing with the invented KS are disclosed. Subsequently, specific taxonomy and particulars is put forth to give a more thorough understanding of the present invention. However, it will be obvious to one especially experienced and well informed in the art that these specific details are not required in order to practice the present invention. For instance, the present invention describes an invariant knowledge representation scheme based on the Rumsfeldian reasoning and trained by an AI expert for memory predictions and decision making. Nevertheless, other knowledge representations and training systems may be used to make predictions and decisions. The numerous teachings of the present invention are put forth with reference to an illustrative practical reactive treadmill speed control system. However, the ideas set forth in the present invention can be applied to any type of knowledge mining task providing an environment with a regular source of information.

Knowledge is a very important part of human life and existence. Real human knowledge can be stored (memorized), retrieved and processed just like in most typical computer based knowledge systems.

However, the operations in real human knowledge systems are different from computer based systems. The origins and interpretations thereof have eluded knowledge theorists, scientists across all domains and field of endeavor for centuries.

In one hypothesis of human knowledge, there exists the concept of the mind and things that are known, unknown or supposedly known or unknown. In another hypothesis, there exists the schema which can exist in diverse forms and generate abstract levels of thoughts from low level schemas.

As earlier highlighted in the background, attempts at developing knowledge systems have been made by several researchers with promising applications. However, no attempt has been made at representing knowledge in a form and manner that yields a mathematization of knowledge components for use in real time processing applications.

As knowledge cannot be gained without reasoning and a thought process, it should be advantageous if a system exists that can afford to exercise such functions. More so, it is highly desirous if such a knowledge system can be mathematized for real-time decision making applications.

Knowledge can be described in terms of Known and Unknown units. Knowledge is generally composed of a set of vague representations; overtime these vague or fuzzy representations become more regular.

A thought process may be described in terms of a state of known and unknown events. When this is the case we may refer to the known events as a Known-Known or “KK” events and the Unknown as Unknown-Unknown or “UU” events.

In 2006, the US secretary of state, Donald Rumsfeld made a remarkable and novel statement. He stated that there are “Known Knowns, Known-Unknowns, and Unknown Unknowns”. Rumsfeld further stated that the Known Knowns refer to the things we know we know, the Known Unknown are things we know we don't know, and the Unknown Unknown are the things we don't know we don't know. A lot of real world observations give credence to this theory of knowledge representation.

In one instance, there is the desire phenomenon which may be described as a sequence of fantasies which is an image of real things. As a consequence of the aforementioned, experience teaches us that there are things we have learnt but we do not know we know them. This gives us a fourth assumption and representation of knowledge in the “Unknown-Unknown”.

In another instance, information search and hence knowledge discovery teaches us that there are things we know through a finding out or questioning process. This is one aspect of interrogative learning.

FIG. 1shows a simplified block diagram of an apparatus1in accordance with an embodiment of the present invention. The apparatus1may include a computer memory2, a computer processor4, a user interactive device6, a display device8, a communications device10, a heart rate sensor12, a treadmill speed motor14, and a treadmill incline device or motor16. The components2,6,8,10,12,14, and16communicate with the computer processor4via communications links. The apparatus1may also include a walking speed sensor17, which may be used, in at least one embodiment, as an input to the computer processor4to control the incline device16and/or the speed motor14.

FIG. 2shows a simplified block diagram20explaining a method, apparatus, and/or process in accordance with an embodiment of the present invention. The block diagram shows KK data22or known known data, UU data24or unknown unknown data, KU data26or known unknown data, and UK data28or unknown known data. KK data22may be data which is known and stored in computer memory2ofFIG. 1. UK data28may be data which is randomly selected portion of the KK data22as shown by link22a.KU data26may be data which is a randomly selected portion of the KK data22as shown by link22b.UU data24may be data which is randomly derived from both the KU data26and the UK data28as shown by links26aand28a.

FIG. 3shows a flow chart of a method and apparatus100in accordance with an embodiment of the present invention.

Block or module102indicates a collection of data which may be called a “Knowledge Field” or KF, and which may correspond to the KK data22. For example, in at least one embodiment, the collection of data may be an array of fifty rows by three columns as shown for the Knowledge Field, KF, for the sample computer program from LINE 21 to before LINE 22, as shown at the end of this detailed description of invention.

Each row of the fifty rows of the knowledge field has a number indicating heart rate of a person on an exercise treadmill in a first column, a number indicating the incline of the exercise treadmill in a second column, and a number indicating the speed of the treadmill in a third column. For example, the first row in the sample program of the KF field indicates “150” (which is for beats per minute), “0” (which is for a zero degree inclination), and “3.6” (which is a measure of speed of the treadmill belt). The sample computer program may be stored in the computer memory2and executed by the computer processor4. The fifty rows by three columns of data may be stored in the computer memory2. Each row, represents data at a specific instance in time. The fifty rows show fifty different instances in time for the same treadmill. The data may be chronological. For example, the first row may be at a first time, and the next rows may be at increments of one second thereafter.

The collection of data of module102ofFIG. 3, which may be the fifty rows of the sample computer program, may be stored in the computer memory2ofFIG. 1.

Block or module104indicates UU (or Unknown-Unknown) data processor104. The UU processor104may be implemented by or part of the computer processor4and programmed by a computer program stored in computer memory2. The UU processor104may be programmed by computer software stored in the computer memory2to search through the data in KF module102and to select only a percentage of the plurality of samples (each sample includes all three field, i.e. each sample include a heart rate, an inclination, and a speed at a particular instant of time) of the KF module102to form UUs at module106which are designated as UUs or Unknown-Unknowns. The UU data may be stored in the computer memory2. Generally, all of the plurality of samples of module102are categorized as being unknown to the knowledge part of the computer processor4. I.e. although all of the plurality of samples in module102are stored in the computer memory2, and accessible to the computer processor4, the plurality of samples of module102are still categorized as unknowns. In addition, which samples of the module102are selected by the random process are also categorized as unknown. The selection process for UUs may be a random selection process.

The UUs are a percentage of the plurality of samples of the KF module102, and are supplied to the UK processor108. The UK processor108may be part of the computer processor2and/or may be programmed by computer software stored in the computer memory4.

The UK processor108uses an experiential learning rule based on a Euclidean difference or Hebbian memory conditioning to select a percentage of the samples of the KF module102or a percentage of samples of the UUs to form UKs at module110which are a percentage of the plurality of samples in the KF module102. Also, the UKs are designated as UKs and stored in computer memory2.

The KU processor112uses a query rule based on prefix search and/or end-user requests with Euclidean difference conditioning to select a percentage of the samples of KF module102or a percentage of samples of the UUs to form KUs at module114which are a percentage of the plurality of samples of the samples in the KF module102. Also the KUs are designated as KUs and stored in the computer memory2.

The UKs and KUs are supplied to the KK processor116. The KK processor116may be part of the computer processor4and/or implemented by the computer memory2. The KK processor116may produce KKs which are a percentage of the plurality of samples of the samples in the KF module102. Also, the KKs are designated as KKs and stored in the computer memory2.

The method ofFIG. 3produces UUs, UKs, KUs and KKs, as designated in computer memory2, which are used for desiring an available knowledge source field, synthesizing knowledge from an experiential process, synthesizing knowledge from a questioning process, and synthesizing a knowledge field respectively.

In the sample computer program shown at the end of this detailed description, the UUs are granular portions of the KF representing the portion of the desired information or data source, the KUs are granular portions of the UUs or KF representing the information sources derived from a query or questioning operation, and the UKs are granular portions of the UUs or KF representing the information sources derived from an experiential operation.

FIGS. 4A-4Cshow tables200,210, and22, respectively, regarding data used in the apparatus1in accordance with an embodiment of the present invention.FIG. 4Adepicts a table and/or a comparative tabular view of Shannon data bits and the KS (knowledge system) units in linguistic terms.FIG. 4Bdepicts an alternate view of the Shannon bits and KS units shown inFIG. 4A.FIG. 4Cdepicts an intuitive numeric representation of the KS units.

Each of the tables200and210, has two columns. The first column has three rows and the second column has five rows. The tables200and210indicate that Known-Known (KK) data has a representation of high and 1, and that Known-Unknown (KU), Uknown Known (UK), and Unknown Unknown (UU) data have a representation of low and 0, respectively.

FIG. 5shows a chart300for data in accordance with an embodiment of the present invention.FIG. 5depicts a granulated view ofFIG. 4Cin a circle diagram including positive and negative phases of the KS units in percent granules.

The top semicircle of the chart300shows the upper half of KS (knowledge system) variables for a positive phase, and the bottom semicircle of the chart300shows the lower half of the KS (knowledge system) variables for a negative phase. The numbers in the chart are all derived using principles of granular computing. The numbers shown inFIG. 5may be stored in computer memory2.

FIG. 6shows a flow chart400of another method and apparatus in accordance with an embodiment of the present invention, and for use with the apparatus1ofFIG. 1FIG. 6is a flow chart of a process to derive a KS representation.

FIG. 6depicts a flow diagram for deriving a KS (knowledge system) representation of states by use of computer processor4, by a computer program stored in computer memory2, also herein called the ‘knowledge granules’. As can be observed with one moderately skilled in the art of data conversion, this task can be accomplished, the computer processor4, in relatively short duration and also easily. In one or more embodiments a transformation of positive KS variable states at step402into negative KS variable states is accomplished, and then both positive KS and negative KS are concatenated into a single form at step404.

Note that VE inFIG. 6represents and/or implies a positive state, and −VE represents and/or implies a negative state.

Specifically, in one or more embodiments of the present invention, transformations are computed and concatenated from a base (positive) KS variable state by a mathematization process, stored in computer memory2, and implemented by computer processor4into a definite ordered sequence of positive and negative KS states at step406.

More specifically, in one or more embodiments, numeric encoding at step408is used, by the computer processor4to derive the aforementioned, and then granulation at step410is used to further derive a well defined set of KS granules at step412.

Furthermore, in one or more embodiments of the present invention, KS granules are trained by a sequence of machine intelligent algorithms in a memory prediction framework within an AI system within the computer processor4; the KS granules themselves also define the rules for portioning memory states into a knowledge reservoir at step414.

FIG. 7shows a flow chart500of another method and apparatus in accordance with an embodiment of the present invention, and for use with the apparatus1ofFIG. 1.FIG. 7illustrates a possible interface with a real-time learning unsupervised AI expert.

FIG. 7deals with dimensionality reduction. As can be observed, an AI system may be supervised or unsupervised use a set of pattern mining and memory operations to synthesize a sequence of streaming knowledge patterns from a data or knowledge source. Streaming knowledge patterns may be decoded by a desire (UU) thing in a predetermined manner and at a pre-defined percentage in units of KS granules. Thus, the (UU) thing is a representation of a desire phenomenon in percent granules. Subsequently, UK states (units) are derived sparsely in percent KS granules from the UU in an experiential manner; in a similar manner KU states (units) are derived sparsely in percent KS granules from the UU via a questioning phase.

Furthermore, in one or more embodiments of the present invention, machine intelligent learning can be used to make the UK experience and the KU questioning more sparse and predictive. These sparse units can then be mixed to form KK states (units) rich in knowledge granules.

At step502source data or knowledge, such as KF knowledge or fifty rows by three columns of data as shown in the sample computer program may be determined or stored in computer memory2. The KF knowledge may be provided to AI system Supervised or Unsupervised at step504to extract and memorize data patterns.

The AI system, which may be implemented by the computer processor4executing computer software stored in the computer memory2, may implement pattern mining operations510such as intelligent search and retrieval of knowledge from data, and memory operations512, such as storage of the knowledge so obtained.

The AI system which may be implemented by the computer processor4executing computer software stored in the computer memory2, may implement streaming knowledge patterns at step506, and nKK(UU) desire, or a percentage of KK may be provided as UU at step508. A percentage of UU may be provided as UK at step514, and a percentage of UU may be provided as KU at step516by action of the computer processor4implementing computer software stored in computer memory2. The AI learning system518and522may implement the UK or experience step514using standard machine intelligence operations such as Euclidean difference match and/or Hebbian learning and the KU or information request (questions) step516using standard prefix search or Euclidean difference search.

KK information may be provided to block520, which distributes a percent of KK as UK at step514and a percent of KK as KU at step516, as determined by computer software stored in the computer memory2and executed by the computer processor4.

FIG. 8shows a flow chart of another method and apparatus600in accordance with an embodiment of the present invention, and for use with the apparatus1ofFIG. 1.FIG. 8depicts a detailed KS representation flowchart including change events, search request, prevailing conditions, context information etc.

FIG. 8deals with the KS (knowledge system) as a service.

It is possible to put the KS in service using a sequence of knowledge operations (call this KSaaS). This is detailed in the flowchart ofFIG. 8. InFIG. 8, a series of processing is taken care of by the computer processor4, as programmed in the computer memory2, by knowledge operation blocks and where necessary corresponding states are encoded numerically in a block. In the context of the present invention, a state is a meta-granular numeric representation of a knowledge operation. In one or more embodiments of the present invention, the entire sequences of operations are coordinated using control blocks.

In the flowchart ofFIG. 8, desires604are obtained from a knowledge field or source602and encoded at step632or1.0. A detect change request at step606is made on the encoded desires604by the computer processor4and the resulting effect encoded at step634or1.8. In addition a control point (A) is set at the detect change encoding point by the computer processor4in the computer memory2. If we make a search on the encoded desires (1.0) or632in computer memory2using the resulting change (1.8) or634we derive a search encoding (3.7) or step636controlled at set point (B). Using (A) as reference input and the search encoding as monitoring input, the computer processor4obtains the Match or Match (sequence) and stores this in computer memory2.

The Match (or Match sequence) is encoded (5.5) or step638by the computer processor4in the computer memory2and this encoding is set at control point (C). A Relevance operation is then used by the computer processor4to determine in the first instance if the desire from desires604(and hence the match) was actually meaningful or needed. The Relevance encoding or step640(7.4) is set at control point (1). The Opportunity or614in turn determines over time the likelihood of the occurrence of a search or match i.e. if the match is relevant, the Opportunity is increased at614otherwise it is reduced. The Opportunity encoding at step642or (9.2) is set at control point (2).

Finally, in order to generate a universal rule, a necessary condition (NC) at step616and a sufficient condition (SC) at step618is required by the computer processor4. The necessary condition (NC) at step616takes as context a desire instance (e.g. war or peace) as indicated by (1) or632and this further gives us the SC or step618. An Advantage may be pre-computed to further describe the necessary benefits for Rule formulation. The Advantage is defined by a stationary encoding (12.9) at step646and a transition encoding at step622or (14.8); the transition encoding feeds into a decision block conditioned by SC or step618and a threshold (Th) at step624. If the SC meets the predefined threshold, at step624the Rule is formed by concatenating the match encoding at point (C), at step626, with the Context NC encoding and the entire knowledge processing terminates; otherwise, a detect change and search operation is repeated as indicated by (A).

FIG. 9shows a flow chart of another method and apparatus in accordance with an embodiment of the present invention, and for use with the apparatus ofFIG. 1.FIG. 9illustrates alternative KS representation flowchart.

InFIG. 9, is shown a flowchart900of an alternate KS representation as a service. In the flowchart, A0and B0represent key control (or source) points for accessing an imaginary knowledge field in the UK and KU reference frames respectively. In the same vein, A1and A2represent key control points for a UK experience while B1and B2represents corresponding control points for KU questioning. C in turn represents the control point for accessing the benefits at the KK stage. All operations are performed for a period of time defined by an incremental counter.

As indicated in the flowchart900ofFIG. 9, a desire for a thing at step702from knowledge source702is defined by a UU block taking the UK (at step704) and KU (at step708) reference into account.

In the first instance, a UK and KU block is used to detect a change in desire and make a desire search, at step704and at708respectively using appropriate machine intelligence, through use of computer software stored in computer memory2and implemented by computer processor4.

A logical operator block is then used by computer processor4to determine if the change or search is relevant, at step712when compared to a relevance query obtained from a Relevance Generator Query (RGQ) block. If a match is found, at steps718and722, respectively, both UK and KU data is passed to next operator block, otherwise the desire for a thing operation from the knowledge field is performed again as indicated by (A0, B0). If the RGQ block at step712returns an empty query at step720, a desire for a thing operation is also performed (B0).

In the second instance, a store operation is performed on UK and KU data as defined by control points (A1) and (B1) respectively if they meet a relevance requirement coordinated by a second set of operator blocks. For a certain number of time steps, the aforementioned operations are repeated to obtain a rich UK/KU memory sequence store in computer memory2. Then granular amounts of matched UKs and KUs are accumulated at steps734and762, respectively, as indicated by (A2/B2) if they meet a predefined threshold (% UKc, % KUc) at steps732and760respectively, otherwise the store operation process is repeated as indicated by (A1/B1).

In the third instance a context search NC at step736is performed between the accumulated UKs and KUs and if a matches are found at step738, the computer processor4as programmed by computer software stored in the computer memory2, stores the benefits as an SC (indicated by control point, C) in KK designated memory in computer memory2; otherwise the aforementioned operations are repeated. For each count operation at step744, a granular portion of KK (% KK) is extracted at step746and passed over to a decision block at step748. If % KK is greater than a threshold say, % KKc, all KKs are accumulated at step750and the entire operation ends at step752otherwise a store benefits operation is repeated by the computer processor4as indicated by control point (C).

In the previous sections, a Knowledge System (KS) composed of a novel representation of knowledge and that can be integrated into or composed of a machine intelligence memory prediction system for control and decision making have been introduced and explained. This system is indeed a Knowledge System (KS) different from others that attempts to bridge the gap between the origin or structure of knowledge and its pragmatic application—indeed a bridge between theory and practice of variety of knowledge operations thereof. In this section, an example embodiment of the KS that uses the teachings of the previous sections to intelligently control the speed of a treadmill is presented. Indeed, the actual implementation of the KS may be represented by a non-physical model such as a logical program, microcomputer/microprocessor software program stored in computer memory2and implemented by computer processor4, or a microcontroller program etc, as long as the principles of the teachings in the aforementioned sections are adhered to.

In a proposed KS Treadmill application, the connections are set forth as shown inFIG. 10. comprising an input sensor link (ST1) or816, two output control links (AT1, AT2) or802and810and a computer software model or812which may be stored in computer memory2and executed by computer processor4ofFIG. 1. The input link ST1 or816describes a heart rate sensor line feed to a heart rate sensor, which may be similar or identical to heart rate sensor12shown inFIG. 1, and the control links describe the speed and gradient (slope) control line feeds, which may be connected to a treadmill speed motor and to a treadmill incline device such as16and14inFIG. 1. The computer software model812describes a computer software program implementation, such as in computer memory2of the KS that allows the Treadmill (physical model) or818to operate optimally for a comfortable user experience.

In order to implement the KS Treadmill, a PC-interface or a microcontroller interface may be employed, such as by using computer processor4. For the PC (personal computer) based application, a generative procedure is used, in at least one embodiment, to obtain an KF (knowledge field) of >=1000 samples (which may be said to be equivalent to an area of 50*20 squares). Each sample is a concatenation of the velocity (speed), gradient and heart rate, where the first two are control parameters of treadmill, such as through device16for incline and14for speed or velocity, and the last one the corresponding sensing parameter such as through heart rate sensor12. For the embedded microcontroller application in computer memory2, a sample size of about fifty is just sufficient to demonstrate the applicability of the KS model.

It will be pertinent with one moderately skilled in the art of logic comprehension and control theory to note that with the KS architecture, there is no need to set any desired heart rate level. The KS (knowledge system) which is executed by the computer processor4implementing computer software stored in computer memory2, learns from the input data through experiences and search queries (it's questioning phase) via the aforementioned composed knowledge representations. For example, when a novel input or heart rate request is made, it goes through the KU KS module which makes a search of about % KU of the UK experiences. The UK experiences are derived by the UK KS module executed by the computer processor4by applying a relevance cortical-like overlapping learning rule to about % UK of the UU desires or data sources. The desire (UU) sources are generated from UU module executed by the computer processor4by intelligently selecting pseudo-randomly a % UU of the KF. Finally, a % KK of results of the KU operation are stored in the KK module of the computer memory2from which a candidate pattern is pseudo-randomly selected and the first two inputs used as the interpreted winner control signals for the treadmill. The procedure for implementing such a scheme is illustrated by the sample computer software program at the end of this description.

A physical computing view of a prototype digital-electronic circuitry for actualizing the KS model task described in the previous section is as shown inFIG. 1. The circuitry includes the key components required for operating the KS-treadmill including an electric motor for the treadmill14and a servo motor for the inclination of the treadmill16acting as actuators, a belt drive component (not shown would be connected to14), a heart rate sensor12, an embedded microcomputer/microcontroller unit, which may include computer processor4and computer memory2, and which contains the necessary internal control program (firmware) is included to coordinate the activities of most of the other components.

To operate (control) the treadmill experientially, the computer processor4receives command sensory signals from the heart rate sensor12, then uses stored data (describing a KF or knowledge field) in computer memory2, to generate explanatory control signals for activating actuators or motor14and motor16. The motor14controls the speed based on the inferred control (speed) pattern; also the motor16or device16controls the elevation (inclination) if a request for this function is made.

The entire process is repeated as long as the user of the treadmill decides to continue with the training otherwise the simulation is halted automatically—this is made possible by the training pattern and by a sample firmware such as described inFIG. 1.

The following is a sample computer program, with comments, as previously referred to which can be stored in the computer memory2and implemented by the computer processor4.

SAMPLE COMPUTER PROGRAM (and comments) regarding at least one embodiment:

FIG. 11shows a simplified block diagram900of a method, apparatus, and/or system in accordance with another embodiment of the present invention.

The diagram900shows constants C0, C1, C2, C3, C4, C5, C6, C7, C8, C9, C10, C20, and C21. These are model blocks used for initializing software process blocks in the MATLAB-SIMULINK language. MATLAB generally, is known MATrix LABoratory computer software and SIMULINK is generally a known graphical programming environment.

The diagram900shows switches902,904,906,908,910,912,914,916,918,920,922,924, and926. The diagram900shows modules for the UU processor928, UK processor932, KK processor936, and KU processor940, all of which may be part of the computer processor4ofFIG. 11, and may implement computer software stored in the computer memory2ofFIG. 11. The diagram900further shows UU display930, UK display934, KK display938, and KU display942, all of which may be part of display device8shown inFIG. 11.

In a related application, an adaptive/dynamic real time software implementation of the KS in a fair judicial process is shown implemented by the method, process, and apparatus shown by diagram900ofFIG. 11. The software application is implemented using the MATLAB modeling language called SIMULINK. The connections are set forth as shown inFIG. 11comprising primarily of an input set of voting switches902-924and any further number of voting switches, an embedded function block of computer software stored in the computer memory2, describing the UU processor928, an embedded function block of computer software stored in computer memory2, describing the UK processor932, another embedded function block of computer software stored in computer memory2describing the KU processor940, and a subsystem block (KK state out) describing the KK processor936.

The input voting switches902-924and any further number of switches describe a sequence of inputs similar to the process of voting in a fair electoral process but encoded as 1 in agreement with a judgment or judicial opinion and a 0 in disagreement with the judicial opinion. Each switch of switches902-924allows the entry of a vote in favor (signal a 1) or against (signal a 0) the judicial opinion by a member of a population of participants in the judicial process. These entries, may be stored in computer memory2, through multiplexer901. These entries give rise to, or make up a Knowledge Field (KF) sequence similar to that of the Treadmill application.

The embedded function block of UU processor928operates in accordance with the principles set forth earlier in the Treadmill application. In this UU processor928, a portion of the voting data is extracted, as programmed by computer software, from the generated KK vote field data at MUXsb0identified as multiplexer901shown inFIG. 11and randomly pooled to give rise to the UU vote sequence set which may be stored in the computer memory2. The sample embedded software code implementation of the UU processor928with comments is as set forth below:

The embedded function block, UK processor932, operates in accordance with the principles set forth earlier in the Treadmill application. In this UK processor932, as programmed by computer software stored in computer memory2, a portion of the voting data is extracted from the UU data (which is produced at the output of the UU processor928shown inFIG. 11) and randomly pooled to give rise to the UK vote sequence set at the output of the UK processor932, which may be stored in computer memory2. Then this set (UU) passes through an experiential operator LINES21-25based on the Euclidean distance metric to generate the UK prediction set. The sample embedded software code implementation of the UK processor932with comments is as set forth below:

The embedded function block KU processor940, also operates in accordance with the principles set forth earlier in the Treadmill application. In this processor940, a portion of the voting data is extracted from the UU processor928and randomly pooled to give rise to the KU vote sequence set. Then this set (KU) passes through an experiential operator LINES19-23which is also based on the Euclidean distance metric to generate the KU prediction set. In light of the present invention, the KU processor uses an external query or interrogative signal (query_in) as context in the current time step for deriving its predictions or representations LINE17. The sample embedded software code implementation of the KU processor940with comments is as set forth below:

FIG. 12shows a simplified block diagram1000of a method, apparatus, and/or system in accordance with another embodiment of the present invention being a subsystem ofFIG. 11.

In this diagram1000, the KK processor receives processed knowledge fields (vote signals) from the UK processor at UK (or1002) and the KU processor at KU (or1004) and then concatenates UK with KU at MUXsb1(or1006) forming a field of decision signals. The decision signals emanating from1006are summed up using a summation block or module (Sum of Elements)1008and then divided by the cardinality of the signals formed at1006. The cardinality of the signals at1006is computed by a MATLAB function block1016by setting its function property field to “numel”. The division operation1010gives a probabilistic interpretation of KK at point Pt1which is then compared to a threshold block, Th1(or1012) set to a critical value of 0.5. The resulting KK decision signal is then sent out of the subsystem for further signaling using an outport block KK state out (or1014). Display, KK Display (or1018) gives an indication of the signal at Pt1.