Detecting Events in Computational Space

 

 

 

New product description from OntologyStream

May 23, 2002       Revised May 29th, 2002

 

Written by Dr. Paul S. Prueitt

 

Executive Summary

 

OntologyStream offers a fundamental innovation in methods for Human Information Interaction, a significant contribution in massive data structuring/organization, and a contribution in recognizing and using regularity in data structures.

 

From first principles, our scientists find that a system for optimal Human Information Interaction can be best developed that uses categorical abstraction as the atoms of cognitive analysis.  And we find, also from first principles, that cognitive graph type knowledge representation plays an important role in formalizing and validating the social knowledge shared within a virtual community of practice.  Regularity in data structures allows the functional by-pass of, what is called, “computational complexity” by relying on regularity of structure as imposed by patterns of expression found in the data.

 

Synthetic Intelligence (SI), from machine-human interaction, involves the:

 

  1. Generation of top down expectancy based on opponent processing and frames of reference
  2. Generation of bottom-up aggregation of perceived invariance and patterns.
  3. The representation and use of regularity in data structures.

 

Some qualities from the nature of cognition can be transferred from cognitive neuroscience research literature, as expressed by Karl Pribram in “Languages of the Brain” (1971) and “Brain and Perception” (1991).  The resulting “synthetic” intelligence product has desirable qualities found in the natural product, and yet is absent some of the un-desirable qualities found in the natural product.

 

Synthetic Intelligence (SI) technology takes computer-based knowledge aids closer to the human perceptual-linguistic experience. 

 

Scenario:  A small set of unusual event patterns has been identified automatically, as elements of cA.  Profiles on the patterns are placed into an event library.  According to a set of priority parameters, interpreters pick up the events and view the event patterns.  Real time human comments are captured using voice to text algorithms.  One event seems suspicious.  As part of a very natural conveyance of information a virtual team develops to discuss the patterns.  An event ticket is opened, and a dedicated collaborative system is autonomously brought on line.  The team is able to talk about the event patterns, and directly share knowledge, in reference to shared visual indications.  A virtual White Board is used to allow hand drawing to illustrate discussions. A process of preserving the discussion is present.  The virtual team traces back to the data, and after further inspection concludes that a novel event has occurred.  This event is annotated, including notes on intent and technique, and the event joins the confirmed library of 30-40 major event types and 300 minor event types, each representing a family of variants within meaningful units.   The conjectural causes of the event are noted in annotation.  Evidence is acquired early and thus the motivation behind the causes of the event are recognized and acted on in real time.  Learning within the community is greatly accelerated via a community knowledge base that preserves shared knowledge within a structured ontology as a cognitive graph (and related knowledge representational means). 


Discussion:  OntologyStream is a new company founded in 2001 on principles shared between Drs. Prueitt, Levine, Pribram, Kugler, Citkin and Murray and colleagues within an extended science community.  These scientists have collaborated for over a decade in the areas of natural computing, general systems theory and on the nature of human action and perception.  Their collaboration has also been with several science communities involved in the definition of what we are calling the New Computer Science. 

 

It is the focus of these scientists to

 

1)      Demonstrate experimental cognitive science methodology in a formal study of Synthetic Intelligence (SI) technology systems.

2)      Field a commercially available event detection system. 

 

Visualization of computer data is often an appropriate way to engage human perception.  But visualization of data approaches break down, as data sources continually get larger. There are other deeper perceptual/behavioral issues that inhibit the adoption of HII systems.

 

Successful human and social learning is the basis for the cultural value to be derived from a cyclic process of action followed by perception.  This involves humans being immersed in the experience and in the use of community language. We find that the scholarly literature on Human Information Interaction (HII) will not be transparent until HII involves the development and modification of natural language by those who are using SIT systems in everyday activity.  But we also need machine support to overcome classically understood behavior characteristics having negative impact on truth seeking. 

 

Humans are enormously capable as perceptual-language 'machines', but our analytic thinking is often flawed and simplistic.   So how can the issue of human perception and the issue of cognition be studied, in the context of critical intelligence gathering and analysis?  What about knowledge propagation within communities?  How can this be studied?

 

Experimental cognitive science methodology can be applied to the study of Human Information Interaction if the fidelity of the information technology is of sufficient quality.  Cognitive graph (ontology) based decision aids are not of sufficient quality by itself, because there is no perceptual aspect to system interaction.  In most instances the ontology, that is available, is not formative from a perceptual act.  Without being formative from an act of perception then the information technology has a radically different and often incompatible nature when compared with acting and perceiving as part of the human experience of sense.

 

Within the cognitive neuroscience literature, images of achievement are said to direct human behavior (see Pribram’s chapter on this in “Brain and Perception”).  But the perception of an external reality is necessary to tightly coupled action in the world. 

 

When knowledge base systems are combined with an action-perception based system; then the fidelity may be sufficient to warrant proper experimental study of the human interaction with these systems.

 

The human perception system brings real time proprieties and priorities and contextualizes an action space.  The synthetic perception system extends human perception into the virtual computational spaces.

 

Using categoricalAbstraction (cA), the “synthetic perception” is about categorical invariance in data (structured or natural language data) and the categorical relationships that exist due to co-occurrence and pre-established frames (with slots and fillers). 

 

Is this really new?

 

The key to our work is found in the perceptual measurement (see the work of Howard Pattie, Peter Kugler and Robert Shaw.)  Synthetic perception looks into the invariance of data flow using data mining type computing processes.  The data flow is “measured” with a convolution operator having the form of a conjecture.  The found invariance is treated as if an invariance in the human experience of perception.  To our knowledge, there is no other synthetic perceptual system currently being applied to intelligence problems. The few synthetic perceptual systems that exist are small-scale laboratory applications meant to simulate the operation of natural senses, and these are not based on emergent abstractions from massive flow of real-time data.  However, the prior art for this work can be discussed.

 

The prior art for cA and for the architecture in which we have embedded cA, is extensive but largely exists in literatures outside of Artificial Intelligence and Information Technology.  This work includes long-term research efforts in the areas of reflective control, applied semiotics, and second order cybernetics.  Much of the documentation of this research traces to Former Soviet Union science organized under special governmental organization (prior to 1991), and has only slowly been adopted into the intelligence control and machine intelligence literatures in the United States.  Dr. Citkin is a specialist on this work.  Significant research exists in the ecological psychology literature in the United States and Europe.  Dr. Kugler is a specialist on this work. 

 

Ours would be the first tool based on emergent abstractions, of the type proposed, that is intended for analysis of large data sources.

 

The event detection system

 

We have a new technology for event detection.  Very rapid algorithms select events based on the convolution of a conjectural form “over” massive data sets.  The algorithms work at about the speed of a data encryption algorithm or a data compression algorithm. 

 

Various conjectural-forms are selectable from an engine bank.  The first of these conjectural-forms is based on Russian applied cybernetics, and was developed (1995 – 1998 by Dr. Prueitt) to encode similarity relationships within textual and pictorial elements of scanned images.  Drs Kugler and Murray were involved in this work.  Simple link analysis was the second example of a conjectural-form. Prueitt and Dean Rich applied this conjectural-form to cyber warfare prototype (2001), now demonstrated.   The software engineer for our cyber warfare prototype is Don Mitchell.

 

Formal notation describes this work.

 

It is felt that cA can be shown to have generalized the LSI algorithms into a class of stochastic forms that act to produce categorical atoms and links designating invariance and relationship.  The notation for this work is at the URL.  The notation for the voting procedure is at URL.  A general discussion and notation for Russian Quasi Axiomatic Theory is at URL and URL.


Hierarchical Taxonomy

 

An event database consists of levels of taxonomical organization.  Perception acts on outside stimulus and encodes results into the event database. In the simplest case, distributed instrumentation is placed in the computational space, such as an image library or a full-text database.   Parsing processes are activated with a rule base.  On category of examples of such process is the category of Intrusion Detection Systems (IDS) that parse log files and produce log files based on rule firing.

 

A log file is produced in the form of a table having two or more columns.  A common input, in the form of this log file, was prototyped in systems developed by OntologyStream Inc (2001 – 2002).  Log files can also be produced from a linguistic parser, as in a generalized Latent Semantic Indexing.  From the log file we produce atoms and relationship types.  Emergent computing (a type of feature extraction and categorization process) produces event compounds.  These compounds are then annotated and transferred to a cognitive graph type knowledge base. One is able to then form compounds of compounds and to trend event occurrences into models of cause and motivation.

 

 

Figure 2: Four levels of knowledge representation

 

The bottom layer of the event database is developed autonomously.  The data stream is “perceived” by the variation of conjectural convolutions and a cognitive graph type Knowledge Base helps to constrain the perception into meaningful elements – using frames with slots and fillers as an intermediate control mechanism.  Humans will be able to modify the perception easily, using voice or textual commands, and can make interpretations based on visual clues and remembrances from past experiences.  These features have been accounted for in our existing software. 

 

The synthetic perceptional system is informed by cognitive influences, in a way that is parallel to what is know about the human perceptional system.  Specifically, the architecture directly reflects the organizational stratification in human perception, and the use of convolution (integration over space and time) as a means to move categorical information from one level of organization to a higher level of organization.  (Memory invariance becomes convolved into direct perception.)

 

In both human and synthetic perception, there is an inner perception and an outer perception; both having elements of cognition.   The substance of synthetic perception is composed of categorical abstraction produced by a conjectural-form acting on data.  A substructural process occurs over time and location that is not perceived.  In humans, the perception occurs only as emergent process that directly depends on this un-perceived process. 

 

This is like the massive number of individual photons passing into the retina.  The individual photons cause quantum mechanical events that then add to a potential that is sampled by dendrites.  A series of additional steps cumulates in a physical electro magnetically guided convolution over the cortical layers of the visual cortex (see the published works of Pribram.)  A range of adequate computational models of this neuro-physical model exists (see the work of Juri Kropotov, for example.)

 

The inner perception of synthetic perception can be modified, in real time, by human modification; direct or indirect, of sensor parameters and conjectural forms.  What remains constant is that which is being looked at.   We have not yet achieved this software functionality; however, the effect, when achieved, should be real time “looking at” behavior.  It is this behavior that can be properly studied by our human factors scientists.  Drs. Meyers (SAIC) and Murray (OntologyStream) have provided a technology interface to these scientists and advises on knowledge engineering issues as these issues arise.

 

The outer perception comes from a cognitive interaction with remembrance of past events.  This occurs in both the machine and in the human.  In the second layer, event compounds are rendered visually as simple graphs.  A top down expectancy, from the knowledge base, can be used to structure the graph.  This has not been done, but both Adaptive Critic neural networks (Paul Werbos) and Adaptive Resonance Theory (Stephen Grossberg) are very well known machine expectancy algorithms.  Feature extraction and pattern completion are mature technologies.  Human remembrance of the past may also structure the interpretation of the graph. 

 

The SI technology can be exceedingly simple to use.  The event stream can be rendered with sounds (like background music), so that humans might attend to other things and use only auditory acuity to sense the event stream behavior in real time. The third layer is a knowledge management system that connects members of a virtual community.  Policy compliance is modeled and represented at the fourth level.

 

The bottom layer of the layered taxonomy is an open symbol system depending on invariance types (atom and link categories) produced from the aggregation (convolution) of computer data at selected points within an instrumented system.  An example of an instrumented system is a system for processing Intrusion Detection System audit logs.  A second example is data base access log files.  A third example is a sub-event log file produced while a text (conceptual search) engine is finding correlation between textual elements (this is generalized LSI).  In each case, the log file has the same simple (editable) format. 

 

The bottom layer of the layered taxonomy is also a structure of sub-types related to the invariance that compose events of interest to the human community.  This structure, of sub-types, can be compared – using grounded metaphor, with the “memory” of texture, color and form in a human perceptional system.  Formal notations, on voting procedure (Prueitt) and quasi-axiomatic theory (Alex Citkin, Victor Finn and Dimtri Pospelov), exist for autonomous aggregation of memory (categorically linked invariance) of this type into cognitive graphs.  These formal notations support advanced methods that will be developed.  Used in this way, cA “memory” of invariance is aggregated into situated ontology, ontology with emergent contextual scope that will appear as an instant retrieval of information to the human.

 

The second layer of the layered taxonomy is an event layer that is responsive to a deployed infrastructure of human annotated event types and to the visualization of pre-existing event patterns at the third level.  This layer second can, in practical ways, have algorithmic interactions with a cognitive graph type knowledge base. 

 

Top down expectancies is thus possible using any of a number of algorithmic methods (evolutionary programming, adaptive resonance theory, or adaptive critics).  The class of these expectancies is compared with the connectionist scholarly work; by Werbos, Grossberg, Holland and others; in automating recall using formal models of knowledge associative memory.  Our initial architecture is a bit simpler than these classical algorithms, but a more sophisticated associative memory will be implemented about midway into our 20-month commitment. 

 

The more sophisticated associative memory will be developed under direction and consulting with Professors Daniel Levine and Karl Pribram.  Daniel Levine is one of the leaders in the cognitive science / biological and artificial neural network community (and PhD (1988) advisor to Dr. Prueitt).  Dr. Levine is a professor of psychology at University of Texas at Arlington and has had several decades of scientific interaction with Professor Pribram, Drs. Kugler, Murray and Prueitt.  Professor Karl Pribram is Stanford Professor Emeritus (now at Georgetown University) and world renown as one of the founders of the field of cognitive neuroscience. Both Pribram and Levine are advisors to OntologyStream Inc.

 

The third level of the layered taxonomy is a knowledge management system having knowledge propagation and a knowledge base system developed based on Peircean logics (small cognitive graphs) that have a formative and thus situational aspect.

 

The fourth level of the layered taxonomy is a machine representation of the compliance models produced by policy makers.

 

Existing work

 

In Figure 3a, we show some of the early work on creating categories and rendering these categories as visual abstraction.  Figure 3a shows the results of a feature extraction process (scatter-gather on the surface of a sphere) that has produced a compositional ontology having five layers.  Each of these elements in the compositional ontology can be viewed as an event compound composed of elementary patterns of invariance and types of relationships that this pattern has with other patterns.  The compound has the nature of a chemical compound composed of elementary atoms (of invariance type) and valance (Figure 3b).  Both elementary number theory and category theory are used in the underlying formalism (again, this formalism was developed by Drs. Prueitt, Murray and Kugler, with scholarship citation to a foundational literature.)

 

  

a                                                               b

 

   

c                                                  d

 

Figure 3: Some of the early work on the formation and visual rendering of cA

 

Great flexibility is provided for the fast assembly of atoms (of invariance type) and link-types into small colored icons (Figure 3c and 3d).  An in-memory data structure is expressed or rendered directly in one pass over the structure. This is considered a perception of the map.  Evidence has been acquired that this in-memory map structure has the nature of a hologram/fractal, in that partial (random) retrieval will often look very similar to complete retrieval.  Human information interaction occurs during incomplete rendering, or when the event itself is only partially complete. 

 

As in a real event, occurring in the natural world; an eventCompound is nested in other eventCompound and has sub eventCompounds.  In the natural world, the phenomenon of emergence provides a core open problem regarding the specification of the boundary and initial conditions of an event.  Work on this involves the physical science study of emergence and non-equilibrium physics.  An event series is a temporally ordered series of events with strong causal dependencies.  Event detection in the computational spaces has a similar modeling problem, except that in the computational spaces there is an absolute ground level composed of the binary switch.  There are the elementary atoms that classical (Newtonian) physics incorrectly hypothesized to exists as non-decomposable blocks.  But, all events in the computational space are reducible to these patterns of 0s and 1s.  Levels of organization in the computational space are seen as expressions of habits by programmers in coding, and in the regularity of human interaction with the computational spaces.

 

The theory of stratified complexity when applied to computer science produces some startling results.  These results can be sampled in the URL.

 

The integration of the categoricalAbstraction based Synthetic Perceptual System (cA-SPS) with industry standard metadata rich knowledge engineering systems is made via a translation into machine-readable ontology (such as XML with RDF, KIF, or Cognitive Graphs (CG)).  

 

Innovative claims for the research: 

 

Knowledge Operating System (KOS)

 

 

 

 

Innovations in Algorithms and Processing Architecture (IAPA)

 

 

 

 

Basic innovations entail several surprising and beneficial features that will be substantiated during the research project:

 

 

The work has been developed over a period of twenty years.  Our work in this area fills a critical gap.  Intelligence vetting must be made both autonomous and automatic.  Abstraction levels, with drill down, is necessary if the policy makers of our representational democracy are to be properly informed in real-time of what is in fact going on in the computational spaces. 

 

Our system provides the first significant break through in human/computer anticipatory systems. 

 

We argue that emergent categories of data invariance, when harnessed with the human's perceptual system, can make sense of massive data and efficiently find novel intelligence. 

 

The process and software is to be applied to several domains, such as textual intelligence, in which informational novelty resides in massive data.  We anticipate image-understanding techniques and have strategies for acquiring data from large images databases. 

 

The domain expert (i.e. intelligence analyst) and the software component communicate via the conjectural statement, images of the categorical abstractions, and annotations attached to images.  Data collected by sensors and instrumentation is accumulated into an in-memory data structure.  The data structure in memory is then processed to extract structures that are in turn served up to an interpretation layer. An augmented librarian classifies the new abstractions, places them in the visual library, and alerts analysts or response bots. Analysts view aspects of the abstraction and may act on the item. Interpretations are fed back to the system to modify visual cues and shared memory. The man-machine system adapts and learns, producing general and comprehensive models of events and relationship between events that can be distinguished from other events.

 

Features of the system

 

 

The first project milestone is to acquire categorical abstractions from a data set, and to have the analyst provide interpretations that are then shared.  We will conduct experiments and make corrections and enhancements to this unique system for specific application into the intelligence domain.


 Expectations

 

We expect that the American government is serious about moving intelligence technology towards Predictive Analysis and Anticipatory Mechanisms.  There are significant cultural barriers that agencies of the American government will have to address.  However reaching into the science community is the only way that these barriers can be moved aside.

 

Scientists in the fields of general systems theory, cognitive psychology and human factors will study Human Information Interaction (HII) with advanced knowledge technologies.  But one can see that there is a judgment about the deployed information technology.  This technology does not have high fidelity to what is known, and has been know for some decades, regarding the nature of human natural intelligent and natural language. 

 

Current generations of DARPA knowledge representation technology have produced a number of high quality ontology constructors and interfaces to machine ontology.   These are now available to purchase as COTS products.  What has not been developed, at DARPA, is a technology based on the derivation of categorical invariance over the real time structure of data in computational spaces. 

 

 

Figure 3: The High level View of a Knowledge Operating System

 

The interface between DARPA developed machine ontology systems and categorical Abstractions (cA) is straightforward and quite natural.  Our use of regularity in the data structure and in-memory referential information bases (RIBs) is essential to the real time performance of the KOS. 

 

Natural language learning and natural language use provides a different type of cognitive (behavioral) emersion.  There is adequate scientific literatures and academic work in these areas.  However, these scientists generally do not show an interest in DARPA developed technology (except as funded by government programs).  The core business of OntologyStream is the properly grounded evaluation of knowledge technologies based on HII research principles.

Scenario:  A terrorist organization has assembled four independent groups of computer hackers across the world.  Each group is given a different task; each task supports another group’s task. The interweaving of tasks creates a well-coordinated and timed attack.  The first group’s task is to breach the internal networks of the U.S. Power Grid Command and Control networks.  The second group is tasked to electronically break into five major U.S. banks, and the Federal Reserve.  Meanwhile the third and fourths groups are “running interference” by launching false or partial attacks on various government agencies and facilities.  Groups one and two begin preparation for their attacks by doing basic Internet searches and reconnaissance probes into the targeted networks.  Given the distributed and well-coordinated nature of these probes (and later on the attacks), the computer intrusion analysts see basic scans and attack attempts, of which they see thousands of per day.  There’s no reason for them to believe anything particularly devious would be in the works, due to the limited scope of each analysts sensor arrays and data.  Each analyst at each facility allows the system to automatically log the attempts, but overlook them due to the routine nature of such attempts.  Later, the attacks are carried out and successful breaches of internal security at 90% of their targets are successful, the Federal Reserve was not breached due to existing security measures.  Once the full attacks were being launched, the computer intrusion analysts were unable to completely repel the attacks because of the attack obfuscation and interference created by groups three and four. The U.S. economic infrastructure is totally devastated, hospitals are forced to run on emergency power, many die from the power outs, and mass chaos ensues.

 

Had a system such as our proposed synthetic perception system and categorical abstraction been employed throughout this attack, along with a well-made event base, the outcome would have been significantly different.  The system will have the ability to look at all the data from intrusion detection systems nation wide, and identify patterns that would indicate a coordinated attack.  This information would then be presented to the analyst for further review and corroboration.  Further, if the coordinated attack patterns had been allowed unchecked by the human analyst and the attack had still gone into effect, the system would have had the ability to detect which were the “real” attacks, and which were mere attempts at obfuscating the real attacks.  This would allow the nation as a whole to prioritize reaction to attacks, not by what is being attacked, but whether it is believed to be the actual target or not.  Without this, the analyst first instinct would be to protect major intelligence networks first, and leave the banking and power systems lower down in priority, due to false attacks launched on the intelligence networks.

 

Market Integration:  The proposed synthetic perceptual system is designed specifically with interoperability in mind. It can work completely independent from all other systems, and requires no third party software or modules. The independence of the system allows it to be implemented with any number of modules or systems. Compatibility with any and all data sources and systems will be achieved using relevant XML, RDF, KIF, XTM, and upper ontology standards.  The stand-alone modules are lightweight, sometimes less than one megabit in file size.  These modules will exist as Internet browsers.  The modules can address any data source.

 

A perceptual system into the data invariance results in new human language use and this language use can be mediated by knowledge base technology.   

 

Outcomes

 

 

 

Comparison To Other Ongoing Research:

 

The Cognitive Graph (CG) approach has extended the principles of Charles Sanders Peirce's Existential Graphs, Entity Relation Diagrams, Semantic Networks and XML type ontology representation.  Once in a CG, various technologies provide a direct mapping from CGs to first order logic.  CGs is used in a number of COTs systems to management n-ary relations in a novel way.  

 

Architecturally we regard the cA Synthetic Perceptional System (cA-SPS) is requiring a CG-Synthetic Cognitive System (CG-SCS) having the form of a CG type standard knowledge base.  We assume the responsibility for integrating the CG-SCS and the cA-SPS and for studying the Human Information Interaction between the combined Synthetic Intelligence (SI).  The differences and similarities between Artificial Intelligence (AI) and Synthetic Intelligence is subject to some current science publications.  These publications include literatures on the new sciences or quantum-neuro interfaces and nano-technology.  The central authors, within this scholarship literature, are Steward Hameroff (Univ of Arizona), Sir Roger Penrose (Cambridge University), Karl Pribram (now at Georgetown, formerly at Stanford), Robert Shaw (University of Connecticut), and others. 

 

CG-type systems generally assume that knowledge can be fully represented as tokens in logic and rules based on these tokens.  This assumption is seen to have merit by many technologists and some cognitive scientists.  However, this issue is in active dispute by important foundational scholarship such as by Karl Pribram (cognitive neuroscience) and Robert Shaw (ecological foundations to perception).

 

In contrast, cA-SPS produces small-situated ontology structures (easily converted to Peircean type CG) through a very rapid reduction of the occurrences of patterns in massive data sets.  These small-situated ontologies are not interpreted by the rules of a first order logic.  The cA are visually rendered into a 2, 3 or n dimensional space; and the visual acuity of human observers accesses the natural cognitive activities in the brain system.  The sharing of knowledge about these small-situated ontologies engages the natural exercise of judgment by a community of practice. 

 

The cA-SPS and artificial knowledge representational systems (such as those that depend on CG) share a separation of responsibilities.  The cA-SPS acts as an event perception algorithm that looks into the artificial world of computer data.  The CG systems can take the form of the event representation and convert this into standard machine ontology with first order predicate logics. 

 

Key project members

 

 

OntologyStream Advisors