Category: Machine Learning

Authors:
John Carbone, Forcepoint LLC
Dr. James Crowder, Colorado Engineering Inc.

Abstract:
Historically, research shows analysis, characterization, and classification of complex heterogeneous non-linear systems and interactions have been difficult to accurately understand and effectively model. Synonymous, exponential growth of Internet of Things (IoT), Cyber Physical Systems, and the litter of current accidental and unscrupulous cyber events portray an ever-challenging security environment wrought with complexity, ambiguity, and non-linearity. Thus, providing significant incentive to industry and academia towards advanced, predictive solutions to reduce persistent global threats. Recent advances in Artificial Intelligence (AI) and Information Theoretic Methods (ITM) are benefitting disciplines struggling with learning from rapidly increasing data volume, velocity, and complexity. Research shows Axiomatic Design (AD) providing design and datum disambiguation for complex systems utilizing information content reduction. Therefore, we propose a comprehensive transdisciplinary AD, AI/ML, ITM, approach combining axiomatic design with advanced, novel, and adaptive machine-based learning techniques. We show how to significantly reduce risks and complexity by improving cyber system adaptiveness, enhancing cyber system learning, and increasing cyber system prediction and insight potential where today context is sorely lacking. We provide an approach for deeper contextual understanding of disjointed cyber events by improving knowledge density (KD) (how much we know about a given event) and knowledge fidelity (KF) (how well do we know) ultimately improving decision mitigation quality and autonomy. We improve classification and understanding of cyber data, reduce system non-linearity and cyber threat risk, thereby, increasing efficiency by reducing labor and system costs, and “peace of mind.”

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[download id=”26882″]

Authors:
Dr. James Crowder, Raytheon Intelligence and Information Systems
Dr. Lawrence Scally, President and CTO, Colorado Engineering Inc.
Michael Bonato, VP of Program Management, Colorado Engineering Inc.

Abstract:
Current and future space, air, and ground systems will continue to grow in complexity and capability, creating a serious challenge to monitor, maintain, and utilize systems in an ever growing network of assets. The push toward autonomous systems makes this problem doubly hard, requiring that the on-board system contain cognitive skills that can monitor, analyze, diagnose, and
predict behaviors real-time as the system encounters its environment. Described here is a cognitive system of Learning Agents for Autonomous Space Asset Management (LAASAM) that consists of Intelligent Information Agents (I2A) that provide an autonomous Artificially Intelligent System (AIS) with the ability to mimic human reasoning in the way it processes information and develops knowledge [Crowder 2010a, 2010b]. This knowledge takes the form of answering questions and explaining situations that the AIS might encounter. The I2As are persistent software
components, called Cognitive Perceptrons, which perceive, reason, act, and communicate. Presented will be the description, methods, and framework required forCognitive Perceptrons to provide the following abilities to the AIS:

1. Allows the AIS to act on its own behalf;
2. Allows autonomous reasoning, control, and analysis;
3. Allows the Cognitive Perceptrons to filter information and communicate and collaborate with other Cognitive Perceptrons;
4. Allows autonomous control to find and fix problems within the AIS; and
5. Allows the AIS to predict a situation and offer recommend actions, providing automated complex procedures.

A Cognitive Perceptron Upper Ontology will be provided, along with detailed descriptions of the I2A framework required to construct a hybrid system of Cognitive Perceptrons, as well as the Cognitive Perception processing infrastructure and rules architecture. In particular, this paper will present an application of Cognitive Perceptrons to Integrated System Health Management (ISHM), and in particular Condition-Based Health Management (CBHM), to provide the ability to manage and maintain an AIS in utilizing real-time data to prioritize, optimize, maintain, and allocate resources.

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[download id=”20155″]

Authors:
Dr. James Crowder, Systems Fellow, Colorado Engineering Inc.
Dr. Shelli Friess, LPC, NCC, ACS, School of Counseling, Walden University
Dr. John Carbone, Electrical and Computer Eng., Southern Methodist University

Abstract:
There has been much discussion and research over the last few decades on the differences between implicit and explicit learning and subsequently, the difference between explicit and implicit memories that result from implicit vs. explicit learning. Implicit learning differs from explicit learning in that implicit learning happens through unconscious acquisition of knowledge. Implicit learning represents a fundamental process in overall cognition, stemming from unconscious acquisition of knowledge and skills as a result of an entity interacting with its environment. One of the issues or consequences of implicit learning is the notion of how do we recognize that implicit learning has occurred, how will it affect the overall cognitive functions of the entity, and how do we measure and affect implicit learning within an entity? Here we discuss the notion of self adapting, cognitive, artificial intelligent entities and the notion of implicit learning within the artificial intelligent entity; how this will lead to implicit memories and how they might affect an overall artificial intelligent entity, for better or worse.

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[download id=”19988″]

Authors:
Dr. James Crowder, Systems Fellow, Colorado Engineering Inc.
Dr. Shelli Friess, LPC, NCC, ACS, School of Counseling, Walden University
Dr. John Carbone, Department of Electrical and Computer Eng., Southern Methodist University

Abstract:
Current machine learning architectures, strategies, and methods are typically static and non-interactive, making them incapable of adapting to changing and/or heterogeneous data environments, either in real-time, or in near-real-time. Typically, in real-time applications, large amounts of disparate data must be processed, learned from, and actionable intelligence provided in terms of recognition of evolving activities. Applications like Rapid Situational Awareness (RSA) used for support of critical systems (e.g., Battlefield Management and Control) require critical analytical assessment and decision support by automatically processing massive and increasingly amounts of data to provide recognition of evolving events, alerts, and providing actionable intelligence to operators and analysts.

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[download id=”19981″]

Authors:
Dr. James A. Crowder, Systems Fellow, Colorado Engineering Inc.
John N. Carbone, Electrical and Computer Engineering Department, Southern Methodist University

Abstract:
Current machine learning architectures, strategies, and methods are typically static and non-interactive, making them incapable of adapting to changing and/or heterogeneous data environments, either in real-time, or in near-real-time. Typically, in real-time applications, large amounts of disparate data must be processed, learned from, and actionable
intelligence provided in terms of recognition of evolving activities….

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[download id=”6912″]