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The Systems AI Thinking Process (SATP) for Artificial Intelligent Systems

Authors:
Dr. James Crowder, Systems Fellow, Colorado Engineering Inc.
Dr. Shelli Friess, School of Counseling, Walden University

Abstract:
Previous work has focused on the overall theory of Systems- Level Thinking for artificial intelligent entities in order to understand how to facilitate and manage interactions between artificial intelligent system and humans or other systems. This includes the ability to predict and produce behaviors consistent with the overall mission (duties) of the AI system, how to control the behaviors, and the types of control mechanisms required for self-regulation within an AI entity. Here we advance that work to look at the overall Systems AI Thinking Process (SATP) and the architecture design of self-regulating AI systems-level processes. The overall purpose here is to lay out the initial design and discussion of concepts to create an AI entity capable of Systems-Level thought and processing.

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The Systems AI Thinking Process (SATP) for Artificial Intelligent Systems (6 downloads)

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Synthetic AI Nervous/Limbic Derived Instances (SANDI)

Authors:
Dr. Shelli Friess, School of Counseling, Walden University
Dr. James A. Crowder, Systems Fellow, Colorado Engineering  Inc.
Dr. Michael Hirsch, President and CTO, ISEA TEK LLC

Abstract:
Artificial feelings and emotions are beginning to play an increasingly important role as mechanisms for facilitating learning in intelligent systems. What is presented here is the theory and architecture for an artificial nervous/limbic system for artificial intelligence entities. Here we borrow from the military concept of operations management and start with a modification of the DoD Observe, Orient, Decide and Act (OODA) loop. We add a machine learning component and adapt this for processing and execution of artificial emotions within an AI cognitive system. Our concept, the Observe, Orient, Decide, Act, and Learn (OODAL) loop makes use of Locus of Control methodologies to determine, during the observe and orient phases, whether the situation constitutes external or internal controls, which will affect the possible decisions, emotions, and actions available to the artificial entity (e.g., robot). We present an adaptation of the partial differential equations that govern human systems, adapted for voltage/current regulation rather than blood/nervous system regulation in humans. Given human trial and error learning, we incorporate a Q-learning component to the system that allows the AI entity to learn from experience whether its emotions and decisions were of benefit or problematic.

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Synthetic AI Nervous/Limbic Derived Instances (SANDI) (4 downloads)

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A Hybrid Cognitive System for Radar Monitoring and Control using the Rasmussen Cognition Model

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

Abstract:
The long-term goal of artificial intelligence (AI) is to provide machines the capabilities to learn, think and reason like humans. To achieve these long-term goals, it is necessary to introduce human cognitive-like abilities into AI systems to create truly self-adaptive artificially intelligent systems. This marriage of human cognitive skills with “machines” creates hybrid systems that have characteristics of both. The question becomes, which human cognitive model is appropriate for hybrid artificial intelligent systems. The purpose of this paper is to discuss the development of cognitive models to be infused into a modern radar system to create a Cognitive Radar System (CRS). The notion of a hybrid artificially intelligent system can be divided into two main categories: (a) human-in-the-loop systems with hybrid augmented intelligence requiring human-AI communication/collaboration, and (b) a cognitive computing-based AI in which a fully cognitive model is infused into the machine to allow fully autonomous operation. Here, we discuss the first type, human-in-the-loop cognitive radar systems that provide intelligent decision support and analysis for radar systems. The design of hybrid artificial intelligence methods and algorithms are presented with applications to improvement to modern radar systems, utilizing a Rasmussen Cognition Model (RCM), which we feel is appropriate for a hybrid cognitive system utilized to create a Cognitive Radar System (CRS).

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A Hybrid Cognitive System for Radar Monitoring and Control using the Rasmussen Cognition Model (3 downloads)

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Artificial Intelligence Profiles for Foundational Counselor Training Sessions

Authors:
Dr. James Crowder, Systems Fellow, Colorado Engineering Inc.
Dr. Shelli Friess, LPC, NCC, ACS, School of Counseling, Walden University

Abstract:
Training for practicing counselors involves training to help people learn to cope more effectively with mental health issues and developmental issues, along with life issues in general. Counselors are trained in a variety of techniques, based on the best available research and minimal standards based on professional accreditation. However, live training in the beginning, with actual patients is difficult since it is not practical to have counselors “practice” on people with actual psychological issues. Presented here is a proposed training system, called the “Cognitive, Interactive, Psychological Training System (CIPTS), which provides artificially intelligent profiles instantiated as “avatars for interaction with counselors,” based on a set of different personality profiles created by a team of leading counselor educators and represent a variety of psychological, social, and biopsychosocial health issues that can be used to help train counselors in a non-live, non-threatening environment, but yet valuable.

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Artificial Intelligence Profiles for Foundational Counselor Training Sessions (113 downloads)

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Human Cognition and Artificial Intelligence: The Artificial Prefrontal Cortex Revisited

Authors:
Dr. James A. Crowder, Systems Fellow, Colorado Engineering Inc.
Dr. Shelli Friess, LPC, NCC, ACS, School of Counseling, Walden University

Abstract:
Many researchers have postulated that human cognition is implemented by a multitude of relatively small, special purpose processes, almost always unconscious communication between them is rare and over a narrow bandwidth. Coalitions of such processes find their way into consciousness. This limited capacity work space available for our cognition serves to broadcast the message of the coalition to all the unconscious processors within the human brain, to recruit other processors to join in handling the current novel situation, or in solving the current problem. Therefore, consciousness in this theory allows us to deal with novelty or problematic situations that can’t be dealt with efficiently, or at all, by habituated unconscious processes. It provides access to appropriately useful resources, thereby solving the relevance problem. Here we present the design and testing of and Artificial Prefrontal Cortex (APC) model for use cognitive state transition and management in artificial cognition systems.

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Human Cognition and Artificial Intelligence: The Artificial Prefrontal Cortex Revisited (7 downloads)

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Enhancing Prosthetic Musculotendinous Proprioception Utilizing Multidisciplinary Artificially Intelligent Learning Approach

Authors:
John N. Carbone, Electrical and Computer Engineering Department, Southern Methodist University
Ryan A. Carbone, Biological Sciences, Baylor University
Dr. James A. Crowder, Systems Fellow, 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. Advanced biophysical and biomechanical prosthesis research shows that development of patient specific physiologically meaningful musculotendinous proprioception would generate a marked impact on reflex control, fine volitional motor control, and overall user experience.

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Enhancing Prosthetic Musculotendinous Proprioception utilizing Multidisciplinary Artificially Intelligent Learning Approach (6 downloads)

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Artificial Neural Diagnostics and Prognostics: Self-Soothing in Cognitive Systems

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

Abstract:
Self-diagnostics and prognostics in multi-agent processing systems is explored in the context of self-soothing concepts in neuropsychology. This is one of the first steps to facilitate systems-level thinking in AI. Autonomous or semi-autonomous system must be able to understand, at a system wide level, how every part of the system is influencing the other parts of the system.

Full access to this whitepaper requires submitting the form on the following page. Click the link below to continue.

Artificial Neural Diagnostics and Prognostics: Self-Soothing in Cognitive Systems (11 downloads)