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
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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