A Model-Driven Approach to Integrated Cognition

Abstract

Cognitive Architectures are used to test theoretical and conceptual frameworks identifying and explaining the underlying components of thought, namely the essential structures, mechanisms, and processes realized through models of human-like information processing. They define and prescribe those boundaries deemed both necessary and sufficient for intelligent agents based on our current understanding of human cognition. The Common Model of Cognition (CMC) attempts to establish a community consensus on theoretical commitments and assumptions built into commonly used architectures, and group the assumptions according to structure and processing, memory, learning, and perceptual interfaces. The CMC remains a verbal-conceptual consensus across broad theories essential for general phenomenon (i.e., a Meta-Model of Cognition), however, lacks a formal specification to support domain-general task model comparisons for evaluation and validation of new theories implemented in new or existing architectures, or specific micro-theoretic implementations as cognitive architecture models and task models. Thus, a lack of a formal model supporting the CMC inhibits exploration of philosophical enquiries about core theoretical assumptions, and the adoption of refined peripheral theories across architectures. This thesis presents a formal meta-model specific to the constraints represented at Newell's Cognitive level using the principles of Model-Driven Design (MDD) to encapsulate these entities and relationships across architectures. This formal model can be used as a framework generator, and to specify the abstract interfaces common across Common Model agents, allowing modelers to explore verbal-conceptual theories through experimentation with virtual environments, further supporting a common ground. Frameworks generated through MDD support an empirical evaluation and comparison of variations on the Common Model for the purpose of application to Artificial Intelligence problem domains, lending additional credibility to computational Cognitive Modeling as a formal discipline, and the Cognitive Science research enterprise as a whole.