Associative Memory-Based Reasoning: A Computational Model of Analogy-Making in a Decentralized Multi-Agent Cognitive Architecture

Through analogy, novel situations and problems can be understood in terms of familiar ones. There is converging evidence that analogy-making lies at the very core of human cognition. Conversely, successful analogy-making requires the resources of an entire cognitive architecture. This book describes a computational model of analogy-making called AMBR (Associative Memory-Based Reasoning). AMBR is based on a hybrid symbolic-connectionist multi-agent cognitive architecture called DUAL. Macroscopic behavior in DUAL emerges from the interactions of simple processing agents in dynamic coalitions. Unlike the mainstream models of analogy-making, AMBR uses a decentralized representational scheme for problems and situations. The dynamic emergent processing of these decentralized representations is consistent with the context-sensitive and constructive nature of human memory. Both DUAL and AMBR were developed by Boicho N. Kokinov and his graduate students at New Bulgarian University. This book is a revised and expanded version of the author's Ph.D. thesis written under Prof. Kokinov's supervision at NBU. It will be of interest to cognitive modelers and cognitive scientists more generally.