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