Donald W. Dearholt
Department of Computer Science, NMSU

Abstract

The Pathfinder paradigm utilizes pairwise estimates or measures of proximity to form a family of networks intended to model aspects of the associations within human semantic memory.  This model supports clustering of similar concepts (and thus higher levels of abstraction) and minimum-cost paths, thus providing a well-defined associative structure to the concepts within a domain.  Recently, a method of modeling dynamic phenomena by incrementally constructing a Pathfinder network based upon counting co-occurring concepts at each sampling time has been developed, utilizing a canonical scenario.  This procedure can be viewed as computing the cumulative consensus over a set of adaptive agents, in which each agent has certain responsibilities for the storage of memories of the co-occurring phenomena.  This learning algorithm transforms sequential phenomena into a Pathfinder network representation, and thus provides a candidate model for the transition from episodic to semantic memory in humans.
 

Keywords

co-occurrence, clustering, dynamic systems, adaptive systems, discrete models, Pathfinder networks, consensus, agents, episodic memory, semantic memory, associative graphs, learning algorithm, machine learning