The Pathfinder paradigm utilizes pairwise estimates or measures of proximity to form a family of networks intended to model aspects of human semantic memory. This model supports clustering of similar concepts, higher levels of ion, and minimum-cost paths, thus providing a reasonably well-defined structure to the concepts within a domain. Recently, a method of modeling a dynamic phenomenon 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. An extension of this procedure allows the adaptive agents to participate in evolutionary processes in the representation and exchange of their memories of present and past events, by the concurrent application of operations to the agents at each time step. This generates a population of agents within each scenario which differ from those in the canonical scenario. These scenarios yield cumulative consensus and Pathfinder networks which are different from those of the canonical network. Candidate operators and measures of fitness are proposed, and potential advantages and difficulties are discussed.
['Donald W. Dearholt']
The Cumulative Consensus of Cognitive Agents in Scenarios: A Framework for Evolutionary Processes in Semantic Memory