Collective memory search

Abstract

Collective action has been examined to expedite search in optimization problems [2]. Collective memory has been applied to learning in multiagent systems [4]. We integrate the simplicity of collective action with the pattern detection of collective memory to significantly improve both the gathering and processing of knowledge. We augment distributed search in genetic programming based systems with collective memory. Four models of collective memory search are defined based on the interaction of the search agents and the process agents which manipulate the collective memory. We implement one of the collective memory search models and show how it facilitates "scaling up" a problem domain. A Passive- Active model, in which the gathered results are collated. is employed by the process agents to piece together the solution from the parts collected by the search agents. © 1997 ACM.