Evolutionary parallel computation on welfare intelligent agent

The paper proposes the realization of evolutionary computation on an intelligent agent using knowledge creation based on chaotic retrieval and parallel processing. This intelligent agent has the ability to self-organize knowledge, and the authors intend to introduce a kind of evolutionary computation into intelligent agents to make use of the flexibility in a group of various agents. This agent model consists of hierarchical parts which memorize fuzzy knowledge. Each hierarchical part retrieves the knowledge based on fuzzy associative inference on associative memories. Essentially, this inference in each part is parallel processing, and these hierarchical parts also work in parallel. Furthermore, a large number of agents work in parallel on the multi-agent model and its evolutionary computation. They realize parallel processing according to these parallel properties in the brain and in nature. When they realize a welfare intelligent robot, robots have to move in a suitable formation, in cooperation with the outer environment. Therefore, they apply the knowledge creation method to multi-agent robots which move abreast and simulate parallel processing of the multi-agent model as the basis for realizing evolutionary computation. They implement a parallel processing algorithm on an A-NET (Actors NETwork) parallel object-oriented computer, and show the usefulness of parallel processing for future evolutionary computation.