Naoki Kohata

Dynamic formation on mobile agents and its evolutionary parallel computation

Proposes a chaotic evolutionary computation algorithm instead of a conventional genetic algorithm for such intelligent agents as welfare robots which assist humans. This evolutionary computation is realized by applying chaotic retrieval and soft DNA (soft computing oriented data driven functional scheduling architecture) on associative memories. We apply this evolutionary computation to multi-agent robots which move abreast and intelligent transport systems. Essentially, the process of this evolutionary computation is parallel processing. Therefore, we implement its parallel processing algorithm on A-NET (actors network) parallel object-oriented computer, and show the usefulness of parallel processing for the proposed evolutionary computation.

Chaotic evolutionary processing and its applications

Proposes the realization of evolutionary computation by chaotic dynamics instead of conventional genetic algorithms for such intelligent agents as welfare robots which assist humans. This evolutionary computation is realized by applying chaotic retrieval on associative memories. We apply this evolutionary computation to multi-agent robots which move abreast. Essentially, the process of this evolutionary computation is parallel processing. Therefore, we implement its parallel processing algorithm on an A-NET(Actors NETwork) parallel object-oriented computer, and show the usefulness of parallel processing for the proposed evolutionary computation.

Evolutionary Computation for Intelligent Agents Based on Chaotic Retrieval and Soft DNA

This paper proposes a chaotic evolutionary computation algorithm instead of conventional GA (Genetic Algorithm) for such intelligent agents as welfare robots which assist humans. This evolutionary computation is realized by applying chaotic retrieval and Soft DNA (Soft computing oriented Data driven fuNctional scheduling Architecture) on associative memories. We apply this evolutionary computation to multi-agent robots which move abreast and ITS(Intelligent Transport System). Essentially, the process of this evolutionary computation is parallel processing. Therefore, we implement its parallel processing algorithm on A-NET (Actors NETwork) parallel object-oriented computer, and show the usefulness of parallel processing for proposed evolutionary computation.

Dynamic formation generating for intelligent transport systems using algorithm to select function by environmental information

In a multi-agent system, agents must have functions to work in cooperation with other agents. To produce the cooperative works, we propose soft DNA (soft computing oriented data driven functional scheduling architecture) to change dynamically an agents control block based on the fuzzy associative memory organizing units system (FAMOUS) and a conceptual fuzzy set (CFS). Its function is to alot each agents role in the agents group and select the role dynamically according to circumstance. We apply this system to intelligent transport systems.

Dynamic formation generating in mobile-agents using algorithm to select function by environmental information

In a multi-agent system, agents must have functions to work with in cooperation with other agents. To produce the cooperative works, we propose Soft DNA (soft computing oriented data driven functional scheduling architecture) to change dynamically agents control block based on the fuzzy associative memory organizing units system and conceptual fuzzy set. It is a function to allow each agents role in the agents group and select the role dynamically according to the circumstance. We apply this system to intelligent transport systems, and simulation was performed.

Intelligent transport systems based on diversity growing using evolutionary parallel computation

This paper proposes the generation of diverse intelligence based on a chaotic evolutionary computation algorithm instead of conventional GA (genetic algorithm) for such mobile-agent systems as intelligent transport systems (ITS). This evolutionary computation is realized by applying chaotic retrieval and soft DNA (soft computing oriented data driven functional scheduling architecture) on associative memories. We apply this evolutionary computation to multiagent systems such as ITS. Essentially, the process of this evolutionary computation is parallel processing. Therefore, we also propose its parallel processing algorithm. We show the usefulness of the proposed method by means of simulation experiment.

Knowledge generation for intelligent agent using evolutionary chaotic retrieval

Scientific technology is evolving which is used in many fields. But, accompanying the deepening of the technology, using sophisticated technology needs specific technology. Because of this, technologies that enable us to use the sophisticated technology are required. It is required that various people can use sophisticated technologies to be used in many fields. The needed technology is intelligent agents. The technology makes people who have no skill able to use sophisticated technology. We researched a driving support agent as an example of an intelligent agent, which is similar to a human support robot.

Diversity oriented evolutionary parallel computation on intelligent agents

Proposes the generation of diverse intelligence on a chaotic evolutionary computation algorithm. This evolutionary computation is realized by applying chaotic retrieval and Soft DNA (Soft computing oriented Data driven fuNctional scheduling Architecture) on associative memories. We apply this evolutionary computation to multi-agent systems such as ITS.

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.