This paper proposes a new method to improve cooperation in concurrent systems within the framework of Multi-Agent Systems (MAS) by utilizing reinforcement learning. When subsystems work independently and concurrently, achieving appropriate cooperation among them is important to improve the effectiveness of the overall system. Treating subsystems as agents makes it easy to explicitly deal with the interactions among them since they can be modeled naturally as communication among agents with intended information. In our approach agents try to learn the appropriate balance between exploration and exploitation via reward, which is important in distributed and concurrent problem solving in general. By focusing on how to give reward in reinforcement learning, not the learning equation, two kinds of reward are defined in the context of cooperation between agents, in contrast to reinforcement learning within the framework of single agent. In our approach reward for insistence by individual agent contributes to facilitating exploration and reward for concession to other agents contributes to facilitating exploitation. Our cooperation method was examined through experiments on the design of micro satellites and the result showed that it was effective to some extent to facilitate cooperation among agents by letting agents themselves learn the appropriate balance between insistence and concession. The result also suggested the possibility of utilizing the relative magnitude of these rewards as a new control parameter in MAS to control the overall behavior of MAS.

In this paper, we propose a new cooperative search algorithm based on pheromone communication for solving the Vehicle Routing Problems. In this algorithm, multi-agents can partition the problem cooperatively and search partial solutions independently using pheromone communication, which mimics the communication method of real ants. Through some computer experiments the cooperative search of multi-agents is confirmed.

This paper proposes a new method to improve cooperation in concurrent systems within the framework of Multi-Agent Systems (MAS). Since subsystems work concurrently, achieving appropriate cooperation among them is important to improve the effectiveness of the overall system. When subsystems are modeled as agents, it is easy to explicitly deal with the interactions among them since they can be modeled naturally as communication among agents with intended information. Contrary to previous approaches which provided the syntax of communication protocols without semantics, we focus on the semantics of cooperation in MAS and aim at allowing agents to exploit the communicated information for cooperation. This is attempted by utilizing more coarse-grained communication based on the different perspective for the balance between formality and richness of communication contents so that each piece of communication contents can convey more meaningful information in application domains. In our approach agents cooperate each other by giving feedbacks based on the metaphor of explanation which is widely used in human interactions, in contrast to previous approaches which use direct orders given by the leader based on the pre-defined cooperation strategies. Agents show the difference between the proposal and counter-proposals for it, which are constructed with respect to the former and given as the feedbacks in the easily understandable terms for the receiver. From the comparison of proposals agents retrieve the information on which parts are agreed and disagreed by the relevant agents, and reflect the analysis in their following behavior. Furthermore, communication contents are annotated by agents to indicate the degree of importance in decision making for them, which contributes to making explanations or feedbacks more understandable. Our cooperation method was examined through experiments on the design of micro satellites and the result showed that it was effective to some extent to facilitate cooperation among agents.

This paper describes a mobile information access system based on a multi-agent architecture. With the rapid progress of wireless data communications, mobile Internet access will be more and more popular. In mobile environments, user location plays an important role for information filtering and flexible communication service. In this paper, we propose a mobile information service system where a user with a handy terminal accesses Internet in an open air to look up map information and related town information. Each user information is managed by an independent agent process. And the agent provides each user with a personal service collaborating with other applications. A map-based information service example based on this architecture is also described.

This paper describes COCE (COordinative Character Extractor), a method for extracting printed Japanese characters and their character strings from all sorts of document images. COCE is based on a multi-agent system where each agent tries to find a character string and extracts the characters in it. For the adaptability, the agents are allowed to look after arbitrary parts of documents and extract the characters using only the knowledge independent of the layouts. Moreover, the agents check and correct their results sometimes with the help of the other agents. From experimental results, we have verified the effectiveness of our approach.

The concept of flexible system is long being used by many researchers, aiming to solve some particular problem of adaptation. The problem is viewed differently in different situations. In this paper, we first give a set of definitions and specifications to generalize this concept applicable to any system and in particular to communication networks. Through these definitions we will formalize, what are the conditions a system should satisfy to be called as a Flexible Communication System. The rest of the paper we formalize the concepts of flexible information network, and propose an agent oriented architecture that can realize it.