The Virtual Secretary 2 project (ViSe2) focuses on the construction of a multi-agent cooperation system. As a research vehicle, we have chosen to build intelligent agents that perform secretarial tasks for their users either by themselves or via cooperation. An individual ViSe2 agent has limited

to help agents attain such abilities and thus achieve e#cient cooperation. We propose a twin-base system #cooperator-base U task-base# to monitor the other agents' activities in a small agent group. The cooperator-base collects stable information of the others and acts as an auxiliary base

subpopulations, and test together in the common task. In this paper, such a method, called Multi-Agent ESP (Enforced Subpopulations) is presented, and demonstrated in a prey-capture task. The approach is shown more efficient and robust than evolving a single central controller for all agents. The role

their way and pursue it autonomously, and to be able to adapt and learn. In multi-agent systems, many intelligent agents that can interact with each other, cooperate to achieve a set of goals. Because of the inherent complexity that exists in dynamic and changeable multiagent environments, there is always a

the optimal operating point, efficient, fair, and incentive compatible spectrum sharing becomes possible. We present examples that illustrate that in many cases the performance loss due to selfish behavior is small. We also prove that our results are tight and quantify the best achievable performance in a non-cooperative

to be most efficient through stigmergy, i.e. through role-based responses to the environment, rather than direct communication between the agents. Together these results suggest that role-based cooperation is an effective strategy in certain multi-agent domains. [ This paper is a revision of AI01-287.]

ch. In setting up the test environment, modeling and the execution of the model are not distinguished. This hampers a reuse of test scenarios, reproducing the results of experiments, and comparison of results achieved through experimentation. The requirements of a simulation system which has a

. The system presented in this paper is driven to personalization and efficiency. Personalization is achieved representing explicitly a fuzzy reputation of other agents. Therefore, the interaction among agents will be closer to human way of thinking. The system deals with changes in fuzzy reputation values

to cooperate with other agents and learn how to become experts. I am studying these themes in several domains to achieve a more broader understanding of how learning is influ-enced by different multi-agent domains and thus how learning algorithms can be developed. The research aims at answering the following

This paper analyzes the use of a multi-agent System for Model-Based Diagnosis. In a large dynamical system, it is often infeasible or even impossible to maintain a model of the whole system. Instead, several incomplete models of the system have to be used to detect possible faults. These models may