An intelligent peer-to-peer multi-agent system for collaborative management of bibliographic databases

This paper describes the design of a peer-to-peer system for collaborative management of distributed bibliographical databases. The goal of this system is twofold: firstly, it aims at providing help for users to manage their local bibliographical databases. Secondly, it offers the possibility to exchange bibliographical data among like-minded user groups in an implicit and intelligent manner. Each user is assisted by a personal agent that provides help such as: filling in bibliographical records, verifying the correctness of information entered and more importantly, recommendation of relevant bibliographical references. To do this, the personal agent needs to collaborate with its peers in order to get relevant recommendations. Each agent applies a case-based reasoning approach in order to provide peers with requested recommendations. The paper focuses mainly on describing the recommendation computation approach

Learning to Form Dynamic Committees

Learning agents can improve performance when they cooperate with other agents. Specifically, learning agents forming a committee outperform individual agents. This "ensemble effect" is well know for multi-classifier systems in Machine Learning. However, multi-classifier systems assume all data is know to all classifiers while we focus on agents that learn from cases (examples) that are owned and stored individually. In this article we focus on the selection of the agents that join a committee for solving a problem. Our approach is to frame committee membership as a learning task for the convener agent. The committee convener agent learns to form a committee in a dynamic way: at each point in time the convener agent decides whether it is better to invite a new member to join the committee (and which agent to invite) or to close the membership. The convener agent performs learning in the space of voting situations, i.e. learns when the current committee voting situation is likely to solve correctly (or not) a problem. The learning process allows an agent to decide when to individually solve a problem, when it is better to convene a committee, and which individual agents to be invited to join the committee. Our experiments show that learning to form dynamic committees results in smaller committees while maintaining (and sometimes improving) the problem solving accuracy