A Distributed Problem-Solving Approach to Rule Induction: Learning in Distributed Artificial Intelligence Systems
نویسندگان
چکیده
One of the interesting characteristics of multi-agent problem solving in distributed artificial intelligence(DAI) systems is that the agents are able to learn from each other, thereby facilitating the problem-solving process and enhancing the quality of the solution generated. This paper aims at studying the multi-agent learning mechanism involved in a specific group learning situation: the induction of concepts from training examples. Based on the mechanism, a distributed problem-solving appmch to inductive learning, referred to as DLS, is developed and analyzed. This approach not only provides a method for solving the inductive learning problem in a distributed fashion, it also helps shed light on the essential elements contributing to multi-agent learning in DAI systems. An empirical study is used to evaluate the efficacy of DLS for d e induction as well as its performance patterns in relation to various group m e t e r s . The ensuing analysis helps form a model for characterizing multi-agent learning.
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