TitleCooperative Case-Based Reasoning
Publication TypeBook Chapter
Year of Publication1997
AuthorsPlaza E, Arcos JLluis, Martin F
EditorWeis G
Book TitleArtificial Intelligence Meets Machine Learning. Lecture Notes in Artificial Intelligence
Volume1221
Pagination180-201
PublisherSpriger-Verlag
Abstract

We are investigating possible modes of cooperation among homogeneous agents with learning capabilities. In this paper we will be focused on agents that learn and solve problems using Case-based Reasoning (CBR), and we will present two modes of cooperation among them: Distributed Case-based Reasoning (DistCBR) and Collective Case-based Reasoning (ColCBR). We illustrate these modes with an application where different CBR agents able to recommend chromatography techniques for protein purification cooperate. The approach taken is to extend Noos, the representation language being used by the CBR agents. Noos is knowledge modeling framework designed to integrate learning methods and based on the task/method decomposition principle. The extension we present, Plural Noos, allows communication and cooperation among agents implemented in Noos by means of three basic constructs: alien references, foreign method evaluation, and mobile methods.