Multiagent Inductive Learning: an Argumentation-based Approach

Speaker: Enric Plaza

Affiliation: Spanish Council for Scientific Research

Time: Tuesday 13/11/2012 from 14:30 to 15:30

Venue: Access Grid UWS. Presented from Penrith (Y239), accessible from Parramatta (EB.1.32) and Campbelltown (26.1.50).

Abstract: Multiagent Inductive Learning is the problem that groups of agents face when they want to perform inductive learning, but the data of interest is distributed among them. This paper focuses on concept learning, and presents A-MAIL, a framework for multiagent induction integrating ideas from inductive learning, case-based reasoning and argumentation. Argumentation is used as a communication framework with which the agents can communicate their inductive inferences to reach shared and agreed-upon concept definitions.

Biography: Prof Enric Plaza holds a Ph.D. in Computer Science by the Technical University of Catalonia (UPC) and is Research Professor of the Spanish Council for Scientific Research (CSIC). His research takes place at the Barcelona Artificial Intelligence Research Institute (IIIA) since 1988, where he is currently head of the Learning Systems Department. He has worked on knowledge acquisition, case-based reasoning, and machine learning in a dozen of European and Spanish projects. He has chaired three international conferences on A.I. fields and has authored over 120 scientific papers, among them one that is the most cited paper in the field of case-based reasoning. His research is now focused on new techniques for case-based reasoning, learning in the framework of multiagent systems and on the use of ontologies in that framework. Currently, he is working on learning from communication using computational argumentation. He is member of a dozen program committees annually for international conferences, plus other conferences and workshops. He is ECCAI fellow and has served as Chairman of the Board of Trustees of the ACIA (Catalan Association for Artificial Intelligence) duringfour years and frequently publishes articles for AI popularization.