Causal maps for explanation in multi-agent system

Abstract

All the scientific community cares about is understanding the complex systems, and explaining their emergent behaviors. We are interested particularly in Multi-Agent Systems (MAS). Our approach is based on three steps : observation, modeling and explanation. In this paper, we focus on the second step by offering a model to represent the cause and effect relations among the diverse entities composing a MAS. Thus, we consider causal reasoning of great importance because it models causalities among a set of individual and social concepts. Indeed, multiagent systems, complex by their nature, their architecture, their interactions, their behaviors, and their distributed processing, needs an explanation module to understand how solutions are given, how the resolution has been going on, how and when emergent situations and interactions have been performed. In this work, we investigate the issue of using causal maps in multi-agent systems in order to explain agent reasoning. © 2013 Springer-Verlag.