PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

A learning algorithm for multi-agent causal models
Stijn Meganck, Sam Maes, Bernard Manderick and Philippe Leray
In: Third European Workshop on Multi-Agent Systems EUMAS 2005, 7-8 Dec 2005, Bruxelles, Belgique.

Abstract

In this paper we propose a structure learning algorithm for Multi-Agent Causal Models, which are an extension of Causal Bayesian Networks to a distributed domain. It is assumed that there is no single agent that has all the information of the domain, instead there are several agents each having access to non-disjoint subsets of the domain variables. Every agent has a causal model, determined by an acyclic causal diagram and a joint probability distribution over its observed variables. We thoroughly study the problems that arise due to the fact that no single agent has complete knowledge and discuss several ways to circumvent them. We propose an algorithm that yields the possibility to learn new local structures that can be combined to perform globally consistent causal inference using prior knowledge on the topology of the learned local networks.

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EPrint Type:Conference or Workshop Item (Paper)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Learning/Statistics & Optimisation
ID Code:1481
Deposited By:Philippe Leray
Deposited On:07 January 2006