PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Distributed learning of multi-agent causal models
Sam Maes, Stijn Meganck, Bernard Manderick and Philippe Leray
In: IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology (IAT 2005), 19-22 Sept 2005, Compiegne, France.

Abstract

In this paper we propose a distributed structure learning algorithm for the recently introduced Multi-Agent Causal Models (MACMs). MACMs are an extension of Causal Bayesian Networks (CBN) to a distributed domain. In this setting it is assumed that there is no single database containing all the information of the domain. Instead, there are several sites holding non-disjoint subsets of the domain variables. At each site there is an agent capable of learning a local causal model. We study the possibility of combining the information of the local models into one globally consistent model. We propose an algorithm that yields the possibility to learn new local structures that can be combined to perform globally consistent causal inference.

EPrint Type:Conference or Workshop Item (Paper)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Learning/Statistics & Optimisation
ID Code:1478
Deposited By:Philippe Leray
Deposited On:28 November 2005