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.
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.