PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Multi-agent causal models for dependability analysis
Sam Maes and Philippe Leray
In: First International Conference on Availability, Reliability and Security (ARES 2006), Bayesian Networks in Dependability (BND 2006) workshop, 20-22 April 2006, Vienna, Austria.

Abstract

In this paper we discuss multi-agent causal models, which are an extension of causal Bayesian networks to the multi-agent case. In this paper we illustrate how these re- cently introduced models could prove useful for dependabil- ity analysis. Their main difference with other graphical modeling techniques that have been applied to the prob- lem is that multi-agent causal models allow for multi-agent, privacy-preserving quantitative causal inference in models with hidden variables.

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EPrint Type:Conference or Workshop Item (Paper)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Learning/Statistics & Optimisation
Theory & Algorithms
ID Code:2104
Deposited By:Philippe Leray
Deposited On:07 May 2006