Patterns Discovery for Efficient Structured Probabilistic Inference

Lionel Torti (1) , Christophe Gonzales (1) , Pierre-Henri Wuillemin (1)

1 DECISION

In many domains where experts are the main source of knowledge, e.g., in reliability and risk management, a framework well suited for the modelisation, maintenance and exploitation of complex probabilistic systems is essential. In these domains, models usually define closed-world systems and result from the aggregation of multiple patterns repeated many times. Object Oriented-based Frameworks (OOF) such as Probabilistic Relational Models thus offer an effective way to represent such systems. OOFs define patterns as classes and substitute large Bayesian networks (BN) by graphs of instances of these classes. In this framework, Structured Inference avoids many computation redundancies by exploiting class knowledge, hence reducing BN inference times by orders of magnitude. However, to keep modeling and maintenance costs low, OOF classes often encode only generic situations. More complex situations, even those repeated many times, are only represented by combinations of instances. In this paper, we propose to determine such combination patterns and exploit them as classes to speed-up Structured Inference. We prove that determining an optimal set of patterns is NP-hard. We also provide an efficient algorithm to approximate this set and show numerical experiments that highlight its practical efficiency.

Domaines: Intelligence artificielle [cs.AI]

Lionel Torti, Christophe Gonzales, Pierre-Henri Wuillemin. Patterns Discovery for Efficient Structured Probabilistic Inference. SUM 2011 - 5th International Conference on Scalable Uncertainty Management, Oct 2011, Dayton, OH, United States. pp.247-260, ⟨10.1007/978-3-642-23963-2_20⟩. ⟨hal-00660477⟩