Practical Issues in Modeling Large Diagnostic Systems with Multiply Sectioned Bayesian Networks

As Bayesian networks become widely accepted as a normative formalism for diagnosis based on probabilistic knowledge, they are applied to increasingly larger problem domains. These large projects demand a systematic approach to handle the complexity in knowledge engineering. The needs include modularity in representation, distribution in computation, as well as coherence in inference. Multiply Sectioned Bayesian Networks (MSBNs) provide a distributed multiagent framework to address these needs. According to the framework, a large system is partitioned into subsystems and represented as a set of related Bayesian subnets. To ensure exact inference, the partition of a large system into subsystems and the representation of subsystems must follow a set of technical constraints. How to satisfy these goals for a given system may not be obvious to a practitioner. In this paper, we address three practical modeling issues.

[1]  Enrique F. Castillo,et al.  Expert Systems and Probabilistic Network Models , 1996, Monographs in Computer Science.

[2]  Eric Horvitz,et al.  Dynamic Network Models for Forecasting , 1992, UAI.

[3]  Judea Pearl,et al.  Probabilistic reasoning in intelligent systems - networks of plausible inference , 1991, Morgan Kaufmann series in representation and reasoning.

[4]  Yang Xiang,et al.  A Probabilistic Framework for Cooperative Multi-Agent Distributed Interpretation and Optimization of Communication , 1996, Artif. Intell..

[5]  Yang Xiang,et al.  Multiply sectioned Bayesian networks for neuromuscular diagnosis , 1993, Artif. Intell. Medicine.

[6]  Nicholas R. Jennings,et al.  Intelligent agents: theory and practice , 1995, The Knowledge Engineering Review.

[7]  David Heckerman,et al.  Decision-theoretic troubleshooting , 1995, CACM.

[8]  David Poole,et al.  MULTIPLY SECTIONED BAYESIAN NETWORKS AND JUNCTION FORESTS FOR LARGE KNOWLEDGE‐BASED SYSTEMS , 1993, Comput. Intell..

[9]  Yang Xiang.  Verification of DAG structures in cooperative belief network-based multiagent systems , 1998, Networks.

[10]  Pxut CtsMedn.  Veriication of Dag Structures in Cooperative Belief Network Based Multi-agent Systems , 1998 .

[11]  Kathryn B. Laskey,et al.  Network Engineering for Complex Belief Networks , 1996, UAI.

[12]  Richard E. Neapolitan,et al.  Probabilistic reasoning in expert systems - theory and algorithms , 2012 .

[13]  Avi Pfeffer,et al.  Object-Oriented Bayesian Networks , 1997, UAI.

[14]  Yang Xiang.  Verification of DAG structures in cooperative belief network‐based multiagent systems , 1998 .

[15]  Michael Wooldridge,et al.  Intelligent agents: theory and practice The Knowledge Engineering Review , 1995 .

[16]  David Heckerman,et al.  Probabilistic similarity networks , 1991, Networks.

[17]  Alan H. Bond,et al.  Readings in Distributed Artificial Intelligence , 1988 .

[18]  Yang Xiang.  Cooperative triangulation in MSBNs without revealing subnet structures , 2001, Networks.

[19]  Sampath Srinivas,et al.  A Probabilistic Approach to Hierarchical Model-based Diagnosis , 1994, UAI.

[20]  Gregory F. Cooper,et al.  The Computational Complexity of Probabilistic Inference Using Bayesian Belief Networks , 1990, Artif. Intell..