Guided Incremental Construction of Belief Networks

Because uncertain reasoning is often intractable, it is hard to reason with a large amount of knowledge. One solution to this problem is to specify a set of possible models, some simple and some complex, and choose which to use based on the problem. We present an architecture for interpreting temporal data, called AIID, that incrementally constructs belief networks based on data that arrives asynchronously. It synthesizes the opportunistic control of the blackboard architecture with recent work on constructing belief networks from fragments. We have implemented this architecture in the domain of military analysis.

[1]  Victor R. Lesser,et al.  The Hearsay-II Speech-Understanding System: Integrating Knowledge to Resolve Uncertainty , 1980, CSUR.

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

[3]  Ramanathan V. Guha,et al.  Building Large Knowledge-Based Systems: Representation and Inference in the Cyc Project , 1990 .

[4]  Eric Joel Hovitz.  Computation and action under bounded resources , 1991 .

[5]  Victor Lesser,et al.  The Evolution of Blackboard Control Architectures , 1992 .

[6]  Sampath Srinivas,et al.  A Generalization of the Noisy-Or Model , 1993, UAI.

[7]  Robert P. Goldman,et al.  A Language for Construction of Belief Networks , 1993, IEEE Trans. Pattern Anal. Mach. Intell..

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

[9]  Kathryn B. Laskey,et al.  Network Fragments: Representing Knowledge for Constructing Probabilistic Models , 1997, UAI.

[10]  David Heckerman,et al.  A Tutorial on Learning with Bayesian Networks , 1998, Learning in Graphical Models.

[11]  H. Nii.  Blackboard Systems , 1998 .

[12]  Avi Pfeffer,et al.  SPOOK: A system for probabilistic object-oriented knowledge representation , 1999, UAI.

[13]  SPT : Hierarchical Agent Control : A Framework For Defining Agent Behavior Tracking , 2000 .

[14]  David L. Westbrook,et al.  Domain-general simulation and planning with physical schemas , 2000, 2000 Winter Simulation Conference Proceedings (Cat. No.00CH37165).

[15]  Stuart J. Russell,et al.  Dynamic bayesian networks: representation, inference and learning , 2002 .

[16]  Clayton T. Morrison,et al.  Temporal Abstraction in Bayesian Networks , 2003 .