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We developed the language of Modifiable Temporal Belief Networks (MTBNs) as astructural and temporal extension of Bayesian Belief Networks (BNs) tofacilitate normative temporal and causal modeling under uncertainty. In thispaper we present definitions of the model, its components, and its fundamentalproperties. We also discuss how to represent various types of temporalknowledge, with an emphasis on hybrid temporal-explicit time modeling, dynamicstructures, avoiding causal temporal inconsistencies, and dealing with modelsthat involve simultaneously actions (decisions) and causal and non-causalassociations. We examine the relationships among BNs, Modifiable BeliefNetworks, and MTBNs with a single temporal granularity, and suggest areas ofapplication suitable to each one of them.

A Structurally and Temporally Extended Bayesian Belief Network Model: Definitions, Properties, and Modeling Techniques