  We developed the language of Modifiable Temporal Belief Networks (MTBNs) as a
structural and temporal extension of Bayesian Belief Networks (BNs) to
facilitate normative temporal and causal modeling under uncertainty. In this
paper we present definitions of the model, its components, and its fundamental
properties. We also discuss how to represent various types of temporal
knowledge, with an emphasis on hybrid temporal-explicit time modeling, dynamic
structures, avoiding causal temporal inconsistencies, and dealing with models
that involve simultaneously actions (decisions) and causal and non-causal
associations. We examine the relationships among BNs, Modifiable Belief
Networks, and MTBNs with a single temporal granularity, and suggest areas of
application suitable to each one of them.
