Constructing Flexible Dynamic Belief Networks from First-Order Probabilistic Knowledge Bases

Abstract. This paper investigates the power of first-order probabilistic logic (FOPL) as a representation language for complex dynamic situations. We introduce a sublanguage of FOPL and use it to provide a first-order version of dynamic belief networks. We show that this language is expressive enough to enable reasoning over time and to allow procedural representations of conditional probability tables. In particular, we define decision tree representations of conditional probability tables that can be used to decrease the size of the created belief networks. We provide an inference algorithm for our sublanguage using the paradigm of knowledge-based model construction. Given a FOPL knowledge base and a particular situation, our algorithm constructs a propositional dynamic belief network, which can be solved using standard belief network inference algorithms. In contrast to common dynamic belief networks, the structure of our networks is more flexible and better adapted to the given situation. We demonstrate the expressive power of our language and the flexibility of the resulting belief networks using a simple knowledge base modeling the propagation of infectious diseases.