A Framework for Reasoning Under Uncertainty with Temporal Constraints

Time is the key stimulus to change, causality and interaction which are the main components of a dynamic world. Therefore, the modeling of knowledge, especially in complex and dynamic domains like economics, sociology, and ecology, must incorporate the concept of time. Although there has been much research over the years on the representation of knowledge (causality, implication, and uncertainty) and on the representation of time, it has been a continuing challenge to unify them in a meaningful and useful fashion. In this paper, we propose a framework for reasoning under uncertainty with temporal constraints. The framework is extended from Bayesian knowledge-bases (BICBs), which represent knowledge in an "if-then" structure and represent uncertainty based on probability theory. By adding temporal constraints to BICBs, the framework provides a comprehensive model that incorporates the semantics of both time and uncertainty.