Modeling a Dynamic and Uncertain World I: Symbolic and Probabilistic Reasoning About Change

Intelligent agency requires some ability to predict the future. An agent must ask itself what is presently its best course of action given what it now knows about what the world will be like when it intends to act. This paper presents a system that uses a probabilistic model to reason about the eeects of an agent's proposed actions on a dynamic and uncertain world, computing the probability that relevant propositions will hold at a speciied point in time. The model allows for incomplete information about the world, the occurrence of exogenous (unplanned) events, unreliable sensors, and the possibility of an imperfect causal theory. The system provides an application program with answers to questions of the form \is the probability that ' will hold in the world at time t greater than ?" It is unique among algorithms for probabilistic temporal reasoning in that it tries to limit its inference according to the proposition, time, and probability threshold provided by the application. The system will also notify the application if subsequent evidence invalidates its answer to a query. Acknowledgements Thanks to Denise Draper, Oren Etzioni, and Dan Weld, who commented on several drafts. David Madigan improved the presentation as well. The referees made careful and helpful comments on an earlier version.