Probabilistic Causal Reasoning
This work addresses the challenge of making accurate predictions in decision-making scenarios with limited information, though it appears incremental as it builds on existing nonmonotonic temporal reasoning schemes.
The paper tackles the problem of predictive inference under uncertainty by developing a theory of causal reasoning that integrates probability with temporal projection to handle persistence, resulting in a polynomial-time algorithm for determining probabilities of consequences and a prototype system for refining causal rules in a manufacturing domain.
Predicting the future is an important component of decision making. In most situations, however, there is not enough information to make accurate predictions. In this paper, we develop a theory of causal reasoning for predictive inference under uncertainty. We emphasize a common type of prediction that involves reasoning about persistence: whether or not a proposition once made true remains true at some later time. We provide a decision procedure with a polynomial-time algorithm for determining the probability of the possible consequences of a set events and initial conditions. The integration of simple probability theory with temporal projection enables us to circumvent problems that nonmonotonic temporal reasoning schemes have in dealing with persistence. The ideas in this paper have been implemented in a prototype system that refines a database of causal rules in the course of applying those rules to construct and carry out plans in a manufacturing domain.