Computing with causal theories

Abstract

Formalizing commonsense knowledge for reasoning about time has long been a central issue in Artificial Intelligence (AI). It has been recognized that the existing formalisms do not provide satisfactory solutions to some fundamental problems of AI, viz. the frame problem. Moreover, it has turned out that the inferences drawn by these systems do not always coincide with those one had intended when he wrote the axioms. These issues call for a well-defined formalism and useful computational utilities for reasoning about time and change. Yoav Shoham of Stanford University inti'oduced in his 1986 Yale doctoral thesis an appealing temporal nonmonotonic logic, the logic of chronological ignorance, and identified a class of theories, causal theories, which have computationally simple model-theoretic properties. This thesis is a study towards building upon Shoham's work on causal theories for the latter are somewhat limited. The thesis mainly centers around improving computational aspects of causal theories while preserving their model-theoretic properties.