Meta-interpreters for rule-based inference under uncertainty

One of the key challenges in designing expert systems is a credible representation of uncertainty and partial belief. During the past decade, a number of rule-based belief languages were proposed and implemented in applied systems. Due to their quasi-probabilistic nature, the external validity of these languages is an open question. This pape~ discusses the theory of belief revision in expert systems through a canonical belief calculus model which is invariant across different languages. A meta-interpreter for non-categorical reasoning is then presented. The purposes of this logic model is twofold: first, it provides a clear and concise conceptualization of belief representation and propagation in rule-based systems. Second, it serves as a working shell which cam be instantiated with different belief calculi. This enables experiments to investigate the net impact of alternative belief languages on the external validity of a fixed expert system. The ability to model uncertainty and belief revision is now considered a key challenge in designing credible expert systems. Regardless of whether the domain of expertise is medical diagnosis , venture capital, or oil exploration-human experts have to cope with uncertain data and inexact decision rules. Moreover, it is now an established fact that humans, laymen and experts alike, are very poor intuitive statisticians [34]. Specifically, human judgement under uncertainty is often irrational, to the extent that rationality is equated with the axioms of utility theory and subjective probability. 0,rep,hio There have been several attempts to represent uncertainty and belief revision within the rigid framework of logic, with Carnap's inductive logic [5] being the most seminal treatise on the subject. Pennsylvania. His research interests focus on the nexus of decision theory and artificial intelligence. His doctoral thesis won a 1988 Best Dissertation Award from ICIT, the International Center of Information Technologies. 1 The authors thank the editor and three anonymous referees for their thoughtful comments. Timothy W. Finin is a Technical Director at the Unisys Paoli Research Center. He has had over 18 years of experience in the applications of artificial intelligence to problems in database and knowledge base systems, expert systems, natural language processing, interactive systems, intelligent interfaces and robotics. Finin received the SB degree in Electrical En-ginecring from the Massachusettes Institute of Technology in 1971. From 1971 to 1974 he was a member of the research staff at the M.I.T. Artificial Intelligence Laboratory where he did research in the area of computer vision and robotics. He received …