Horvitz, E. J.


Decision theory in expert systems and artificial intelligence

Classics

Despite their different perspectives, artificial intelligence (AI) and the disciplines of decision science have common roots and strive for similar goals. This paper surveys the potential for addressing problems in representation, inference, knowledge engineering, and explanation within the decision-theoretic framework. Recent analyses of the restrictions of several traditional AI reasoning techniques, coupled with the development of more tractable and expressive decision-theoretic representation and inference strategies, have stimulated renewed interest in decision theory and decision analysis. We describe early experience with simple probabilistic schemes for automated reasoning, review the dominant expert-system paradigm, and survey some recent research at the crossroads of AI and decision science.


Problem-solving design: Reasoning about computational value, trade-offs, and resources

Classics

The long-term goal of our field is the creation and understanding of intelligence. Productive research in AI, both practical and theoretical, benefits from a notion of intelligence that is precise enough to allow the cumulative development of robust systems and general results. The concept of rational agency has long been considered a leading candidate to fulfill this role. This paper outlines a gradual evolution in the formal conception of rationality that brings it closer to our informal conception of intelligence and simultaneously reduces the gap between theory and practice.


A framework for comparing alternative formalisms for plausible reasoning

Classics

We present a logical relationship between a small number of intuitive properties for measures of belief and the axioms of probability theory. In this paper, we discuss the ramifications of a proof showing that the axioms of probability theory follow logically from a set of simple properties. It is useful to decompose the problem of reasoning under uncertainty in to three distinct components: problem formulation, initial belief assignment, and belief entailment. We refer to the use of' a single real number to represent continuous measures of belief as the Another assertion is that belief in the negation of a proposition Q, denoted -Q, should be determined by the belief in the proposition itself.