The Association for the Advancement of Artificial Intelligence (AAAI) held its 1997 Spring Symposium Series on 24 to 26 March at Stanford University in Stanford, California. This article contains summaries of the seven symposia that were conducted: (1) Artificial Intelligence in Knowledge Management; (2) Computational Models for Mixed-Initiative Interaction; (3) Cross-Language Text and Speech Retrieval; (4) Intelligent Integration and Use of Text, Image, Video, and Audio Corpora; (5) Natural Language Processing for the World Wide Web; (6) Ontological Engineering; and (7) Qualitative Preferences in Deliberation and Practical Reasoning.

The Association for the Advancement of Artificial Intelligence (AAAI) held its 1997 Spring Symposium Series on 24 to 26 March at Stanford University in Stanford, California. This article contains summaries of the seven symposia that were conducted: (1) Artificial Intelligence in Knowledge Management; (2) Computational Models for Mixed-Initiative Interaction; (3) Cross-Language Text and Speech Retrieval; (4) Intelligent Integration and Use of Text, Image, Video, and Audio Corpora; (5) Natural Language Processing for the World Wide Web; (6) Ontological Engineering; and (7) Qualitative Preferences in Deliberation and Practical Reasoning.

Probabilistic rules and their variants have recently supported several successful applications of expert systems, in spite of the difficulty of committing informants to particular conditional probabilities or ";certainty factors"; and in spite of the experimentally observed insensitivity of system performance to perturbations of the chosen values. Here we survey recent developments concerning reasoned assumptions which offer hope for avoiding the practical elusiveness of probabilistic rules while retaining theoretical power, for basing systems on the information unhesitatingly gained from expert informants, and reconstructing the entailed degrees of belief later.

Editors' Note: Many expert systems require some means criticisms of this approach from those steeped in the practical of handling heuristic rules whose conclusions are less than certain issues of constructing large rule-based expert systems. Abstract the expert system draws inferences in solving different problems. Doyle's paper argues that it is difficult for a human expert "certainty factors," and in spite of the experimentally observed insensitivity of system performance to perturbations of the chosen values Recent successes of "expert systems" stem from much Research Projects Agency (DOD), ARPA Order No. 3597, monitored In the following, we explain the modified approach together with its practical and theoretical attractions. The client's income bracket is 50%, can be found (Minsky, 1975; Shortliffe & Buchanan, 1975; and 2. The client carefully studies market trends, Duda, Hart, & Nilsson, 1976; Szolovits, 1978; Szolovits & THEN: 3. There is evidence (0.8) that the investment Pauker, 1978). Reasoned Assumptions (from Davis, 1979) and would use the rule to draw conclusions whose "certainty factors" depend on the observed certainty Although our approach usually approximates that of Bayesian probabilities, accommodates representational systems based on "frames" namely as subjective degrees of belief.