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YANG XIANG. University of Guelph. Department of Computing and Information Science. Guelph, ON N1G 2W1, Canada e-mail: [email protected].
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IEEE TRANSACTIONS ON SYSTEMS, MAN AND CYBERNETICS—PART B: CYBERNETICS, VOL. 32, NO. 1, FEBRUARY 2002

Editorial Advances on Uncertain Reasoning in Intelligent Systems

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T IS widely accepted that a core competence of an intelligent system situated in an application environment is the ability to represent and update uncertain knowledge on the environment, to reason about the state of the environment, and to take actions that affect the environment in directions that best reflect the interest of the user. The research on uncertain knowledge representation and inference formalisms, on methods for acquisition and learning of uncertain knowledge from human experts as well data, on reasoning algorithms, and on deliberation and planning has been a strong driving force in the field of artificial intelligence. The Uncertain Reasoning (UR) Track at The International Florida Artificial Intelligence Research Society Conference (FLAIRS) was started in 1996 and has been run successfully for six consecutive years as a high quality international research forum. The papers collected in this special issue are selected from the UR Track at FLAIRS’2001. They reflect a cross-section of current research in the uncertain reasoning area. Each paper was subjected to two rounds of reviews, with two to three reviewers for each round. Papers are revised after each round of reviews in response to critical comments provided by reviewers. Two papers in the special issue deal with fundamental issues in uncertain knowledge representation. R. T. Cox’s justification of subjective probability has long been cited as the motivation to use probabilistic models for uncertain belief. However, in recent years, the soundness of Cox’s justification has been questioned. In his earlier work, Paul Snow defended the soundness of Cox’s proof. In this special issue, Paul Snow answers new objections against the reasonableness of Cox’s result. The second paper, by Ronald Yager, discusses the use of a class of nonadditive measures of uncertainty, called fuzzy measures, as a unifying frame-

Publisher Item Identifier S 1083-4419(02)00966-4.

work for a number of well established types of uncertainty representations. In particular, he discusses how Dempster–Shafer belief structure corresponds to a set of possible fuzzy measures; how entropy relates to fuzzy measures; and, how fuzzy measures can be used to support uncertain decision making. Two other papers in this special issue are concerned with uncertain inference and decision making. Kristian Olesen and Anders Madsen present the application of maximal prime subgraphs to a number of tasks in probabilistic inference with Bayesian networks. These tasks include triangulation, lazy belief propagation, hybrid belief propagation by combining exact and approximate methods, and construction of junction trees. Next, Anders Madsen, Kristian Olesen, and Soren Dittmer exploit deterministic (versus uncertain) dependence relations to reduce the computational complexity of solving influence diagrams. They present their experience and lessons from a decision support system for mission management of unmanned underwater vehicles. Finally, the last paper in the special issue studies elicitation of expert knowledge as a probabilistic model. Haiqin Wang, Denver Dash, and Marek Druzdzel present a method to evaluate different probability elicitation schemes objectively.

EUGENE SANTOS, JR. University of Connecticut Department of Computer Science and Engineering Storrs, CT 06268 USA e-mail: [email protected] YANG XIANG University of Guelph Department of Computing and Information Science Guelph, ON N1G 2W1, Canada e-mail: [email protected]

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IEEE TRANSACTIONS ON SYSTEMS, MAN, AND CYBERNETICS—PART B: CYBERNETICS, VOL. 32, NO. 1, FEBRUARY 2002

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Eugene Santos, Jr. (M’93) received the B.S. degree in mathematics and computer science in 1985 and the M.S. degree in mathematics in 1986, both from Youngstown State University, Youngstown, OH, and the Sc.M. and Ph.D.degrees in computer science from Brown University, Providence, RI, in 1988 and 1992, respectively. He is currently an Associate Professor of computer science and engineering at the University of Connecticut, Storrs. He has over 80 refereed technical publications in journals and conferences. His areas of research interest include multi-agent systems, intelligent information systems, decision support, human–computer interaction, intelligent user interfaces, uncertainty, probabilistic reasoning, knowledge engineering, verification and validation of knowledge-based systems, virtual reality for phobia treatments, neural networks, optimization, and natural language processing. Dr. Santos has chaired and served on numerous major conference program committees from intelligent agents to reasoning under uncertainty to evolutionary computing. He is currently an associate editor for the IEEE TRANSACTIONS ON SYSTEMS, MAN, AND CYBERNETICS and on the Executive Board for the Connecticut Chapter of ACM SIGCHI. He is a member of ACM, the IEEE Computer Society, IEEE ComSoc, IEEE Information Theory Society, AAAI, SIAM, and Sigma Xi.

Yang Xiang received the Ph.D. degree from the University of British Columbia, Vancouver, BC, Canada, in 1992. He is an Associate Professor in the Department of Computing and Information Science, University of Guelph, Guelph, ON, Canada, where he also directs the Intelligent Decision Support System Lab. He is a Principle Investigator of the Canadian Institute of Robotics and Intelligent Systems (IRIS). His main research interest concerns probabilistic reasoning with belief networks, knowledge discovery from data, and distributed inference in multi-agent systems. He has published over 50 articles in the area, and has developed WEBWEAVR-III, a comprehensive Java toolkit for normative decision support which has been distributed to users in more than 20 countries.