J. de Kleer

Bio: J. de Kleer is an academic researcher from PARC. The author has contributed to research in topics: Reason maintenance & Mutual information. The author has an hindex of 3, co-authored 3 publications receiving 4040 citations.

A unified information criterion for evaluating probe and test selection

Abstract: Diagnostic tasks often need to make the decision of what measurement to make or what action to take in order to resolve ambiguities in diagnosis. Intuitively one would like to seek the most ldquoinformativerdquo choice. In the paper, we formalize this intuition and propose an information criterion for evaluating and comparing measurement/action choices based on their information contribution. The criterion is mutual information, an information-theoretic concept measuring statistical dependence. The information criterion gives a precise quantitative metric to differentiate the quality of measurement/action choices. We use a few concrete example in two separate paradigms, probe selection in circuit diagnosis and test generation in production plants, to illustrate the mutual information criterion. Despite the apparent differences of the two paradigms, the information criterion works coherently. We demonstrate how different probing actions or test plans vary in their information values.

Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference

Abstract: From the Publisher: Probabilistic Reasoning in Intelligent Systems is a complete andaccessible account of the theoretical foundations and computational methods that underlie plausible reasoning under uncertainty. The author provides a coherent explication of probability as a language for reasoning with partial belief and offers a unifying perspective on other AI approaches to uncertainty, such as the Dempster-Shafer formalism, truth maintenance systems, and nonmonotonic logic. The author distinguishes syntactic and semantic approaches to uncertainty—and offers techniques, based on belief networks, that provide a mechanism for making semantics-based systems operational. Specifically, network-propagation techniques serve as a mechanism for combining the theoretical coherence of probability theory with modern demands of reasoning-systems technology: modular declarative inputs, conceptually meaningful inferences, and parallel distributed computation. Application areas include diagnosis, forecasting, image interpretation, multi-sensor fusion, decision support systems, plan recognition, planning, speech recognition—in short, almost every task requiring that conclusions be drawn from uncertain clues and incomplete information. Probabilistic Reasoning in Intelligent Systems will be of special interest to scholars and researchers in AI, decision theory, statistics, logic, philosophy, cognitive psychology, and the management sciences. Professionals in the areas of knowledge-based systems, operations research, engineering, and statistics will find theoretical and computational tools of immediate practical use. The book can also be used as an excellent text for graduate-level courses in AI, operations research, or applied probability.

Chaff: engineering an efficient SAT solver

Abstract: Boolean satisfiability is probably the most studied of the combinatorial optimization/search problems. Significant effort has been devoted to trying to provide practical solutions to this problem for problem instances encountered in a range of applications in electronic design automation (EDA), as well as in artificial intelligence (AI). This study has culminated in the development of several SAT packages, both proprietary and in the public domain (e.g. GRASP, SATO) which find significant use in both research and industry. Most existing complete solvers are variants of the Davis-Putnam (DP) search algorithm. In this paper we describe the development of a new complete solver, Chaff which achieves significant performance gains through careful engineering of all aspects of the search-especially a particularly efficient implementation of Boolean constraint propagation (BCP) and a novel low overhead decision strategy. Chaff has been able to obtain one to two orders of magnitude performance improvement on difficult SAT benchmarks in comparison with other solvers (DP or otherwise), including GRASP and SATO.