The third edition of this best-selling guide to Prolog and Artificial Intelligence has been updated to include key developments in the field while retaining its lucid approach to these topics. Divided into two parts, the first part of the book introduces the programming language Prolog, while the second part teaches Artificial Intelligence using Prolog as a tool for the implementation of AI techniques. Prolog has its roots in logic, however the main aim of this book is to teach Prolog as a practical programming tool. This text therefore concentrates on the art of using the basic mechanisms of Prolog to solve interesting problems. The third edition has been fully revised and extended to provide an even greater range of applications, which further enhance its value as a self-contained guide to Prolog, AI or AI Programming for students and professional programmers alike.

Co-winner of The 2005 AAAI Classic Paper Awards. Summary of Significance by Hector Levesque. Proc. AAAI-86

IEEE Expert, 1[#3]:23-29

"This paper describes a general method for estimating the nominal relationship and expected error (covariance) between coordinate frames representing the relative locations of ob jects. The frames may be known only indirectly through a series of spatial relationships, each with its associated error, arising from diverse causes, including positioning errors, measurement errors, or tolerances in part dimensions. This estimation method can be used to answer such questions as whether a camera attached to a robot is likely to have a particular reference object in its field of view. The calculated estimates agree well with those from an independent Monte Carlo simulation. The method makes it possible to decide in advance whether an uncertain relationship is known accu rately enough for some task and, if not, how much of an improvement in locational knowledge a proposed sensor will provide. The method presented can be generalized to six degrees offreedom and provides a practical means of esti mating the relationships ( position and orientation) among objects, as well as estimating the uncertainty associated with the relationships." Int. J. Robotics Research, 5 (4), 56-68.

"Chunking was first proposed as a model of human memory by Miller (1956), and has since become a major component of theories of cognition. More recently it has been proposed that a theory of human learning based on chunking ..." Kluwer Academic Publishers, Norwell, MA, USA.

We present a logical relationship between a small number of intuitive properties for measures of belief and the axioms of probability theory. The relationship was first demonstrated several decades ago but has remained obscure. We introduce the proof and discuss its relevance to research on reasoning under uncertainty in artificial intelligence. In particular, we demonstrate that the logical relationship can facilitate the identification of differences among alternative plausible reasoning methodologies. Finally, we make use of the relationship to examine popular non-probabilistic strategies.

Raymond Reiter' Department of Computer Science University of Toronto Toronto, Ontario, Canada M5S-1A4 Johan de Kleer Intelligent Systems Laboratory XEROX Palo Alto Research Center 3333 Coyote Hill Road Palo Alto, California 94304 ABSTRACT In this paper we (1) define the concept of a Clause Managetnent System (CMS) A Problem-Solving Architecture Figure 1 illustrates an architecture for a problem solving system consisting of a domain dependent Reasoner coupled to a domain independent Clause Management System (CMS). For our present purposes, the Reasoner is a black box which, m the process of doing whatever it does, occasionally transmits a propositional clause 2 to the CMS.

"Arithmetic reasoning" can range in complexity from simple integer arithmetic to powerful symbolic algebraic reasoning of the sort done by MACSYMA. We describe an arithmetic reasoning system of intermediate complexity called the Quantity Lattice. In a computationally efficient manner the Quantity Lattice integrates qualitative and quantitative reasoning, and combines inequality reasoning with reasoning about simple arithmetic expressions, such as addition or multiplication. The system has proven useful in doing simulation and analysis in several domains, including geology and semiconductor fabrication, by supporting useful forms of reasoning about time and the changes that hap pen when processes occur.

While program verification was always a. desirable, but never an easy task, the advent of concurrent

Formal proof-checking has long been recognized as an interesting application of computers. It has often been claimed that significant proofs in mathematics cannot be checked using an automatic proof-checker, and that formal proofs lack the intuitive plausibility and insight that informal proofs possess. We argue against these claims by presenting machine-checked versions of some landmark proofs in metamathematics, such as those of the tautology theorem, Godel's incompleteness theorem, and the Church-Rosser theorem. These proofs were checked using the Boyer-Moore theorem power. The tautology theorem and the incompleteness theorem are proved by first defining a proof-checker for the formal theory Z2 in the Boyer-Moore logic.