AI Magazine Volume 9 Number 3 (1988) ( AAAI) The first part of the book is intended to be an introduction to computational jurisprudence for both groups. It identifies issues critical to the purpose, behavior, knowledge sources, knowledge structures, and reasoning processes of expert legal systems. The second part implements a simple prototype system for a well-defined area of contract law and is more appropriate for experienced developers of knowledge-based systems. Law is a domain in which the experts are supposed to disagree, and lawyers must be able to argue either side of a case. A judge or juror must decide which argument is "best."

The importance of contextual reasoning is emphasized by various researchers in AI. (A partial list includes John McCarthy and his group, R. V. Guha, Yoav Shoham, Giuseppe Attardi and Maria Simi, and Fausto Giunchiglia and his group.) Here, we survey the problem of formalizing context and explore what is needed for an acceptable account of this abstract notion. Although the word context is frequently used in descriptions, explanations, and analyses of computer programs in these areas, its meaning is frequently left to the reader's understanding; that is, it is used in an implicit and intuitive manner. An example of how contexts may help in AI is found in McCarthy's (constructive) criticism (McCarthy 1984) of I wish honorable gentlemen would have the fairness to give the entire context of what I did say, and not pick out detached words (R. Cobden [1849], quoted in Oxford English Dictionary [1978], p. 902). The main motivation for studying formal contexts is to resolve the problem of generality in AI.

The origins of the BDI model lie in the theory of human practical reasoning developed by the philosopher Michael Bratman (1987) in the mid-1980s. AI Magazine Volume 22 Number 4 (2001) ( AAAI) Among the three strands of BDI research, Reasoning about Rational Agents focuses almost exclusively on the logical foundations of the theory. "If you've heard about A-life but aren't quite sure what it is or where it's going, Grand's book is an excellent place to enter one of the more exciting areas of twenty-firstcentury science." Steve Grand showed us how to build a universe of evolving creatures, without the prevailing academic biases.This delightful book is a fresh and inspiring account of how to succeed in creating artificial life." Nevertheless, it continues to be an important, thriving research area that helps to crystallize some of the deepest questions in the field.

Genetic Epistemology Editor: In his recent article in AI Magazine, "AI prepares for 2001," Nils Nilsson put forward a paradigm of AI based on a declarative representation of knowledge with semantic attachments to problem-specific procedures and data structures. The author discussed various research strategies for AI and specifically a computer-individual project was introduced as an efficient way of stimulating research and advances in the basic science of AI The undertaking of such a project immediately raises some classical psychological questions. Besides the deductive versus inductive or declarative versus procedural controversials, problems related to knowledge representation and evolution in an interactive environment must be considered. I would like to present some ideas and concepts stemming from current research in Genetic Epistemology (GE), initiated by Jean Piaget, as possible contributions to AI research fields. Knowledge is a common preoccupation for GE and AI.

My collaborators and I have recently reported in domain science journals several human-comput- er discoveries in biology, chemistry, and physics. One might ask what accounts for these findings, for example, whether they share a common pattern. My conclusion is that each finding involves a new representation of the scientific task: The problem spaces searched were unlike previous task problem spaces. Such new representations need not be wholly new to the history of science; rather, they can draw on useful representational pieces from elsewhere in natural or computer science. This account contrasts with earlier explanations of machine discovery based on the expert system view.

Leibniz's (1984) An Introduction to a Secret Encyclopedia includes the following marginal note: Principle of Physical Certainty: Everything which men have experienced always and in many ways will still happen: for example that iron sinks in water (Leibniz 1984). In our daily lives, we routinely use this principle. Thus, we know that we can pull with a string but not push with it; that a flower pot dropped from our balcony falls to the ground and breaks; that when we place a container of water on fire, water might boil after a while and overflow the container. The origin of such knowledge is a matter of constant debate. It is clear that we learn a great deal about the physical world as we grow up.

One of the major problems in developing so-called intelligent computer-aided design (CAD) systems (ten Hagen and Tomiyama 1987) is the representation of design knowledge, which is a two-part process: the representation of design objects and the representation of design processes. We believe that intelligent CAD systems will be fully realized only when these two types of representation are integrated. Progress has been made in the representation of design objects, as can be seen, for example, in geometric modeling; however, almost no significant results have been seen in the representation of design processes, which implies that we need a design theory to formalize them. According to Finger and Dixon (1989), design process models can be categorized into a descriptive model that explains how design is done, a cognitive model that explains the designer's behavior, a prescriptive model that shows how design must be done, and a computable model that expresses a method by which a computer can accomplish a task. A design theory for intelligent CAD is not useful when it is merely descriptive or cognitive; it must also be computable.

AUTOMATIC PROBLEM SOLVING' For intelligent computers to be able to interact with the real world, they must be able to aggregate individual actions into sequences to achieve desired goals. During the last decade, a number of techniques have been developed for improving the efficiency of these strategies. The bulk of this paper consists of a description of the problem-solving strategies and a catalogue of tactics for improving their efficiency.This is followed by an attempt to'This is a slight revision of a paper presented at the Sixth International Joint Conference on Artificial Intelligence, Tokyo, Japan, August 20-24, 1979 The original vetsion was prepared while the author was with SRI International, Menlo park, California, suppotted by the Defense Advanced Research Projects Agency under constract N00039-79-C-0118 with the Naval Electronic Systems Command The general function of an automatic problem solving system, then, is to construct a sequence of actions that transforms one world model into another. There are three basic capabilities that a problem solving system must have. These are: 1. Management of State Description Models The information will not all be explicitly encoded, however, so a deductive engine of some sort must be provided to allow needed information to be extracted from a model.

Among other issues, Michalski stressed the importance of unification of terminology and extraction of general principles. Amarel suggested we need both theory and application, even within a single project (one supports the other).Another indicator: Chapter XIV Handbook of Artificial Intelligence (Dietterich, London, Clarkson & Dromey, 1982) compares and contrasts generalization methods; Michalski The author would like to thank Dave Coles for his comments on a draft of this article. AI is being related to cognitive science (Pylyshyn, 1982) and with control theory and pattern recognition (Buchanan, Mitchell & Smith, 1978).) This article suggests new ideas and attempts to relate them to some existing ones in AI. While the focus is heuristic learning in search, it is examined with broader intent.

Introduction: Typical Uses of Heuristics Heuristics are methods and criteria for judging the relative merits of alternative courses of planning or action. There is hardly any intellectual activity which does not rely on heuristics of some kind. The decision to begin reading this paper, for example, reflects a tacit use of heuristics which has lured the reader to invest time and effort in anticipation of certain benefits. Ahhough such anticipations may occasionally be disappointed, on the whole they are essential to planning our everyday activities. Complex combinatorial problems require the use of heuristics if a reasonably "good" solution is to be produced We shall demonstrate this point using three simple problems (readers familiar with the properties of A' may skip to section Where do these heuristics come from?):