The eight sections are (1) Artificial Intelligence (introductory material); (2) Problem-Solving (search and game playing); (3) Knowledge and Reasoning (propositional and predicate logic, inference techniques, knowledge representation); (4) Acting Logically (planning); (5) Uncertain Knowledge and Reasoning (probabilistic reasoning, Bayesian nets, decision-theoretic techniques); (6) Learning (inductive learning, neural nets, reinforcement learning); (7) Communicating, Perceiving, and Acting (natural language processing, computer vision, robotics); and (8) Conclusions (philosophical foundations and summary). What makes this textbook so good? First, it is remarkably comprehensive. In the preface, the authors suggest several alternative paths through the book that could serve as the basis of a one-semester course. At the University of Pittsburgh, my colleagues and I cover roughly the first half of the book (Sections 1-4) in the firstsemester introductory graduate AI course, covering most of Sections 5 through 8 in a second-semester course.

We propose an approach for meeting realtime constraints in AI systems that views (1) time as a resource that should be considered when making control decisions, (2) plans as ways of expressing control decisions, and (3) approximate processing as a way of satisfying time constraints that cannot be achieved through normal processing. In this approach, a real-time problem solver estimates the time required to generate solutions and their quality. This estimate permits the system to anticipate whether the current objectives will be met in time. The system can then take corrective actions and form lower-quality solutions within the time constraints. These actions can involve modifying existing plans or forming radically different plans that utilize only rough data characteristics and approximate knowledge to achieve a desired speedup.

The Workshop on Applications of Ontologies and Problem-Solving Methods (PSMs), held in conjunction with the Thirteenth Biennial European Conference on Artificial Intelligence (ECAI '98), was held on 24 to 25 August 1998. Twenty-six people participated, and 16 papers were presented. Participants included scientists and practitioners from both the ontology and PSM communities. The first day was devoted to paper presentations and discussions. The second (half) day, a joint session was held with two other workshops: (1) Building, Maintaining, and Using Organizational Memories and (2) Intelligent Information Integration.

Many techniques for representing knowledge have been proposed, but there have been few reports that compare their application This article presents an experimental comparison of four knowledge representation schemes: a simple production system, a structured production system, a frame system, and a logic system. The authors would like to express their appreciation to Dr Edward A. Feigenbaum and H Penny Nii of Stanford University for discussing the early results of this research during their visit to the authors' laboratory The authors also would like to acknowledge the support of Dr Jun Kawasaki, the general manager of Systems Development Laboratory, Hitachi, Ltd This information helps an expert system designer clarify the domain's characteristics and develop a conceptual system design. However, little information is provided for selecting adequate techniques after the system's function (input/output) is determined. The system was designed to interpret the X-ray powder diffraction spectra of rocks to determine their constituent minerals. This article focuses on expert system building tools; however, there may be many cases where no such tools are available.

Northrop Research and Technology Center, One Research Park, Pales Wdes Peninsula, CA 90274 It, is interesting t,o note that there is no agreed upon definition of artificial intrlligence. Because government agencies ask for it, software shops claim to provide it, popular magazines and newspapers publish articles about, it, dreamers base their fant,asies on it, and pragmatists criticize and denounce it. Such a stat,c of affairs has persisted since Newell, Simon, and Shaw wrote thcif first. Not knowing exactly what we ale talking about, or expecting is typical of a new field; for example, witness the chaos that centcrcd around program verification of security rclated aspects of systems a few years ago The details are too glim to recount, in mixed company. However, artificial intelligence has been around for nearly 30 years, so one might wonder why our wheels are st,ill spinning.

Introduction Factory design is the specification of functional requirements for a new factory or the specification of functional changes to an existing factory. Factory design is essentially initiated upon formalization of a product or set of products that must be manufactured. Once designed, the factory is subjected to a continuous cycle of redesign that is only complete when the factory has served its useful life, which can include the manufacture of products not conceived during the original design. The design of a factory and the implications of this design on the manufacture of goods typically involves millions of dollars in expenditures. Recent estimates are that 8 percent of the U.S. gross national product (GNP) can be attributed to new factory design and construction (Tompkins and White 1984).

Satellite domains are becoming a fashionable area of research within the AI community due to the complexity of the problems that satellite domains need to solve. With the current U.S. and European focus on launching satellites for communication, broadcasting, or localization tasks, among others, the automatic control of these machines becomes an important problem. Many new techniques in both the planning and scheduling fields have been applied successfully, but still much work is left to be done for reliable autonomous architectures. The purpose of this article is to present CONSAT, a real application that plans and schedules the performance of nominal operations in four satellites during the course of a year for a commercial Spanish satellite company, HISPASAT. For this task, we have used an AI domain-independent planner that solves the planning and scheduling problems in the HISPASAT domain thanks to its capability of representing and handling continuous variables, coding functions to obtain the operators' variable values, and the use of control rules to prune the search.

BBN's project in knowledge representation for natural language understanding is developing techniques for computer assistance to a decision maker who is collecting information about and making choices in a complex situation. In particular, we are designing a system for natural language control of an intelligent graphics display. This system is intended for use in situation assessment and information management. Our work is concentrated in the development of fundamental techniques for knowledge representation and language understanding. Specifically, we are working on advanced parsing techniques, syntactic/semantic interaction, recognition of speaker intent, anaphora and deixis, fundamental knowledge representation techniques, and parallel algorithms and techniques for knowledge-based inference.

A longstanding problem in the field of automated reasoning is designing systems that can describe a set of actions (or a plan) that can be expected to allow the system to reach a desired goal. Ideally, this set of actions is then passed to a robot, a manufacturing system, or some other form of effector, which can follow the plan and produce the desired result. The design of such planners has been with AI since its earliest days, and a large number of techniques have been introduced in progressively more ambitious systems over a long period. In addition, planning research has introduced many problems to the field of AI. Some examples are the representation and the reasoning about time, causality, and intentions; physical or other constraints on suitable solutions; uncertainty in the execution of plans; sensation and perception of the real world and the holding of beliefs about it; and multiple agents who might cooperate or interfere.

Real-time problem solving is not only reasoning about time, it is also reasoning in time. This ability is becoming increasingly critical in systems that monitor and control complex processes in semiautonomous, ill-structured, real-world environments. Many techniques, mostly ad hoc, have been developed in both the real-time community and the AI community for solving problems within time constraints. However, a coherent, holistic picture does not exist. This article is an attempt to step back from the details and examine the entire issue of real-time problem solving from first principles. We examine the degrees of freedom available in structuring the problem space and the search process to reduce problemsolving variations and produce satisficing solutions within the time available. This structured approach aids in understanding and sorting out the relevance and utility of different real-time problem-solving techniques. Such applications are subject to the real-time constraints of the ...