This book is a collection of research papers covering a range of topics in expert systems and knowledge engineering.

Johnson-Laird In a field choked with seemingly impenetrable jargon, Quick and thorough. Philip Johnson-Laird has done the impossible: written a By mixing forward and backward chaining, goal search book about how the mind works that requires no advance time can be shortenedramatically And, using GURU's knowledge of artificial intelligence, neurophysiology, or multiple rule firing capabilityou can refire rules psychology, providing the single best introduction to cognitive as values change GURU also comes equipped with science available. "Philip Johnson-Laird has that rare gift of being a cognitive seamlessly integrated 4th generation decision support scientist of the first order, yet he addresses himself to capabilitiesuch as data base, spreadsheet, and the deep classical issues in psychology, in the philosophy report generator

An informal workshop on concurrent logic programming, metaprogramming, and open systems was held at Xerox Palo Alto Research Center (PARC) on 8-9 September 1987 with support from the Association for the Advancement of Artificial Intelligence. The 50 workshop participants came from the Japanese Fifth Generation Project (ICOT), the Weizmann Institute of Sci-ence in Israel, Imperial College in London, the Swedish Institute of Computer Science, Stanford University, the Mas-sachusetts Institute of Technology (MIT), Carnegie Mellon University (CMU), Cal Tech, Science University of Tokyo, Melbourne University, Calgary University, University of Wisconsin, Case Western Reserve, University of Oregon, Korea Advanced Institute of Science and Technology (KAIST), Quintus, Symbolics, IBM, and Xerox PARC. No proceedings were generated; instead, participants distributed copies of drafts, slides, and recent papers.

VT (vertical transportation) is an expert system for handling the design of elevator systems that is currently in use at Westinghouse Elevator Company. Although VT tries to postpone each decision in creating a design until all information that constrains the decision is known, for many decisions this postponement is not possible. In these cases, VT uses the strategy of constructing a plausible approximation and successively refining it. VT uses domain-specific knowledge to guide its backtracking search for successful refinements. The VT architecture provides the basis for a knowledge representation that is used by SALT, an automated knowledge-acquisition tool. SALT was used to build VT and provides an analysis of VT's knowledge base to assess its potential for convergence on a solution.

The Computing Research Laboratory (CRL) at New Mexico State University is a center for research in artificial intelligence and cognitive science. Specific areas of research include the human-computer interface, natural language understanding, connectionism, knowledge representation and reasoning, computer vision, robotics, and graph theory. This article describes the ongoing projects at CRL.

This is Part 1 of a two-part article. Part 2 covers tort liability and computers as expert witnesses. It will appear in the Summer 1988 issue of AI Magazine. Technological developments that remove ever-increasing numbers of cognitive tasks from human control will alter the assumptions on which current legal rules are based. These rules will have a growing impact on AI researchers and entrepreneurs as their work reaches a growing audience of beneficiaries. In order to accommodate the needs of practitioners and their recipients, courts and lawmakers will be forced to reevaluate principles whose foundations were developed well before the implications of advanced technology could have been predicted. This article attempts to identify areas of law in which the need for accommodation will be greatest and provide some insight into the process and the direction of change.

We propose an approach for meeting real-time 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. A decision about how to change processing should be situation dependent, based on the current state of processing and the domain-dependent solution criteria. We present preliminary experiments that show how approximate processing helps a vehicle-monitoring problem solver meet deadlines and outline a framework for flexibly meeting real-time constraints.

The fundamental observation we will hands ask, have all the big ideas gone? This is, put field is a real change with several causes, differently, a traditional thesis of artificial and not simply an illusion. Two factors intelligence, namely that the immediately spring to mind: hardware may vary but the basic problems of intelligent action remain the - To some extent, it reflects the maturation same. For example, one big problem is of the field. This notion permeates all of problems are solved, the remaining of artificial intelligence's relatives but problems are harder, making progress less so artificial intelligence itself.

Citing the confusing statements in the AI literature concerning the relationship between branch and bound (B&B) and heuristic search procedures were present a simple and general formulation of B&B which should help dispel much of the confusion. We illustrate the utility of the formulation by showing that through it some apparently very different algorithms for searching And/Or trees reveal the specific nature of their similarities and differences. In addition to giving new insights into the relationships among some AI search algorithms, the general formulation also provides suggestions on how existing search procedures may be varied to obtain new algorithms.

Technical report, Department of Computer Science, Tufts University.