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.

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In this proposal, I describe a. method for formulating plans from

A qualitative physics which captures the depth and breadth of an engineer's knowledge will be orders of magnitude larger than the models of today's qualitative physics. To build and use such models effectively requires explicit modeIing assumptions to manage complexity. This, in turn, gives rise to the problem of selecting the right qualitative model for some purpose.

The Robotics Institute, Carnegie Mellon University Pittsburgh, PA 15213 Abstract In non-cooperative multi-agent planning, resolution of multiple conflicting goals is the result of finding compromise solutions. Previous research has dealt with such multi-agent problems where planning goals are well-specified, subgoals can be enumerated, and the utilities associated with subgoals known. Our research extends the domain of problems to include non-cooperative multi-agent interactions where planning goals are ill-specified, subgoals cannot be enumerated, and the associated utilities are not precisely known. Negotiation is performed through proposal and modification of goal relaxations. Case-Based Reasoning is integrated with the use of multi-attribute utilities to portray tradeoffs and propose novel goal relaxations and compromises. Persuasive arguments are generated and used as a mechanism to dynamically change the agents' utilities so that convergence to an acceptable compromise can be achieved.

In Proceedings of the Fifth International Conference on Machine Learning, pages 54-64, Ann Arbor, Michigan.