Alternative suggestions The call for papers for AAAI-90 requires 68 Willow Road, Menlo Park, CA were discussed. Policy changes each author to specify his or her topic 94025.

Since this article is a counting argument, the conclusion time, a number of alternatives have been proposed. Presumably, in realistic cases, the Universally Bad Idea," analyzes one such number of sensors is large enough that a universal alternative, Marcel Schoppers's universal plan could not fit in your head. He also extends this analysis to a There are two reasons not to be concerned number of other systems, including Pengi about this apparent problem. They involve (A gre and Chapman 1987), which was structure and state, designed by Phil Agre and myself. Ginsberg's criticisms of universal plans rest Using universal plans, he says, is infeasible because their size is exponential in the number of possible domain states. Representing such a plan is infeasible in even quite small realistic domains. I'm sympathetic to such arguments, having made similar ones to the effect that classical planning is infeasible (Agre and Chapman 1988; Chapman 1987b). I don't understand the details of Schoppers's ideas, so I'm not sure whether this critique of universal plans per se is correct. However, I show that these arguments do not extend to Pengi. Ginsberg calls Pengi an approximate universal plan, by which he means it is like a universal plan except that it does not correctly specify what to do in every situation. However, Pengi's operation involves no plans, universal or approximate, and Pengi and universal plans, although they share some motivations, have little to do with each other as technical proposals. Ginsberg suggests number of its inputs. Pengi-like system, computation in the number of pixels or that, Blockhead, which efficiently solves the fruitcake on the average, business data processing takes problem; the way it solves it elucidates exponential work in the number of records. They have a lot The fruitcake problem is to stack a set of of structure to them, and this structure can be labeled blocks so that they spell the word exploited to exponentially reduce the computation's fruitcake. What is apparently difficult about size. I show impossible under the rules of the domain, Blockhead solving a problem involving 45 and the remainder can be categorized relatively blocks in which there are 45! 1056 configurations, cheaply to permit abstraction and There is every in every configuration, so it is not by reason to think that this same structure is approximation that it succeeds. Indeed, Ginsberg makes this and a central system. The [planning couldn't work if] there were no visual system is a small subset of Pengi's rhyme or reason to things."

Letters to the Editor.

Review of Logic Foundations of Artificial Intelligence

Response to Drew McDermott's Review of Logical Foundations of Artificial Intelligence

Phoenix is a real-time, adaptive planner that manages forest fires in a simulated environment. Alternatively, Phoenix is a search for functional relationships between the designs of agents, their behaviors, and the environments in which they work. In fact, both characterizations are appropriate and together exemplify a research methodology that emphasizes complex, dynamic environments and complete, autonomous agents. This article describes the underlying methodology and illustrates the architecture and behavior of Phoenix agents.

To read the book "Automated Reasoning: Thirty-Three Basic Research problems (Prentice Hall, Englewood Cliffs, N.J., 1987, 300 pp., $11.00) by Larry Was it is not necessary to be an expert in mathematics or logic or computer science. However, even if you are such an expert, you will read it with interest, and likely, with enjoyment.

Advances in computer hardware and software and engineering methodologies in the 1960s and 1970s led to an increased use of computers by engineers. AI techniques, in particular the knowledge-based system (KBS) technology, offer a methodology to solve these ill-structured design problems. In this article, we describe several research projects that utilize KBS techniques for design automation. These projects are (1) the Criteria Yielding, Consistent Labeling with Optimization and Precedents-Based System (CYCLOPS), which generates innovative designs by using a three-stage process: normal search, exploration, and adaptation; (2) the Concept Generator (CONGEN), which is a domain independent framework for conceptual or preliminary design; (3) Constraint Manager (CONMAN), which is a constraint-management system that performs the evaluation and consistency maintenance of constraints arising in design; (4) the distributed and integrated environment for computer-aided engineering (DICE), which facilitates coordination, communication, and control during the entire design and construction/manu-facturing phases; and (5) DESIGN-KIT, which can be envisioned as a new generation of computer-aided engineering environment for process-engineering applications.

Nevertheless, significant aspects of behavior and user expectation are peculiar to expert systems and their users. These considerations are discussed here with examples from an actual system. Guidelines for the behavior of expert systems and the responsibility of designers to their users are proposed.