The vendors Based Systems, 355 pp., and Volume 2, techniques. They are interesting of knowledge-based-systems development Knowledge Acquisition Tools for Expert and informative, particularly tools, for example, Inference, Systems, 343 pp., Academic Press, San "Generalization and Noise" by Y. IntelliCorp, Aion, AI Corp., and IBM, Diego, California, 1988), edited by B. Kodratoff and M. Manango, which would do well to pay heed to these R. Gaines and J. H. Boose, is an excellent discusses symbolic and numeric rule books because they point the way to collection of papers useful to both induction.

Several authors have recently suggested that a possible approach to planning in uncertain domains is to analyze all possible situations beforehand and then store information about what to do in each. The result is that a system can simply use its sensors to examine its domain and then decide what to do by finding its current situation in some sort of a table. The purpose of this article is to argue that even if the compile-time costs of the analysis are ignored, the size of the table must, in general, grow exponentially with the complexity of the domain. This growth makes it unlikely that this approach to planning will be able to deal with problems of an interesting size; one really needs the ability to do some amount of inference at run time. In other words, an effective approach to acting in uncertain domains cannot be to look and then leap; it must always be to look, to think, and only then to leap.

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.