The two-volume set entitled "Knowledge-Based Systems (Volume 1, Knowledge Acquisition for Knowledge-Based Systems, 355 pp., and Volume 2, "Knowledge Acquisition Tools for Expert Systems, 343 pp., Academic Press, San Diego, California, 1988), edited by B. R. Gaines and J. H. Boose, is an excellent collection of papers useful to both commercial practitioners of knowledge-based-systems development and research-oriented scientists at specialized centers or academic institutions.

The designer's input can be manually added to Design Automation: Automated Full-which itself is awkward) in the The author is criticizing the capability Custom VLSI Layout Using the Ulysses script environment, which considerably of the Weaver system (a knowledge-based Design Environment (Academic Press, reduces the power and authority circuit interconnections Boston, Massachusetts, 1988, 463 of the demonstration. This disappointing router) to restart, continue (that is, to pages) by Michael L. Bushnell deals demonstration might be the be interrupted), or accept that a user with an interesting and challenging result of the project's ambitious nature might specify some routing channels. A The book is misleading in its treatment achieve. The problem here is not the system called Ulysses that implements of some key points. Any routing expert blackboard architecture is described.

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.

Jim Saveland For a fire in that fuel complex to Research Forester The Phoenix project ("Trial by Fire: grow to the size indicated in the time Associate Editor, AI Application in Understanding the Design Requirements indicated would require a midflame Natural Resource Management for Agents in Complex Environments." Agriculture 3) presents very interesting work in The authors go on to state, "Firefighting Forest Service forest fire simulation. I am especially objects are also accurately Southern Forest Fire Laboratory glad to see recognition that the "realtime, simulated; for example, bulldozers Route 1, Box 182A spatially distributed, multiagent, move at a maximum speed of... 0.5 Dry Branch, GA 31020 dynamic, and unpredictable fire kph when cutting a fireline." In reality, environment" provides an excellent sustained fireline production for Editor: opportunity to explore a variety of AI bulldozers is variable (0.1 - 2.0 kph) issues, such as how complex environments depending on steepness of the slope, Mr. Saveland's letter focuses our constrain the design of intelligent vegetation, and size of the bulldozer. I hope more AI researchers Furthermore, although bulldozers are between accuracy and realism.

Universal plans address the tension between reasoned behavior and timely response by caching reactions for classes of possible situations. This technique reduces the average time required to select a response at the expense of the space required to store the cache-the classic time-space trade-off. In his article, Matthew Ginsberg argues from the time extreme and against the space extreme. Although I find both extremes undesirable, I defend an increase in space consumption.

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.

Letters to the Editor.

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.