Summary Eurisko is an AI program that learns by discovery We are applying Eurisko to the task of inventing new kinds of three-dimensional microelectronic devices that can then be fabricated using recently developed laser recrystallization techniques Three experiments have been conducted, and some novel designs and design rules have emerged. The paradigm for Eurisko's exploration is a loop in which it. Many of the well-known primitive devices were synthesized quickly, such as the MOSFET, Junction Diode, and Bipolar Transistor. This was unsurprising, as they were short sentences in the descriptive language we had defined (a language with verbs like Abut and ApplyEField, and with nouns like nDopedRegion and IntrinsicChannelRegion) Future We wish to thank those graduate students who have aided us in the construction of RLL, the language in which Eurisko is written, most notably Greg Harris at CMIJ and Russ Grciner at Stanford.

We describe an application of AI search and information visualization techniques to the problem of school redistricting, in which students are assigned to home schools within a county or school district. This is a multicriteria optimization problem in which competing objectives, such as school capacity, busing costs, and socioeconomic distribution, must be considered. Because of the complexity of the decision-making problem, tools are needed to help end users generate, evaluate, and compare alternative school assignment plans. A key goal of our research is to aid users in finding multiple qualitatively different redistricting plans that represent different tradeoffs in the decision space. We present heuristic search methods that can be used to find a set of qualitatively different plans, and give empirical results of these search methods on population data from the school district of Howard County, Maryland.

In trying to attack domain-independent planning as heuristic search, the main difficulty lies in the automatic derivation of the heuristic function. For human algorithm designers, a common approach to deriving a heuristic is to relax the problem at hand into a simpler problem ' that can be solved efficiently. Facing a search state in, one can then use the solution length of the same state in ' to estimate its difficulty. Bonet, Loerincs, and Geffner (1997) proposed a way of applying this idea to domainindependent planning. They relax the highlevel problem description by simply ignoring delete lists.

This planning graph structure is then fed to a heuristic extractor module that is capable of extracting a variety of effective and admissible heuristics, based on our recent theory (Nguyen and Kambhampati 2000). This heuristic, along with the problem specification, and the set of ground actions in the final action level of the planning graph structure are fed to a regression state search planner. To guide a regression search in the state space, a heuristic function needs to evaluate the cost of some set S of subgoals, comprising a regression state from the initial state, in terms of the number of actions required to achieve S from the initial state. This heuristic approximates the cost of a set S as the length of a "relaxed plan" for supporting S, ignoring all the mutex relations, plus the penalty for ignoring these negative interac-88 AI MAGAZINE Yochan is the planning group directed by Subbarao Kambhampati at Arizona State University.

It has become especially relevant in the last decade, with researchers increasingly investigating how to identify the most suitable existing algorithm for solving a problem instance instead of developing new algorithms. This survey presents an overview of this work focusing on the contributions made in the area of combinatorial search problems, where algorithm selection techniques have achieved significant performance improvements. We unify and organise the vast literature according to criteria that determine algorithm selection systems in practice. The comprehensive classification of approaches identifies and analyzes the different directions from which algorithm selection has been approached. This article contrasts and compares different methods for solving the problem as well as ways of using these solutions.

AI Game-Playing Techniques: Are They Useful for Anything Other Than Games? In conjunction with the American Association for Artificial Intelligence's Hall of Champions exhibit, the Innovative Applications of Artificial Intelligence held a panel discussion entitled "AI Game-Playing Techniques: Are They Useful for Anything Other Than Games?" This article summarizes the panelists' comments about whether ideas and techniques from AI game playing are useful elsewhere and what kinds of game might be suitable as "challenge problems" for future research. AAAI-98's Hall of Champions exhibit) is an AI games researcher at the University of Alberta and author of the checkers program The early research on the alpha-beta search algorithm was useful in establishing a foundation for AI theories of heuristic search, and these theories have been useful in many areas of AI. Several of the panelists (particularly Schaeffer, Wilkins, and Fotland) pointed out that the minimax search algorithms traditionally associated with AI have only a limited range of applicability.

In this article, I describe agent-centered search (also called real-time search or local search) and illustrate this planning paradigm with examples. Agent-centered search methods interleave planning and plan execution and restrict planning to the part of the domain around the current state of the agent, for example, the current location of a mobile robot or the current board position of a game. These methods can execute actions in the presence of time constraints and often have a small sum of planning and execution cost, both because they trade off planning and execution cost and because they allow agents to gather information early in nondeterministic domains, which reduces the amount of planning they have to perform for unencountered situations. These advantages become important as more intelligent systems are interfaced with the world and have to operate autonomously in complex environments. I researchers have studied in detail offline planning methods that first determine sequential or conditional plans (including reactive plans) and then execute them in the world.

Real-time problem solving is not only reasoning about time, it is also reasoning in time. This ability is becoming increasingly critical in systems that monitor and control complex processes in semiautonomous, ill-structured, real-world environments. Many techniques, mostly ad hoc, have been developed in both the real-time community and the AI community for solving problems within time constraints. However, a coherent, holistic picture does not exist. This article is an attempt to step back from the details and examine the entire issue of real-time problem solving from first principles. We examine the degrees of freedom available in structuring the problem space and the search process to reduce problemsolving variations and produce satisficing solutions within the time available. This structured approach aids in understanding and sorting out the relevance and utility of different real-time problem-solving techniques. Such applications are subject to the real-time constraints of the ...

This article introduces a generic framework for constraint-directed search. The research literature in constraint-directed scheduling is placed within the framework both to provide insight into, and examples of, the framework and to allow a new perspective on the scheduling literature. We show how a number of algorithms from the constraintdirected-scheduling research can be conceptualized within the framework. This conceptualization allows us to identify and compare variations of components of our framework and provides new perspective on open research issues. We discuss the prospects for an overall comparison of scheduling strategies and show that firm conclusions visa-vis such a comparison are not supported by the literature.

To the editor: I read "Do L.A. Unified's daily random searches keep students safe, or do they go too far?" and shuddered. Los Angeles Unified School District officials should be ashamed and embarrassed over the dehumanizing way they treat students. How can anyone connected with this fiasco condone such an assault on their students' personhood or create such a threatening environment so adverse to learning? Parents and students have complained, community groups have protested, the American Civil Liberties Union has become involved, but nothing changes. Perhaps now with the curtain pulled back for the broader public to see, this mean-spirited breach of trust in the name of safety will be stopped.