Preventive-maintenance schedules occurring in industry are often suboptimal with regard to maintenance coallocation, loss-of-production costs, and availability. We describe the implementation and deployment of a software decision support tool for the maintenance planning of gas turbines, with the goal of reducing the direct maintenance costs and the often costly production losses during maintenance down time. The optimization problem is formally defined, and we argue that the feasibility version is NPcomplete. We outline a heuristic algorithm that can quickly solve the problem for practical purposes and validate the approach on a real-world scenario based on an oil production facility. We also compare the performance of our algorithm with results from using integer programming and discuss the deployment of the application.

Department of Computer Science Rutgers Universaty New Brunswick, New Jersey 08903 Abstract In this article we discuss a method for learning useful conditions on the application of operators during heuristic search Since learning is not attempted until a complete solution path has been found for a problem, credit for correct moves and blame for incorrect moves is easily assigned We review four learning systems that have incorporated similar techniques to learn in the domains of algebra, symbolic integration, and puzzle-solving We conclude that the basic approach of learning from solution paths can be applied t,o any situation in which problems can be solved by sequential search Finally, we examine some potential difficulties that may arise in more complex domains, and suggest some possible extensions for dealing with them. PEOPLE LEARN FROM EXPERIENCE, and for the past 25 years, Artificial Intelligence researchers have been attempting to replicate this process. In t,his article we focus on learning in domains where search is involved. Furthermore, we will restrict our attention t,o cases in which the legal operators for a task are known, and the learning task is to determine the conditions under which those operators can be usefully applied. Once such a set of heuristically useful conditions has been discovered, search will be directed down profitable We would like to thank Jaime Carbonell and Hans Berliner for helpful comments on an earlier version of this article.

Empirical evidence suggests that searching deeper in game trees using the minimax propagation rule usually improves the quality of decisions significantly. However, despite many recent theoretical analyses of the effects of minimax look-ahead, however, this phenomenon has still not been convincingly explained. Instead, much attention has been given to socalled pathological behavior, which occurs under certain assumptions. This article supports the view that pathology is a direct result of these underlying theoretical assumptions. Pathology does not occur in practice, because these assumptions do not apply in realistic domains.

The object is to bring the situation, or problem state, forward from its initial configuration to one satisfying a goal condition. For example, an initial situation might be the placement of chessmen on the board at the beginning of the game; the desired goal, any board configuration that is a checkmate; and the operators, rules for the legal moves in chess. This difference is then used to index the (forward) operator most relevant to reducing the difference. If this especially relevant operator cannot be immediately applied to the present problem state, subgoals are set up to change the problem state so that the relevant operator can be applied. After these subgoals are solved, the relevant operator is applied and the resulting, modified situation becomes a new starting point from which to solve for the original goal.

However, if it is incorrect, there must be some relationship between the two that allows them to live harmoniously within a single theory. The nature of this relationship is the focus of this article. In passing we note that the theory of weak problem solvers has been well-developed for over a decade; see Kilsson (1971) for example. Some aspects of MYCIN don't fit the problem reduction For example, a THE AI MAGAZINE Summer 1983 25 production whose action part is a conjunction of atomic formulae corresponds to a separate operator for each atomic formula in the conjunction. MYCIN's search strategy effectively applies such operators in a group.

Automated planning is a branch of AI that addresses the problem of generating a set of actions to achieve a specified goal state, given an initial state of the world. It is an active area of research that is central to the development of intelligent agents and au - tonomous robots. In many real-world applications, a multitude of valid plans exist, and a user distinguishes plans of high quality by how well they adhere to the user's preferences. To generate such high-quality plans automatically, a planning system must provide a means of specifying the user's preferences with respect to the planning task, as well as a means of generating plans that ideally optimize these preferences. In the last few years, there has been significant research in the area of planning with preferences.

The map is composed of thousands of scene locations, and for each location there are tens of different images of varying quality to choose from. Constraints and preferences on map quality make it desirable to develop an automated solution to the map-generation problem. This article formulates a global map-generator problem as a constraint-optimization problem (GMG-COP) and describes an approach to solving it using local search. The article also describes the integration of a GMG solver into a graphical user interface for visualizing and comparing solutions, thus allowing for solutions to be generated with human participation and guidance. Data Center to produce a high-resolution mosaic map of the Earth.

A set of challenges to researchers is presented that, we believe, must be met to ensure the practical applicability of parallel SAT solvers in the future. All these challenges are described informally but put into perspective with related research results, and a (subjective) grading of difficulty for each of them is provided. Parallelism is the wave of the future … and always will be. It conveys a general sentiment that the coming of parallel architectures would forever be delayed. This was indeed true at a time when clock-speed growth seemed always possible, allowing sequential code seamlessly to become faster.

This article reviews the two most recent IEEE Conference on Computational Intelligence and Games (CIG) StarCraft Artificial Intelligence (AI) Competitions organized by the authors; these were the fourth and fifth in a series of annual competitions initiated in 2011. StarCraft AI Competitions have been hosted in conjunction with three different events: the AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment (AIIDE), CIG, and Student StarCraft AI Tournament (SSCAIT). The purpose of these competitions is to design bots that are able autonomously and successfully to play the StarCraft game by implementing real-time strategies. Recent results reveal the promising use of AI techniques in creating successful AI entries, but there is room for improvement with respect to the bots' ability to adapt and learn to defeat humans and scripted AI bots. It provides a dynamic environment in which several agents interact to build military units with which to fight against an opponent.

Incremental search reuses information from previous searches to find solutions to a series of similar search problems potentially faster than is possible by solving each search problem from scratch. This is important because many AI systems have to adapt their plans continuously to changes in (their knowledge of) the world. AI has developed several ways of speeding up searches by trading off the search time and the cost of the resulting path, which includes using inadmissible heuristics (Pohl 1973, 1970) and search with limited look ahead (Korf 1990; Ishida and Korf 1991; Koenig 2001), which is also called real-time or agent-centered search. In this article, we discuss a different way of speeding up searches, namely, incremental search. Incremental search is a search technique for continual planning (or, synonymously, replanning, plan reuse, and lifelong planning) that reuses information from previous searches to find solutions to a series of similar search problems potentially faster than is possible by solving each search problem from scratch.