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We address the problem of computing a policy for fully observable non-deterministic (FOND) planning problems. By focusing on the relevant aspects of the state of the world, we introduce a series of improvements to the previous state of the art and extend the applicability of our planner, PRP, to work in an online setting. The use of state relevance allows(More)
Tabu search algorithms are among the most effective approaches for solving the job-shop scheduling problem (JSP). Yet, we have little understanding of why these algorithms work so well, and under what conditions. We develop a model of problem difficulty for tabu search in the JSP, borrowing from similar models developed for SAT and other NP-complete(More)
In this paper, we present a novel discrepancy-based search technique implemented as an instance of the generic search procedures framework introduced in [10]. Our empirical results indicate that the Discrepancy-Bounded Depth First Search (DBDFS) procedure exhibits a number of good properties. As a discrepancy based search technique, it is able to quickly(More)
Most classical scheduling formulations assume a fixed and known duration for each activity. In this paper, we weaken this assumption, requiring instead that each duration can be represented by an independent random variable with a known mean and variance. The best solutions are ones which have a high probability of achieving a good makespan. We first create(More)
Considerable effort has been spent extending the scope of planning beyond propositional domains to include, for example , time and numbers. Each extension has been designed as a separate specific semantic enrichment of the underlying planning model, with its own syntax and customised integration into a planning algorithm. Inspired by work on SAT Modulo(More)
tics with the same consistency techniques (Nuijten, 1994). In this paper we re-evaluate texture-based heuristics in light of recent advances in scheduling technology and show that on two job shop scheduling problem sets (a widely used set of Operations Research benchmark problems and a set of randomly generated, hard problems) a texture-based heuristic(More)
A hybrid technique using constraint programming and linear programming is applied to the problem of scheduling with earliness and tardiness costs. The linear model maintains a set of relaxed optimal start times which are used to guide the constraint programming search heuristic. In addition, the constraint programming problem model employs the strong(More)
While the exploitation of problem structure by heuristic search techniques has a long history in AI (Simon, 1973), many of the advances in constraint-directed scheduling technology in the 1990s have resulted from the creation of powerful propagation techniques. In this paper, we return to the hypothesis that understanding of problem structure plays a(More)