Abstract This paper describes DTS, a decisiontheoretic scheduler designed to employ stateof-the-art probabilistic inference technology to speed the search for efficient solutions to constraint-satisfaction problems. Our approach involves assessing the performance of heuristic control strategies that are normally hard-coded into scheduling systems, and using probabilistic inference to aggregate this information in light of features of a given problem. BPS, the Bayesian Problem-Solver [2], introduced a similar approach to solving singleagent and adversarial graph search problems, yielding orders-of-magnitude improvement over traditional techniques. Initial efforts suggest that similar improvements will be realizable when applied to typical constraint-satisfaction scheduling problems. Background Scheduling problems arise in schools, in factories, in military operations and in scientific laboratories. Although many algorithms have been proposed, scheduling remains among the most difficult of optimization problems. Because of the problem's ubiquity and complexity, small improvements to the state-of-the-art in scheduling are greeted with enormous interest by practitioners and theoreticians alike. A large class of scheduling problems can be represented as constraint-satisfaction problems (CSPs), representing attributes of tasks and resources as variables. Task attributes include the scheduled time for the task (start and end time) and its resource requirements. A schedule is constructed by assigning times and resources to tasks, while obeying the constraints *This research was supported by the National Aeronautics and Space Administration under contract NAS2-13340. of the problem. Constraints capture logical requirements (a typical resource can be used by only one task at a time) and problem requirements (task T~ requires N units of time, must be completed before task Tv, and must be completed before a specified date). One common approach to finding an assignment for the variables employs a preprocessing stage which tightens the constraints (e.g., by composing two constraints to form a third), followed by a backtrack search to find a satisfying assignment
