Decision-theoretic control of EUVE telescope scheduling

This paper describes a decision theoretic scheduler (DTS) designed to employ state-of-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 the features of a given problem. The Bayesian Problem-Solver (BPS) introduced a similar approach to solving single agent and adversarial graph search patterns 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

DTS: Building custom, intelligent schedulers

DTS is a decision-theoretic scheduler, built on top of a flexible toolkit -- this paper focuses on how the toolkit might be reused in future NASA mission schedulers. The toolkit includes a user-customizable scheduling interface, and a 'Just-For-You' optimization engine. The customizable interface is built on two metaphors: objects and dynamic graphs. Objects help to structure problem specifications and related data, while dynamic graphs simplify the specification of graphical schedule editors (such as Gantt charts). The interface can be used with any 'back-end' scheduler, through dynamically-loaded code, interprocess communication, or a shared database. The 'Just-For-You' optimization engine includes user-specific utility functions, automatically compiled heuristic evaluations, and a postprocessing facility for enforcing scheduling policies. The optimization engine is based on BPS, the Bayesian Problem-Solver (1,2), which introduced a similar approach to solving single-agent and adversarial graph search problems

Automating Mission Scheduling for Space-Based Observatories

In this paper we describe the use of our planning and scheduling framework, HSTS, to reduce the complexity of science mission planning. This work is part of an overall project to enable a small team of scientists to control the operations of a spacecraft. The present process is highly labor intensive. Users (scientists and operators) rely on a non-codified understanding of the different spacecraft subsystems and of their operating constraints. They use a variety of software tools to support their decision making process. This paper considers the types of decision making that need to be supported/automated, the nature of the domain constraints and the capabilities needed to address them successfully, and the nature of external software systems with which the core planning/scheduling engine needs to interact. HSTS has been applied to science scheduling for EUVE and Cassini and is being adapted to support autonomous spacecraft operations in the New Millennium initiative

Experiments with a Decision-Theoretic Scheduler*

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

Generating Admissible Heuristics by Criticizing Solutions to Relaxed Models

This paper examines one paradigm used to develop admissible heuristics: problem relaxation [10, 11,32]. This consists of three steps: simplify (or relax) a problem, solve the simplified problem, and use that solution as advice to guide the search for a solution to the original problem. We introduce an extension to this methodology which exploits the simplicity of relaxed models. By criticizing the feasibility of a relaxed solution, we arrive at a closer approximation of the solution to the original problem. This solution-criticism process recovers some of the information lost by relaxation. and yields more powerful admissible heuristics than by relaxation alone. We apply our methodology to the Traveling-Salesman problem and the N Puzzle. For the Traveling-Salesman Problem, it yields the well known. admissible minimum spanning tree heuristic. For the Eight and Fifteen Puzzles (in general the N puzzle), it yields a new heuristic which performs significantly better than all previously known heuristics

The CICT Earth Science Systems Analysis Model

Contents include the following: Computing Information and Communications Technology (CICT) Systems Analysis. Our modeling approach: a 3-part schematic investment model of technology change, impact assessment and prioritization. A whirlwind tour of our model. Lessons learned