A number of successful applications of automated planning and scheduling applications to spacecraft operations have recently been reported in the literature. However, these applications have been one-of-a-kind applications that required a substantial amount of development effort. In this paper, we describe ASPEN, a modular, reconfigurable application framework which is capable of supporting a wide variety of planning and scheduling applications. We describe the architecture of ASPEN, as well as a number of current spacecraft control/operations applications in progress.

In most real-world reasoning problems, planning and scheduling phases are loosely coupled. For example, in project planning, the user comes up with a task list and schedules it with a scheduling tool like Microsoft Project. One can view automated planning in a similar way in which there is an action selection phase where actions are selected and ordered to reach the desired goals, and a resource allocation phase where enough resources are assigned to ensure the successful execution of the chosen actions. On the other hand, most existing automated planners studied in Artificial Intelligence do not exploit this loose-coupling and perform both action selection and resource assignment employing the same algorithm. The current work shows that the above strategy severely curtails the scale-up potential of existing state of the art planners which can be overcome by leveraging the loose coupling. Specifically, a novel planning framework called RealPlan is developed in which resource allocatio...

This work integrates three related AI search techniques and applies the result to processing computational semantics, both in the analysis of source text to discover underlying semantics, as well as in the planning of target text from an input semantic representation. We summarize the approach as "Hunter-Gatherer " (HG): ffl Branch-and-Bound and Constraint Satisfaction allow us to "hunt down " nonoptimal and impossible solutions and prune them from the search space. ffl Solution Synthesis methods then "gather together " all optimal solutions while

Practical scheduling usually has to react to many unpredictable events and uncertainties in the production environment. Although often possible in theory, it is undesirable to reschedule from scratch in such cases. Since the surrounding organization will be prepared for the predicted schedule it is important to change only those features of the schedule that are necessary. We show how on one side fuzzy logic can be used to support the construction of schedules that are robust with respect to changes due to certain types of event. On the other side we show how a reaction can be restricted to a small environment by means of fuzzy constraints and a repair-based problem-solving strategy. We demonstrate the proposed representation and problem-solving method by introducing a scheduling application in a steelmaking plant. We construct a preliminary schedule by taking into account only the most likely duration of operations. This schedule is iteratively "repaired" until some threshold evaluation is found. A repair is found with a local search procedure based on Tabu Search. Finally, we show which events can lead to reactive scheduling and how this is supported by the repair strategy.

The choice of search algorithm can play a vital role in the success of a scheduling application. In this paper, we investigate the contribution of search algorithms in solving a real-world warehouse scheduling problem. We compare performance of three types of scheduling algorithms: heuristic, genetic algorithms and local search.

A number of successful applications of automated planning and scheduling applications to spacecraft operations have recently been reported in the literature. However, these applications have been one-of-a-kind applications that required a substantial amount of development effort. In this paper, we describe ASPEN (Automated Planning/Scheduling Environment), a modular, reconfigurable application framework which is capable of supporting a wide variety of planning and scheduling applications. We describe the architecture of ASPEN, as well as a number of current spacecraft control/operations applications in progress.

In this paper we describe a finite-capacity algorithm that can be used for production scheduling in a semiconductor wafer fabrication facility (wafer fab). The algorithm is a beam-search-type algorithm. We describe the basic features of the algorithm. The implementation of the algorithm is based on the ILOG-Solver libraries. We describe the simulation environment, which is used to evaluate the performance of the proposed algorithm. We show some results from computational experiments with the algorithm and the simulation test-bed described.

Abstract. Scheduling is a knowledge-intensive task spanning over many activities in day-to-day life. It deals with the temporallybound assignment of jobs to resources. Although scheduling has been extensively researched in the AI community for the past 30 years, efforts have primarily focused on specific applications, algorithms, or 'scheduling shells ' and no comprehensive analysis exists on the nature of scheduling problems, which provides a formal account of what scheduling is, independently of the way scheduling problems can be approached. Research on KBS development by reuse makes use of ontologies, to provide knowledge-level specifications of reusable KBS components. In this paper we describe a task ontology, which formally characterises the nature of scheduling problems, independently of particular application domains and independently of how the problems can be solved. Our results provide a comprehensive, domain-independent and formally specified reference model for scheduling applications. This can be used as the basis for further analyses of the class of scheduling problems and also as a concrete reusable resource to support knowledge acquisition and system development in scheduling applications. 1

Abstract. Many real-world constraint satisfaction problems (CSPs) can be over-constrained or too large to solve using a standard constructive/backtracking approach. Instead, faster heuristic techniques have been proposed that perform a partial search of all possible solutions using an iterative repair or hill-climbing approach. The main problem with such approaches is that they can become stuck in local minima. Consequently, various strategies or meta-heuristics have been developed to escape from local minima. This paper investigates the application of one such meta-heuristic, weighted iterative repair, to solving a real-world problem of scheduling nurses at an Australian hospital. Weighted iterative repair has already proved successful in solving various binary CSPs. The current research extends this work by looking at a non-binary problem formulation, and partial constraint satisfaction involving hard and soft constraints. This has lead to the development of a soft constraint heuristic to improve the level of soft constraint optimisation and an extension of the original weighted iterative repair that avoids certain forms of cyclic behaviour. It is also demonstrated that weighted iterative repair can learn from repeatedly solving the same problem. and that restarting the algorithm on the same problem can result in faster execution times. The overall results show that weighted iterative repair finds better quality solutions than a standard iterative repair, whilst approaching near optimal solutions in less time than an alternative integer programming approach. 1

Planning consists of an action selection phase where actions are selected and ordered to reach the desired goals, and a resource allocation phase where enough resources are assigned to ensure the successful execution of the chosen actions. In most realworld problems, these two phases are loosely coupled. On the other hand, most existing planners do not exploit this loose-coupling and perform both action selection and resource assignment employing the same algorithm. The current work shows that the above strategy severely curtails the scale-up potential of existing planners, including such recent ones as Graphplan and Blackbox. In response, a novel planning framework was developed in which resource allocation is de-coupled from planning and is handled in a separate "scheduling" phase. Implementing this framework raises several interesting issues regarding the role of resources in planning, the interactions between the planning and scheduling phases and the choices in selecting the meth...