In this paper we address the problem of automatically generating job workflows for the Grid. These workflows describe the execution of a complex application built from individual application components. In our work we have developed two workflow generators: the first (the Concrete Workflow Generator CWG) maps an abstract workflow defined in terms of application-level components to the set of available Grid resources. The second generator (Abstract and Concrete Workflow Generator, ACWG) takes a wider perspective and not only performs the abstract to concrete mapping but also enables the construction of the abstract workflow based on the available components. This system operates in the application domain and chooses application components based on the application metadata attributes. We describe our current ACWG based on AI planning technologies and outline how these technologies can play a crucial role in developing complex application workflows in Grid environments. Although our work is preliminary, CWG has already been used to map high energy physics applications onto the Grid. In one particular experiment, a set of production runs lasted 7 days and resulted in the generation of 167,500 events by 678 jobs. Additionally, ACWG was used to map gravitational physics workflows, with hundreds of nodes onto the available resources, resulting in 975 tasks, 1365 data transfers and 975 output files produced.

yQSS Group Inc. zQSS Group Inc. xRIACS experiment is assigned a scientific value). Different ob-servations and experiments take differing amounts of time and consume differing amounts of power and data storage.There are, in general, a number of constraints that govern the rovers activities: ffl There are time, power, data storage, and posi-tioning constraints for performing different activities. Time constraints often result from illuminationrequirement--that is, experiments may require that a target rock or sample be illuminated with a certain in-tensity, or from a certain angle.

This paper provides an overview of the field of qualitative decision theory: its motivating tasks and issues, its antecedents, and its prospects. Qualitative decision theory studies qualitative approaches to problems of decision making and their sound and effective reconciliation and integration with quantitative approaches. Though it inherits from a long tradition, the field offers a new focus on a number of important unanswered questions of common concern to artificial intelligence, economics, law, psychology, and management.

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We show that for several variations of partially observable Markov decision processes, polynomial-time algorithms for nding control policies are unlikely to or simply don't have guarantees of nding policies within a constant factor or a constant summand of optimal. Here "unlikely" means \unless some complexity classes collapse," where the collapses considered are P = NP, P = PSPACE, or P = EXP. Until or unless these collapses are shown to hold, any control-policy designer must choose between such performance guarantees and ecient computation.

Military operations planning involves concurrent actions, resource assignment, and conflicting costs. Individual tasks sometimes fail with a known probability, promoting a decision-theoretic approach. The planner must choose between multiple tasks that achieve similar outcomes but have different costs. The military domain is particularly suited to automated methods because hundreds of tasks, specified by many planning staff, need to be quickly and robustly coordinated. The authors

We have implemented and tested a PDA-based system that gives a shopper directions through a shopping mall on the basis of (a) the types of products that the shopper has expressed an interest in, (b) the shopper's current location, and (c) the purchases that the shopper has made so far. The system uses decision-theoretic planning to compute a policy that optimizes the expected utility of a shopper's walk through the shopping mall, taking into account uncertainty about (a) whether the shopper will actually find a suitable product in a given location and (b) the time required for each purchase. To assess the acceptability of this approach to potential users, on two floors of a building we constructed a mock-up of a shopping mall with 15 stores. Each of 20 subjects in our study shopped twice in the mall, once using our system and once using a paper map. The subjects completed their tasks significantly more effectively using the PDA-based shopping guide, and they showed a clear preference for it. They also yielded numerous specific ideas about the conditions under which the guide will be useful and about ways of increasing its usability.

Grid computing is emerging as key enabling infrastructure for science. A key challenge for distributed computation over the Grid is the synthesis on-demand of end-toend scientific applications of unprecedented scale that draw from pools of specialized scientific components to derive elaborate new results. In this paper, we outline the technical issues that need to be addressed in order to meet this challenge, including usability, robustness, and scale. We describe Pegasus, a system to generate executable grid workflows given a high-level specification of desired results. Pegasus uses Artificial Intelligence planning techniques to compose valid end-to-end workflows, and has been used in several scientific applications. We also outline our design for a more distributed and knowledge-rich architecture.

The recent advances in computer speed and algorithms for probabilistic inference have led to a resurgence of work on planning under uncertainty. The aim is to design AI planners for environments where there may be incomplete or faulty information, where actions may not always have the same results and where there may be tradeoffs between the different possible outcomes of a plan. Addressing uncertainty in AI planning algorithms will greatly increase the range of potential applications but there is plenty of work to be done before we see practical decision-theoretic planning systems. This article outlines some of the challenges that need to be overcome and surveys some of the recent work in the area. Introduction AI planning algorithms are concerned with finding a course of action to be carried out by some agent to achieve its goals. In problems where actions can lead to a number of different possible outcomes, or where the benefits of executing a plan must be weighed against t...