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.

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Classical planning is the problem of finding a sequence of actions to achieve a goal given an exact characterization of a domain. An algorithm to solve this problem is presented, which searches a space of plan prefixes, trying to extend one of them to a complete sequence of actions. It is guided by a heuristic estimator based on regression-match graphs, which attempt to characterize the entire subgoal structure of the remaining part of the problem. These graphs simplify the structure by neglecting goal interactions and by assuming that variables in goal conjunctions should be bound in such a way as to make as many conjuncts as possible true without further work. In some domains, these approximations work very well, and experiments show that many classical-planning problems can solved with very little search. 1 Definition of the Problem The classical planning problem is to generate a sequence of actions that make a given proposition true, in a domain in which there is perfect informati...

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Domain-independent planning is a hard combinatorial problem. Taking into account plan quality makes the task even more difficult. This article introduces Planning by Rewriting (PbR), a new paradigm for efficient high-quality domain-independent planning. PbR exploits declarative plan-rewriting rules and efficient local search techniques to transform an easy-to-generate, but possibly suboptimal, initial plan into a high-quality plan. In addition to addressing the issues of planning efficiency and plan quality, this framework offers a new anytime planning algorithm. We have implemented this planner and applied it to several existing domains. The experimental results show that the PbR approach provides significant savings in planning effort while generating high-quality plans.

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This paper reports on the first phase of the Continuous Planning and Execution Framework (CPEF), a system that employs sophisticated plan generation, execution, monitoring, and repair capabilities to solve complex tasks in unpredictable and dynamic environments. CPEF embraces the philosophy that plans are dynamic, open-ended artifacts that must evolve in response to an ever-changing environment. In particular, plans and activities are updated in response to new information and requirements to ensure that they remain viable and relevant. Users are an integral part of the process, providing input that influences plan generation, repair, and overall system control. CPEF has been applied successfully to generate, execute, and repair complex plans for gaining and maintaining air superiority within a simulated operating environment.

The Internet provides access to a wealth of information. For any given topic or application domain there are a variety of available information sources. However, current systems, such as search engines or topic directories in the World Wide Web, offer only very limited capabilities for locating, combining, and organizing information. Mediators, systems that provide integrated access and database-like query capabilities to information distributed over heterogeneous sources, are critical to realize the full potential of meaningful access to networked information. Query planning, the task of generating a cost-efficient plan that computes a user query from the relevant information sources, is central to mediator systems. However, query planning is a computationally hard problem due to the large number of possible sources and possible orderings on the operations to process the data. Moreover, the choice of sources, data processing operations, and their ordering, strongly affects the plan c...

One of the less appreciated obstacles to scaling multi-agent systems is understanding the impact of the role(s) that people will play in those systems. As we try to adapt existing software tools and agent-based applications to play supportive roles in larger multi-agent systems, we must develop strategies for coordinating not only the problem solving behavior of these agent communities, but also their information sharing and interactive behavior. Our research interest is in mixed-initiative control of intelligent systems [Burstein and McDermott, 1996; Burstein et al., 1998; Ferguson et al., 1996a] and, in particular, of interactive planning systems comprised of a heterogeneous collection of software agents. In this paper, we describe our experience constructing a prototype tool combining elements of TRIPS [Ferguson and Allen, 1998], an interactive, mixed-initiative agentbased planning architecture using spoken natural language dialogue, with the CAMPS Mission Planner, an interactive airlift scheduling tool developed for the Air Force [Emerson and Burstein, 1999], together with some related resource management agents representing other parts of the airlift planning organization. The latter scheduling tools were not originally designed to participate as part of a mixed-initiative, interactive agent community, but rather were designed for direct user interaction through their own GUIs. We describe some requirements revealed by this effort for effective mixed-initiative interaction in such an environment, including the role of explanation, the need for contextual information sharing among the agents, and our approach to intelligent invocation and integration of available agent capabilities. i 1