Abstraction, Reformulation, and Approximation: 5th by Robert P. Kurshan (auth.), Sven Koenig, Robert C. Holte

By Robert P. Kurshan (auth.), Sven Koenig, Robert C. Holte (eds.)

It has been well-known because the inception of man-made Intelligence (AI) that abstractions, challenge reformulations, and approximations (AR&A) are crucial to human good judgment reasoning and challenge fixing and to the power of platforms to cause successfully in advanced domain names. AR&A strategies were used to resolve various initiatives, together with computerized programming, constraint delight, layout, prognosis, computing device studying, seek, making plans, reasoning, online game enjoying, scheduling, and theorem proving. the first objective of AR&A strategies in such settings is to beat computational intractability. moreover, AR&A strategies are worthy for accelerating studying and for summarizing units of strategies. This quantity includes the complaints of SARA 2002, the 5th Symposium on Abstraction, Reformulation, and Approximation, held at Kananaskis Mountain resort, Kananaskis Village, Alberta (Canada), August 2 four, 2002. The SARA sequence is the continuation of 2 separate threads of workshops: AAAI workshops in 1990 and 1992, and an advert hoc sequence starting with the "Knowledge Compilation" workshop in 1986 and the "Change of illustration and Inductive Bias" workshop in 1988 with followup workshops in 1990 and 1992. the 2 workshop sequence merged in 1994 to shape the 1st SARA. next SARAs have been held in 1995, 1998, and 2000.

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Roveri, and P. Trverso. Strong planning in non-deterministic domains via model-checking. In Proc. of 4th International Conference on AI Planning and Scheduling (AIPS’00), 2000. 7. M. Clark. Construction domains: a generic type solved. In Proceedings of 20th Workshop of UK Planning and Scheduling Special Interest Group, 2001. 8. S. Cresswell, M. Fox, and D. Long. Extending TIM domain analysis to handleADL constructs. In L. McCluskey, editor, Knowledge Engineering Tools and Techniques for AI Planning: AIPS’02 Workshop, 2002.

Of 4th International Conference on AI Planning Systems, 1998. 3. F. Bacchus and F. Kabanza. Using temporal logic to express search control knowledge for planning. Artificial Intelligence, 116(1-2):123–191, 2000. 4. A. Blum and M. Furst. Fast Planning through Plan-graph Analysis. In Proc. of 14th Internation Joint Conference on AI, pages 1636–1642. Morgan Kaufmann, 1995. 5. B. Bonet and H. Geffner. Planning as heuristic search: new results. In Proc. of 4th European Conference on Planning (ECP). Springer-Verlag, 1997.

Long. Fast temporal planning in a Graphplan framework. In M. Fox and A. Coddington, editors, Planning for Temporal Domains: AIPS’02 Workshop, 2002. 18. M. Fox, D. Long, S. Bradley, and J. McKinna. Using model checking for pre-planning analysis. In AAAI Spring Symposium Series: Model-based Validation of Intelligence. AAAI Press, 2001. 32 D. Long, M. Fox, and M. Hamdi 19. M. Fox, D. Long, and M. Hamdi. Handling multiple sub-problems within a planning domain. In Proc. of 20th Workshop of UK Planning and Scheduling Special Interest Group, 2001.

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