Download Abstraction, Reformulation and Approximation: 6th by Scot Anderson, Peter Revesz (auth.), Jean-Daniel Zucker, PDF

By Scot Anderson, Peter Revesz (auth.), Jean-Daniel Zucker, Lorenza Saitta (eds.)

This publication constitutes the refereed court cases of the sixth overseas Symposium on Abstraction, Reformulation, and Approximation, SARA 2005, held in Airth fort, Scotland, united kingdom in July 2005.

The 17 revised complete papers and eight prolonged abstracts have been conscientiously reviewed and chosen for inclusion within the ebook. additionally incorporated are three invited papers and eight examine summaries. All present facets of abstraction, reformulation, and approximation within the context of human common sense reasoning, challenge fixing, and successfully reasoning in complicated domain names are addressed. one of the software fields of those concepts are automated programming, constraint pride, layout, prognosis, computer studying, seek, making plans, reasoning, online game taking part in, scheduling, and theorem proving.

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Additional resources for Abstraction, Reformulation and Approximation: 6th International Symposium, SARA 2005, Airth Castle, Scotland, UK, July 26-29, 2005. Proceedings

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Porteous, L. Sebastia, and J. Hoffmann. On the extraction, ordering, and usage of landmarks in planning. In Proceedings of the 6th European Conference on Planning (ECP01), 2001. 14. E. D. Sacerdoti. Planning in a hierarchy of abstraction spaces. Artificial Intelligence, 5(2):115–135, 1974. 15. D. E. Smith and M. A. Peot. A critical look at knoblock’s hierarchy mechanism. In J. Hendler, editor, Proceedings of the 1st International Conference on Artificial Intelligence Planning Systems(AIPS92), pages 307–308.

To manipulate blocks in a room r1 , the robot has to be in room r1 too. Therefore the blocks domain operators (pick-up, put-down, stack, and unstack) use a literal in their preconditions to impose such restriction. Now, suppose that the agent wants to achieve the two goals g1 = (on b a), and g2 = (robot-at = r2 ) (where both blocks a and b are in room r3 ). If the agent achieves g2 first then solving g1 will violate g2 ; because the robot has to move to room r3 to be able to stack b on a. Consequently, g1 has to be achieved before g2 .

Take more actions) and subsequently collect a larger reward. 1 The Model Problem Specification To test whether heuristic estimates generated by abstraction produce any savings as compared to uninformed search, the LAO* algorithm was used to solve the following problem that involves uncertainty regarding the use of resources. An agent operates autonomously for a period of time. Its goal is to perform a sequence of M tasks (see Fig. 3). A terminal state is reached when the agent either performs all tasks or runs out of at least one resource.

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