Titlebook: Change of Representation and Inductive Bias; D. Paul Benjamin Book 1990 Kluwer Academic Publishers 1990 autonomous system.hierarchical pla

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Improving Problem Solving Performance by Example Guided Reformulation,eatures are explicit in the syntax. This method, in contrast to previous reformulation methods, employs sets of training examples to constrain and direct the reformulation process. The use of examples offers two advantages over purely deductive approaches: First, the examples identify the exact part

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STRATA: Problem Reformulation and Abstract Data Types,e that the performance of an algorithm design system can be enhanced by first reformulating a problem specification into an abstract data type by incorporating relevant problem properties. We will describe the semantics of a class of valid problem reformulations and relate them to the semantics of t

aviator

Abstracting First-Order Theories,in search, so that concrete level problems can be solved by first mapping the problems to the abstract level, performing search within the abstract space, and using the abstract solution to guide the concrete level search. Formalizing this mapping requires a precise specification of the relationship

協(xié)定

A Theory of Abstraction for Hierarchical Planning,ot well understood. This paper formally defines hierarchical planning, shows that the technique can reduce an exponential search space to a linear one, and identifies the assumptions under which this analysis holds. Since these assumptions would be difficult to guarantee in general, the paper identi

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Similarities in Problem Solving Strategies,to be a functor from the category of problems to the category of solutions. Two strategies are considered similar if there is a natural transformation on one into the other..Prof. Takahara considered the case where problems are defined by pay-off matrices and solutions are pre-orders on the rows of

過(guò)時(shí)

Constraint Incorporation and the Structure Mismatch Problem,icit forms. The compilation techniques we are developing produce a . problem solver where the generators produce values for solution parameters, and the testers check the satisfaction of the problem constraints. To improve the efficiency of the generate-and-test problem solver, our approach uses . t