By Eugene Fink

The function of our study is to augment the potency of AI challenge solvers via automating illustration adjustments. we've constructed a approach that improves the outline of enter difficulties and selects a suitable seek set of rules for every given challenge. Motivation. Researchers have accrued a lot proof at the impor tance of applicable representations for the potency of AI structures. an identical challenge might be effortless or tricky, reckoning on the way in which we describe it and at the seek set of rules we use. prior paintings at the computerized im provement of challenge descriptions has typically been constrained to the layout of person studying algorithms. The person has ordinarily been chargeable for the alternative of algorithms applicable for a given challenge. We current a procedure that integrates a number of description-changing and problem-solving algorithms. the aim of the said paintings is to formalize the concept that of illustration and to verify the next speculation: a good representation-changing method should be outfitted from 3 elements: • a library of problem-solving algorithms; • a library of algorithms that enhance challenge descriptions; • a keep watch over module that selects algorithms for every given problem.

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**Extra info for Changes of Problem Representation: Theory and Experiments**

**Example text**

1998] have begun development of an architecture for integration of AI planning techniques. Their architecture, called PLAN++, includes tools for implementing, modifying, and reusing the main elements of planning systems. The purpose is to modularize typical planning algorithms, construct a large library of search modules, and use them as building blocks for new algorithms. Since the effectiveness of planning techniques varies across domains, the authors of PLAN++ intend to design software tools that enable the user to select appropriate modules and configure them for specific domains.

If this information is available, the changer algorithm utilizes it to generate a better description; otherwise, the algorithm uses some default assumptions. The optional input may include restrictions on the allowed problem instances, useful knowledge about domain properties, and advice from the user. For example, we may specify constraints on the allowed problems as an input to Margie. 2). As another example, the user may pre-select some primary effects of operators. 1). 14; however, this specification does not account for advanced features of the PRODIGY domain language.

3, we will describe a general utility function that unifies the three evaluation factors. When evaluating the utility of a new representation, we have to account for the overall time for improving a domain description, selecting a problem solver, and using the resulting representation to solve given problems. The system is effective only if this overall time is smaller than the time for solving the problems with the initial description and some fixed solver algorithm. 2 Specifications of description changers The current version of SHAPER includes seven description changers.