By Eugene Fink
The goal of our learn is to augment the potency of AI challenge solvers via automating illustration alterations. we've got constructed a procedure 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 facts at the impor tance of applicable representations for the potency of AI structures. an identical challenge should be effortless or tough, looking on the way in which we describe it and at the seek set of rules we use. past paintings at the automated im provement of challenge descriptions has in most cases been restricted to the layout of person studying algorithms. The person has routinely been accountable for the alternative of algorithms applicable for a given challenge. We current a approach that integrates a number of description-changing and problem-solving algorithms. the aim of the stated paintings is to formalize the idea that of illustration and to substantiate the next speculation: a good representation-changing process could be outfitted from 3 components: • a library of problem-solving algorithms; • a library of algorithms that increase challenge descriptions; • a keep an eye on module that selects algorithms for every given problem.
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Additional resources for Changes of Problem Representation: Theory and Experiments
1997]. The reader may find a summary of PRODIGy learning techniques in the review papers by Carbonell et at.  and Veloso et at. . These results have been major contributions to machine learning; however, they have left two notable gaps. First, PRODIGy researchers tested each learning module separately, without exploring the synergetic use of multiple modules. , 1991a], the researchers have not pursued this direction. Second, there have been no automated techniques for deciding when to invoke specific learning modules.
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. 1, we will extend it and describe the implementation of Margie in the PRODIGY architecture.
First, it applies heuristics for estimating their relative utility and eliminates ineffective representations. Second, the system collects experimental data on the performance of the remaining representations, and applies statistical analysis to select the most effective domain description and solver. 5 Extended abstract The main results of the work on SHAPER include development of several description changers and a general-purpose control module. 19. Part I includes the motivation and description of the PRODIGY search.
Changes of Problem Representation: Theory and Experiments by Eugene Fink