By D Sleeman; Peter Edwards
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Extra resources for Machine learning : proceedings of the ninth international workshop (ML92)
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Our experiments do not demon strate the exact source of power in FS2. It is important to find out whether FS2 gets most of its power from the forward search or from its learning method. In particular an important issue for future research is to investigate the role that preservable censors learned by FS2 by incomplete explanations of search failures can play in the backward means-ends analysis search. An important issue for future research is to evaluate the relative effect of recursion on the branching factor of forward and backward chaining types of search.
It is shown by the al gorithm EGU that a training example can be used to guide unfolding while maintain ing completeness, which previously has been questioned. Experimental results are pre sented showing that the problem of decreas ing efficiency can be substantially reduced when search state redundancy is eliminated. 1 INTRODUCTION The benefits from explanation-based learning (EBL) come from reordering effects and decreased path costs when learned clauses1 are successfully applied [Minton 90] .
General Features _ Step 2 (SM Generation) Examples of Texture Classes ----•� & General Descriptions of Texture Oasses (SM) General Features Figure 2: Two-step Inductive Learning In the example presented in Figure 1 and Table 1 the initial features {xl, x2} are transformed into new features {PA 1 , PA2. PA3 } . This transformation can be expressed as follows: T ( {xl, x2} ) => { MATCH ( { x l ,x2} , PA 1 ), MATCH ( {xl,x2} , PA2 ), MATCH ( {xl,x2} , PA3 ) } Corresponding feature values in two domains for three examples of CLASS 1 (a, b, and c black dots in Figure 1) are shown below Initial example a: { 1 , 3 } b: {3 , 3 } c: {4 .