Acquisition of Evaluation Function of high precision by Learning

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The main and final target of this research is the automatic construction of evaluators by learning, integrating state evaluation by learning, concept formation, and creation of efficient evaluators. Main results of the component researches follow.1.Automatic construction of evaluation functionsThe evaluation functions are constructed, without human preparation, only from the basic rules and framework of the target field. The essential part of this construction is the automatic creation of simple data sets for state evaluation, followed by the selection based on the appropriateness of the data. Following some preparatory studies carried out in 2001 and 2002, more precise evaluation functions are obtained by the analysis of bulk data for states evaluation.2.Concept formation and structure analysisThe structure of a Galois lattice, based on a partial order defined among the set of simple piecewise statuses, is studied as a framework of concepts. In this research, semistructured data, frequently found in the real world, is also studied. The "summary structure" can be extracted from such "non-well ordered structure" by an analytical method.3.Reflection and aspectsIn the target system of this research, a lot of software agents for state evaluation are to be prepared by the method of reflection. These agents are expected to act optimally in the real evaluation referring the concept database. In this research, the framework of aspect oriented programming is studied for interclass function increments. Some results in support systems and theoretical framework in aspect oriented programming are obtained.