The properties of chess as a domain for problem-solving by machine resemble those of other applications. Much conceptualization is needed from human practitioners but very little from new computers, allowing brute-force searches enormously larger than could be covered by the human brain. Because of its finite and modular structure and the availability of a large corpus of systematized conceptual knowledge bequeathed by the masters, chess constitutes ideal laboratory material for studying the relative effectiveness and computational costs of search-driven versus concept-driven approaches.
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