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By Robert K. Lindsay, etc.

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Extra info for Applications of Artificial Intelligence for Organic Chemistry: The Dendral Project (McGraw-Hill advanced computer science series)

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Several of the search heuristics defmed above rely on these secondary languages. Hill climbing needs a language in which to define its gradient of “warmth,” and statistically guided search is based on a rather general but blurry-visioned search for descriptors that have some correlational information. Abstraction planning is another way of bringing to bear different views of the problem space. On a more general level, apart from any of these methods, is the representation problem. A problem has in general many possible representations.

In solving such problems, the chemist may have access to useful information from many sources, including data from the several powerful analytic methods just described. Matching new data against libraries is a standard method of structure identification. However, when new compounds are encountered, as frequently happens,14 library matching is insufficient and the chemist must interpret the data from sources such as the ones described here. These various sources of information provide different types of facts about the structure of the molecule being investigated.

We hope these liabilities are offset by a measure of efficiency in solving interesting and important members of the problem class. Heuristic programs characteristically have the flavor of exploration that is suggested ARTIFICIAL INTELLIGENCE 33 by our problem-solving paradigm. In addition, however, the exploration is selective rather than exhaustive. We distinguish two types of problem-space exploration that we call search (not to be confused with exploration in general) and generation. Search is the production and examination of problem states.

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