Abstract
In order to deal with the inherent combinatorial nature of many tasks in artificial intelligence, domain‐specific knowledge has been used to control search and reasoning or to eliminate the need for general inference altogether. However, the process of acquiring domain knowledge is an important bottleneck in the use of such “knowledge‐intensive” methods. Compute‐intensive methods, on the other hand, use extensive search and reasoning strategies to limit the need for detailed domain‐specific knowledge. The idea is to derive much of the needed information from a relatively compact formalization of the domain under consideration. Up until recently, such general reasoning strategies were much too expensive for use in applications of interesting size but recent advances in reasoning and search methods have shown that compute‐intensive methods provide a promising alternative to knowledge‐intensive methods.
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Selman, B. Compute‐intensive methods in artificial intelligence. Annals of Mathematics and Artificial Intelligence 28, 35–38 (2000). https://doi.org/10.1023/A:1018943920174
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DOI: https://doi.org/10.1023/A:1018943920174