{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2023,7,4]],"date-time":"2023-07-04T21:53:00Z","timestamp":1688507580184},"reference-count":124,"publisher":"Association for Computing Machinery (ACM)","issue":"1","content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["ACM Comput. Surv."],"published-print":{"date-parts":[[2001,3]]},"abstract":"\n Many tasks require \u201creasoning\u201d\u2014i.e., deriving conclusions from a corpus of explicitly stored information\u2014to solve their range of problems. An ideal reasoning system would produce all-and-only the\n correct<\/jats:italic>\n answers to every possible query, produce answers that are as\n specific<\/jats:italic>\n as possible, be\n expressive<\/jats:italic>\n enough to permit any possible fact to be stored and any possible query to be asked, and be (time)\n efficient<\/jats:italic>\n . Unfortunately, this is provably impossible: as correct and precise systems become more expressive, they can become increasingly inefficient, or even undecidable. This survey first formalizes these hardness results, in the context of both logic- and probability-based reasoning, then overviews the techniques now used to address, or at least side-step, this dilemma.\n <\/jats:p>","DOI":"10.1145\/375360.375363","type":"journal-article","created":{"date-parts":[[2002,7,27]],"date-time":"2002-07-27T11:32:04Z","timestamp":1027769524000},"page":"1-30","update-policy":"http:\/\/dx.doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":25,"title":["Efficient reasoning"],"prefix":"10.1145","volume":"33","author":[{"given":"Russell","family":"Greiner","sequence":"first","affiliation":[{"name":"Univ. of Alberta, Edmonton, Alta., Canada"}]},{"given":"Christian","family":"Darken","sequence":"additional","affiliation":[{"name":"Siemens Corporate Research, Princeton, NJ"}]},{"given":"N. 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