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Artificial intelligence: A modern approach
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- Buchanan, 1983, Introduction to the COMTEX microfiche edition of memos from the Stanford Artificial Intelligence Laboratory, AI Magazine, 4, 37
- Luckham, 1971, Extracting information from resolution proof trees, Artif. Intell., 2, 27, 10.1016/0004-3702(71)90003-8 · DOI
- Minsky, 1983, Introduction to the COMTEX microfiche edition of the early MIT Artificial Intelligence Memos, AI Magazine, 4, 19
- Newell, 1984, Introduction to the COMTEX microfiche edition of reports on artificial intelligence from Carnegie-Mellon University, AI Magazine, 5, 35
- Nilsson, 1984, Introduction to the COMTEX microfiche edition of the SRI Artificial Intelligence Center Technical Notes, AI Magazine, 5, 41
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Metadata
- Authors
- Nilsson, N. J.
- Venue
- Artificial Intelligence
- Publisher
- Elsevier BV
- Published
- 1996-04
- Volume
- 82
- Issue
- 1-2
- Pages
- 369-380
- ISSN
- 0004-3702 (ISSN-L)
- OpenAlex
- W2623293810
- Cited by
- 10,583
- References
- 0
- FWCI
- 41.33
- Type
- journal article
- Language
- en
- Open access
- bronze
- Licence
- www.elsevier.com/tdm/userlicense/1.0/ (tdm)
- www.elsevier.com/open-access/userlicense/1.0/ (vor, 6324 d)
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