Measuring the likely effectiveness of strategies

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 1138, Issue , 1996, Pages 191-196

  Dupée, Brian J ^a  

^a UNIVERSITY OF BATH   (United Kingdom)

Author keywords

[No Author keywords available]

Indexed keywords

ARTIFICIAL INTELLIGENCE; EXPERT SYSTEMS; UNCERTAINTY ANALYSIS;

DEMPSTER-SHAFER THEORY; MATHEMATICAL PROBLEMS; QUANTITATIVE MEASURES; THEORY OF BELIEF FUNCTIONS;

COMPUTATION THEORY;

EID: 84947914637     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/3-540-61732-9_58     Document Type: Conference Paper

References (12)

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3. Gordon, J., Shortliffe, E.H.: The Dempster-Shafer Theory of Evidence and its Relevance to Expert Systems. In: Buchanen & Shortliffe (1984). 1983.
4. Gordon, J., Shortliffe, E.H.: A Method of Managing Evidential Reasoning in a Hierarchical Hypothesis Space. Artificial Intelligence, 26(3), 323-357. 1985.
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7. Lucks, M., Gladwell, I.: Automated Selection of Mathematical Software. ACM Transactions on Mathematical Software, 18(1), 11-34. Also published in Houstis et al. (1992, pp 421-459) as ‘A Functional Representation for Software Selection Expertise’. 1992.
8. NAG.: Fortran Library Manual - Mark 16. NAG Ltd, Oxford, UK. NAG Publication Code NP2478. 1993.
9. Pearl, J.: On Evidential Reasoning in a Hierarchy of Hypotheses. Artificial Intelligence, 28(1), 9-15. 1986
10. Pearl, J.: Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference. San Mateo, Calif.: Morgan Kaufmann. 1988.
11. Shafer, G.: A Mathematical Theory of Evidence. Princeton, NJ: Princeton University Press. 1976.
12. Shafer, G., Logan, R.: Implementing Dempster’s Rule for Hierarchical Evidence. Artificial Intelligence, 33, 271-298. 1987.