Philosophy Dictionary of ArgumentsHome
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| Decision theory: Decision theory is the study of how to make optimal decisions in the face of uncertainty. It is a branch of applied probability theory and analytic philosophy.
Decision theory uses mathematical models to represent decision problems and to identify the best decision to make in a given situation. These models take into account the different options that are available, the probabilities of different outcomes, and the value of different outcomes.
_____________Annotation: The above characterizations of concepts are neither definitions nor exhausting presentations of problems related to them. Instead, they are intended to give a short introduction to the contributions below. – Lexicon of Arguments. | |||
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AI Research on Decision Theory - Dictionary of Arguments
Norvig I 638 Decision theory/AI research/Norvig/Russell: Decision theory has been a standard tool in economics, finance, and management science since the 1950s. Until the 1980s, decision trees were the main tool used for representing simple decision problems. Smith (1988)(1) gives an overview of the methodology of decision analysis. Influence diagrams were introduced by Howard and Matheson (1984)(2), based on earlier work at SRI (Miller et al., 1976)(3). Howard and Matheson’s method involved the Norvig I 639 derivation of a decision tree from a decision network, but in general the tree is of exponential size. Shachter (1986)(4) developed a method for making decisions based directly on a decision network, without the creation of an intermediate decision tree. This algorithm was also one of the first to provide complete inference for multiply connected Bayesian networks. Zhang et al. (1994)(5) showed how to take advantage of conditional independence of information to reduce the size of trees in practice; they use the term decision network for networks that use this approach (although others use it as a synonym for influence diagram). Nilsson and Lauritzen (2000)(6) link algorithms for decision networks to ongoing developments in clustering algorithms for Bayesian networks. Koller and Milch (2003)(7) show how influence diagrams can be used to solve games that involve gathering information by opposing players, and Detwarasiti and Shachter (2005)(8) show how influence diagrams can be used as an aid to decision making for a team that shares goals but is unable to share all information perfectly. The collection by Oliver and Smith (1990)(9) has a number of useful articles on decision networks, as does the 1990 special issue of the journal Networks. >Decision networks/Norvig. Norvig I 639 Surprisingly few early AI researchers adopted decision-theoretic tools after the early applications in medical decision (…). One of the few exceptions was Jerry Feldman, who applied decision theory to problems in vision (Feldman and Yakimovsky, 1974)(10) and planning (Feldman and Sproull, 1977)(11). After the resurgence of interest in probabilistic methods in AI in the 1980s, decision-theoretic expert systems gained widespread acceptance (Horvitz et al., 1988(12); Cowell et al., 2002)(13). >Expert systems/Norvig. 1. Smith, J. Q. (1988). Decision Analysis. Chapman and Hall. 2. Howard, R. A. and Matheson, J. E. (1984). Influence diagrams. In Howard, R. A. and Matheson, J. E. (Eds.), Readings on the Principles and Applications of Decision Analysis, pp. 721–762. Strategic Decisions Group. 3. Miller, A. C., Merkhofer, M. M., Howard, R. A., Matheson, J. E., and Rice, T. R. (1976). Development of automated aids for decision analysis. Technical report, SRI International. 4. Shachter, R. D. (1986). Evaluating influence diagrams. Operations Research, 34, 871–882. 5. Zhang, N. L., Qi, R., and Poole, D. (1994). A computational theory of decision networks. IJAR, 11, 83–158. 6. Nilsson, D. and Lauritzen, S. (2000). Evaluating influence diagrams using LIMIDs. In UAI-00, pp. 436–445. 7. Koller, D. and Milch, B. (2003). Multi-agent influence diagrams for representing and solving games. Games and Economic Behavior, 45, 181–221. 8. Detwarasiti, A. and Shachter, R. D. (2005). Influence diagrams for team decision analysis. Decision Analysis, 2(4), 207–228. 9. Oliver, R. M. and Smith, J. Q. (Eds.). (1990). Influence Diagrams, Belief Nets and Decision Analysis. Wiley. 10. Feldman, J. and Yakimovsky, Y. (1974). Decision theory and artificial intelligence I: Semantics-based region analyzer. AIJ, 5(4), 349–371. 11. Feldman, J. and Sproull, R. F. (1977). Decision theory and artificial intelligence II: The hungry monkey. Technical report, Computer Science Department, University of Rochester. 12. Horvitz, E. J., Breese, J. S., and Henrion, M. (1988). Decision theory in expert systems and artificial intelligence. IJAR, 2, 247–302. 13. Cowell, R., Dawid, A. P., Lauritzen, S., and Spiegelhalter, D. J. (2002). Probabilistic Networks and Expert Systems. Springer._____________Explanation of symbols: Roman numerals indicate the source, arabic numerals indicate the page number. The corresponding books are indicated on the right hand side. ((s)…): Comment by the sender of the contribution. Translations: Dictionary of Arguments The note [Concept/Author], [Author1]Vs[Author2] or [Author]Vs[term] resp. "problem:"/"solution:", "old:"/"new:" and "thesis:" is an addition from the Dictionary of Arguments. If a German edition is specified, the page numbers refer to this edition. |
AI Research Norvig I Peter Norvig Stuart J. Russell Artificial Intelligence: A Modern Approach Upper Saddle River, NJ 2010 |
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Ed. Martin Schulz, access date 2024-04-20