Decision making in context
Journal Article
·
· IEEE Trans. Pattern Anal. Mach. Intell.; (United States)
From a Bayesian decision theoretic framework, the author shows that the reason why the usual statistical approaches do not take context into account is because of the assumptions made on the joint prior probability function and because of the simplistic loss function chosen. He illustrates how the constraints sometimes employed by artificial intelligence researchers constitute a different kind of assumption on the joint prior probability function. He discusses a couple of loss functions which do take context into account and when combined with the joint prior probability constraint create a decision problem requiring a combinatorial state space search. He also gives a theory for how probabilistic relaxation works from a Bayesian point of view. 32 references.
- Research Organization:
- Virginia Polytechnical Institute and State Univ., Blacksburg
- OSTI ID:
- 5210321
- Journal Information:
- IEEE Trans. Pattern Anal. Mach. Intell.; (United States), Journal Name: IEEE Trans. Pattern Anal. Mach. Intell.; (United States) Vol. 4; ISSN ITPID
- Country of Publication:
- United States
- Language:
- English
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