Uncertainty in Artificial Intelligence
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A Logical Interpretation of Dempster-Shafer Theory, with Application to Visual Recognition
Gregory Provan
We formulate Dempster Shafer Belief functions in terms of Propositional Logic using the implicit notion of provability underlying Dempster Shafer Theory. Given a set of propositional clauses, assigning weights to certain propositional literals enables the Belief functions to be explicitly computed using Network Reliability techniques. Also, the logical procedure corresponding to updating Belief functions using Dempster's Rule of Combination is shown. This analysis formalizes the implementation of Belief functions within an Assumption-based Truth Maintenance System (ATMS). We describe the extension of an ATMS-based visual recognition system, VICTORS, with this logical formulation of Dempster Shafer theory. Without Dempster Shafer theory, VICTORS computes all possible visual interpretations (i.e. all logical models) without determining the best interpretation(s). Incorporating Dempster Shafer theory enables optimal visual interpretations to be computed and a logical semantics to be maintained.
Pages: 287-294
PS Link:
PDF Link: /papers/89/p287-provan.pdf
AUTHOR = "Gregory Provan ",
TITLE = "A Logical Interpretation of Dempster-Shafer Theory, with Application to Visual Recognition",
BOOKTITLE = "Proceedings of the Fifth Conference Annual Conference on Uncertainty in Artificial Intelligence (UAI-89)",
ADDRESS = "Corvallis, Oregon",
YEAR = "1989",
PAGES = "287--294"

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