Uncertainty in Artificial Intelligence
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The Application of Dempster Shafer Theory to a Logic-Based Visual Recognition System
Gregory Provan
Abstract:
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.
Keywords: null
Pages: 389-405
PS Link:
PDF Link: /papers/89/p389-provan.pdf
BibTex:
@INPROCEEDINGS{Provan89,
AUTHOR = "Gregory Provan ",
TITLE = "The Application of Dempster Shafer Theory to a Logic-Based Visual Recognition System",
BOOKTITLE = "Uncertainty in Artificial Intelligence 5 Annual Conference on Uncertainty in Artificial Intelligence (UAI-89)",
PUBLISHER = "Elsevier Science",
ADDRESS = "Amsterdam, NL",
YEAR = "1989",
PAGES = "389--405"
}


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