Uncertainty in Artificial Intelligence
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Handling Uncertain Information: A Review of Numeric and Non-numeric Methods
Raj Bhatnagar, Laveen Kanal
Problem solving and decision making by humans is often done in environments where information concerning the problem is partial or approximate. AI researchers have been attempting to emulate this capability in computer expert systems. Most of the methods used to-date lack a theoretical foundation. Some theories for handling uncertainty of information have been proposed in the recent past. In this paper, we critically review these theories. The main theories that we examine are: Probability Theory, Shafer's Evidence Theory, Zadeh's Possibility Theory, Cohen's Theory of Endorsements and the non-monotonic logics. We describe these in terms of the representation of uncertain information, and combination of bodies of information and inferencing with such information, and consider the strong and weak aspects of each theory.
Keywords: Numeric Methods, Non-numeric Methods, Handling Uncertain Information
Pages: 3-26
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PDF Link: /papers/85/p3-bhatnagar.pdf
AUTHOR = "Raj Bhatnagar and Laveen Kanal",
TITLE = "Handling Uncertain Information: A Review of Numeric and Non-numeric Methods",
BOOKTITLE = "Uncertainty in Artificial Intelligence Annual Conference on Uncertainty in Artificial Intelligence (UAI-85)",
PUBLISHER = "Elsevier Science",
ADDRESS = "Amsterdam, NL",
YEAR = "1985",
PAGES = "3--26"

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