Final version : uncertainty in artificial intelligence

Reasoning with uncertain information has received a great deal of attention recently, as this issue has to be addressed when developing many expert systems.In this thesis we study the literature of uncertainty in AI. The approaches taken by the researchers in this field can be classified into two categories: non-numeric approaches and numeric approaches. From non-numeric methods, we summarize The Theory of Endorsements, and non-monotonic logics. From numeric methods, we elaborate on MYCIN certainty Factors, Dempster-Shafer Theory, Fuzzy Logic, and Probabilistic Approach. We point out that probability theory is an adequate approach if we interpret probability values as beliefs and not only as frequencies.We first discuss broad and more thoroughly researched areas. We then focus more on integrating probability and logic as we believe this is a crucial approach to build up a setting for reasoning with uncertain information based on strong local foundations. Some key works in that area are traced back to 1913 when Lukasiewics published his paper on Logical Foundation of Probability. Comparisons between Nilsson's probabilistic logic and the related work of Quinlan, Grosof, McLeish, Chen, and Bacchus are given. We conclude the thesis by our remarks and suggestions for possible future research topics