Probabilistic Inference and Non-Monotonic Inference
ROCHESTER UNIV NY DEPT OF PHILOSOPHY
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Since the appearance of the influential article by McCarthy and Hays, few people have tried to use probabilities as a basis for non-monotonic inference. One reason, perhaps the main one, is that probabilistic inference easily yields inconsistent bodies of knowledge, as is revealed by the lottery paradox. Here we establish three things First that standard systems of non- monotonic reasoning default logic, non-monotonic logic, and circumscription fall prey to the same lottery-like difficulties as does probabilistic inference. Second, that probabilistic inference provides equally plausible treatment of the standard examples of non-monotonic reasoning. Third, that the inconsistency threatened by the lottery paradox is a petty hodgoblin, and need not in any way interfere with the use of beliefs in planning and design.
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