Probabilistic Inference and Non-Monotonic Inference

Abstract

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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Document Details

Document Type
Technical Report
Publication Date
Jan 01, 1989
Accession Number
ADA250603

Entities

People

  • Henry E. Kyburg Jr.

Organizations

  • University of Rochester

Tags

DTIC Thesaurus Topics

  • Applied Computer Science
  • Artificial Intelligence
  • Computations
  • Computer Science
  • Data Fusion
  • Expert Systems
  • Language
  • Mathematics
  • Measurement
  • New York
  • Probability
  • Probability Distributions
  • Reasoning
  • Security
  • Standards
  • United States

Fields of Study

  • Philosophy

Readers

  • Artificial Intelligence
  • Educational Psychology

Technology Areas

  • AI & ML