Characterizing and Reasoning about Probabilistic and Non Probabilistic Expectation

Abstract

Expectation is a central notion in probability theory. The notion of expectation also makes sense for other notions of uncertainty. We introduce a propositional logic for reasoning about expectation, where the semantics depends on the underlying representation of uncertainty. We give sound and complete axiomatizations for the logic in the case that the underlying representation is(a) probability, (b) sets of probability measures, (c) belief functions, and (d) possibility measures. We show that this logic is more expressive than the corresponding logic for reasoning about likelihood in the case of sets of probability measures, but equi-expressive in the case of probability, belief, and possibility. Finally, we show that satisfiability for these logics is NP-complete, no harder than satisfiability for propositional logic.

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Document Details

Document Type
Technical Report
Publication Date
May 01, 2007
Accession Number
AD1020514

Entities

People

  • Joseph Halpern
  • Riccardo Pucella

Organizations

  • Cornell University

Tags

Communities of Interest

  • Biomedical
  • Human Systems

DTIC Thesaurus Topics

  • Applied Mathematics
  • Artificial Intelligence
  • Convex Sets
  • Information Science
  • Language
  • Mathematical Analysis
  • Mathematical Logic
  • Mathematics
  • Numbers
  • Numerical Analysis
  • Probability
  • Random Variables
  • Rational Numbers
  • Real Numbers
  • Reasoning
  • Statistics
  • Theorems

Fields of Study

  • Computer science

Readers

  • Artificial Intelligence
  • Mathematical Modeling and Probability Theory.