Qualitative Probabilities: A Normative Framework for Commonsense Reasoning
Abstract
Intelligent agents are expected to generate plausible predictions and explanations in partially unknown and highly dynamic environments. Thus, they should be able to retract old conclusions in light of new evidence and to efficiently manage wide fluctuations of uncertainty. Neither mathematical logic nor numerical probability fully accommodates these requirements. In this dissertation I propose a formalism that facilitates reasoning with qualitative rules, facts, and deductively closed beliefs (as in logic), yet permits us to retract beliefs in response to changing contexts and imprecise observations (as in probability). Domain knowledge is encoded as if-then rules admitting exceptions with different degrees of abnormality, and queries specify contexts with different levels of precision. I develop effective procedures for testing the consistency of such knowledge bases and for computing whether (and to what degree) a given query is confirmed or denied. These procedures require a polynomial number of propositional satisfiability tests and hence are tractable for Horn expressions. Finally, I show how to give rules causal character by enforcing a Markovian condition of independence. The resulting formalism provides the necessary machinery for embodying belief updates and belief revision, generating explanations, and reasoning about actions and change.
Document Details
- Document Type
- Technical Report
- Publication Date
- Jan 01, 1992
- Accession Number
- ADA637681
Entities
People
- Moises Goldszmidt
Organizations
- University of California, Los Angeles