Explanation Closure, Action Closure, and the Sandewall Test Suite for Reasoning about Change

Abstract

Explanation closure (EC) axioms were previously introduced as a means of solving the frame problem. This paper provides a thorough demonstration of the power of EC (combined with action closure) for reasoning about dynamic worlds, by way of Sandewall's recently compiled test suite of 12-or-so problems. The problems range from the Yale turkey shoot (and variants) to the stuffy room problem, and were intended as a test and challenge for nonmonotonic logics of action. The EC/AC-based solutions for the most part do not resort to nonmonotonic reasoning at all, yet fare much better in providing the intuitively warranted inferences than the best-known nonmonotonic approaches. While there are good reasons for ultimately employing nonmonotonic or probabilistic logics -- e.g., pervasive uncertainty and the qualification problem -- this does show that the scope of monotonic methods has been underestimated. Subsidiary purposes of the paper are to clarify the intuitive status of EC axioms in relation to action effect axioms, and to show how EC, previously formulated within the situation calculus, can be applied within the framework of a temporal logic similar to Sandewall's discrete fluent logic, with some gains in clarity.

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Document Details

Document Type
Technical Report
Publication Date
Oct 01, 1992
Accession Number
ADA260497

Entities

People

  • Lenhart K. Schubert

Organizations

  • University of Rochester

Tags

Communities of Interest

  • Autonomy
  • Energy and Power Technologies

DTIC Thesaurus Topics

  • Air Force Research Laboratories
  • Artificial Intelligence
  • Calculus
  • Catalogs
  • Causal Reasoning
  • Computer Science
  • Computers
  • Information Systems
  • Military Research
  • Notation
  • Psychological Phenomena And Processes
  • Psychology
  • Qualifications
  • Reasoning
  • Reliability
  • Semantics
  • Standards

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  • Mathematical Modeling and Probability Theory.
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Technology Areas

  • AI & ML
  • AI & ML - Neural Networks