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Approximate Reasoning Using Anytime Algorithms

  • Shlomo Zilberstein
  • Stuart Russell
Chapter
Part of the The Springer International Series in Engineering and Computer Science book series (SECS, volume 318)

Abstract

The complexity of reasoning in intelligent systems makes it undesirable, and sometimes infeasible, to find the optimal action in every situation since the deliberation process itself degrades the performance of the system. The problem is then to construct intelligent systems that react to a situation after performing the “right” amount of thinking. It is by now widely accepted that a successful system must trade off decision quality against the computational requirements of decision-making. Anytime algorithms, introduced by Dean, Horvitz and others in the late 1980’s, were designed to offer such a trade-off. We have extended their work to the construction of complex systems that are composed of anytime algorithms. This paper describes the compilation and monitoring mechanisms that are required to build intelligent systems that can efficiently control their deliberation time. We present theoretical results showing that the compilation and monitoring problems are tractable in a wide range of cases, and provide two applications to illustrate the ideas.

Keywords

Time Allocation Performance Profile Output Quality Approximate Reasoning Input Quality 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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Copyright information

© Kluwer Academic Publishers 1995

Authors and Affiliations

  • Shlomo Zilberstein
    • 1
  • Stuart Russell
    • 2
  1. 1.Department of Computer ScienceUniversity of MassachusettsAmherst
  2. 2.Department of EECS, Computer Science DivisionUniversity of CaliforniaBerkeley

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