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Searching with uncertainty extended abstract

  • Ricardo A. Baeza-Yates
  • Joseph C. Culberson
  • Gregory J. E. Rawlins
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 318)

Abstract

In this paper we initiate a new area of study dealing with the best way to search a possibly unbounded region for an object. The model for our search procedures is that we must pay costs proportional to the distance of the next probe position relative to our current position. This model is meant to give a realistic cost measure for a robot moving in the plane. Also, we examine the effect of decreasing the amount of a priori information given to a class of search problems.

Problems in this class are very simple analogues of non-trivial problems on searching with bounded error, searching an unbounded region, processing digitized images, robot navigation, and optimization.

We show that for some simple search problems, the relative information of knowing the general direction of the goal is much higher than knowing the distance to the goal.

Keywords

Probe Position Lower Order Term Logarithmic Spiral Simple Analogue Optimal Curve 
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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References

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Copyright information

© Springer-Verlag Berlin Heidelberg 1988

Authors and Affiliations

  • Ricardo A. Baeza-Yates
    • 1
  • Joseph C. Culberson
    • 2
  • Gregory J. E. Rawlins
    • 3
  1. 1.Department of Computer ScienceUniversity of WaterlooWaterlooCanada
  2. 2.Department of Computing ScienceUniversity of AlbertaEdmontonCanada
  3. 3.Department of Computer ScienceIndiana UniversityBloomingtonUSA

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