Browse All

Theses & Dissertations

Submissions

  • Submissions (Articles, Chapters, and other finished products)

Searching in an Unknown Environment: An Optimal Randomized Algorithm for the Cow-Path Problem

UNCG Author/Contributor (non-UNCG co-authors, if there are any, appear on document)
Stephen R. Tate, Professor and Department Head (Creator)
Institution
The University of North Carolina at Greensboro (UNCG )
Web Site: http://library.uncg.edu/

Abstract: Searching for a goal is a central and extensively studied problem in computer science. In classical searching problems, the cost of a search function is simply the number of queries made to an oracle that knows the position of the goal. In many robotics problems, as well as in problems from other areas, we want to charge a cost proportional to the distance between queries (e.g., the time required to travel between two query points). With this cost function in mind, the abstract problem known as the w-lane cow-path problem was designed. There are known optimal deterministic algorithms for the cow-path problem; we give the first randomized algorithm in this paper. We show that our algorithm is optimal for two paths (w = 2) and give evidence that it is optimal for larger values of w. Subsequent to the preliminary version of this paper, Kao et al. (in “Proceedings, 5th ACM–SIAM Symposium on Discrete Algorithm," pp. 372-381, 1994) have shown that our algorithm is indeed optimal for all w = 2. Our randomized algorithm gives expected performance that is almost twice as good as is possible with a deterministic algorithm. For the performance of our algorithm, we also derive the asymptotic growth with respect to w—despite similar complexity results for related problems, it appears that this growth has never been analyzed.

Additional Information

Publication
Information and Computation, Vol. 133, No. 1, 1996, pp. 63–80.
Language: English
Date: 1996
Keywords
Searching, Cow-path problem, Algorithms, Randomization