NSA algorithm and its computational complexity-preliminary results

Author

Zhang, Weixiong

Author_Institution

Dept. of Comput. Sci., Old Dominion Univ., Norfolk, VA, USA

fYear

1989

fDate

23-25 Oct 1989

Firstpage

442

Lastpage

446

Abstract

To overcome the limitations of models of statistical heuristic searching the author proposes the idea of combining nonparametric statistical inference methods with the heuristic search. A nonparametric statistical algorithm (NSA) for heuristic searching is presented, and its computational complexity is discussed. It is shown that, in a uniform m-ary search tree G with a single goal S _N located at an unknown site in the N-th depth, NSA can find the goal asymptotically with probability one, and the complexity remains O(N(In N)²), where N is the length of the solution path

Keywords

computational complexity; heuristic programming; inference mechanisms; search problems; statistics; trees (mathematics); computational complexity; goal; nonparametric statistical algorithm; probability; search tree; solution path length; statistical heuristic searching; statistical inference methods; Algorithm design and analysis; Artificial intelligence; Computational complexity; Computer science; Cost function; Heuristic algorithms; Inference algorithms; Parametric statistics; Probability; Random variables;

fLanguage

English

Publisher

ieee

Conference_Titel

Tools for Artificial Intelligence, 1989. Architectures, Languages and Algorithms, IEEE International Workshop on

Conference_Location

Fairfax, VA

Print_ISBN

0-8186-1984-8

Type

conf

DOI

10.1109/TAI.1989.65352

Filename

65352