Measuring complexity of intelligent machines

Author

Lima, Pedro ; Saridis, George

Author_Institution

Dept. of Electr. Comput. & Syst. Eng., Rensselaer Polytech. Inst., Troy, NY, USA

fYear

1993

fDate

2-6 May 1993

Firstpage

917

Abstract

A formalism that combines reliability and complexity as performance measures for intelligent machines is introduced. For a given desired reliability, different algorithms may be available which are reliable enough. Hence, it is important to have a means of choosing the algorithm of least cost among the reliable ones. Cost refers not only to CPU time, but also to other features, such as memory space. Information-based complexity provides a solid formalism for dealing with different sources of information and thus distinct algorithms at all levels of the machine. A case study related to image processing illustrates the method

Keywords

artificial intelligence; computational complexity; image processing; performance evaluation; reliability; CPU time; artificial intelligence; image processing; information based complexity; intelligent machines; memory space; performance measures; reliability; Cost function; Electric variables measurement; Image analysis; Image processing; Information resources; Machine intelligence; Path planning; Reliability engineering; Solids; Systems engineering and theory;

fLanguage

English

Publisher

ieee

Conference_Titel

Robotics and Automation, 1993. Proceedings., 1993 IEEE International Conference on

Conference_Location

Atlanta, GA

Print_ISBN

0-8186-3450-2

Type

conf

DOI

10.1109/ROBOT.1993.292093

Filename

292093