Computational Complexity of Planning Based on Partial Information about the System's Present and Past States

Authors

Chitta Baral
Le Chi Tuan
Raul A. Trejo
Vladik Kreinovich, The University of Texas at El PasoFollow

Publication Date

5-2000

Comments

UTEP-CS-00-13a.

Published in: J. Lloyd et al. (eds.), Proceedings of the First International Conference on Computational Logic CL'2000, London, July 24-28, 2000, Springer Lecture Notes in Artificial Intelligence, Vol. 1861, pp. 882-896.

Abstract

Planning is a very important AI problem, and it is also a very time-consuming AI problem. To get an idea of how complex different planning problems are, it is useful to describe the computational complexity of different general planning problems. This complexity has been described for problems in which planning is based on the (complete or partial) information about the current state of the system. In real-life planning, in addition to this information, we often also use the knowledge about the system's past behavior. To describe such more realistic planning situations, a special language L was developed in 1997 by C. Baral, M. Gelfond and A. Provetti. In this paper, we expand the known results about computational complexity of planning (including our own previous results about complexity of planning in the planning language A) to this more general class of planning problems.