Integer programs and valid inequalities for planning problems

Abstract

Part of the recent work in AI planning is concerned with the development of algorithms that regard planning as a combinato-rial search problem. The underlying representation language is basically propositional logic. While this is adequate for many domains, it is not clear if it remains so for problems that involve numerical constraints, or optimization of complex objective functions. Moreover, the propositional representation imposes restrictions on the domain knowledge that can be utilized by these approaches. In order to address these issues, we propose moving to the more expressive language of Integer Programming (IP). We show how capacity constraints can be easily encoded into linear 0-1 inequalities and how rich forms of domain knowledge can be compac-tly represented and computationally exploited by IP solvers. Then we introduce a novel heuristic search method based on the linear program-ming relaxation. Finally, we present the results of our experiments with a classical relaxation-based IP solver and a logic-based 0-1 optimizer. © 2000 Springer-Verlag Berlin Heidelberg.