Enhancing Constraint Models of Planning Problems by Common Subexpression Elimination

Constraint Programming is an attractive approach for solving AI planning problems by modelling them as Constraint Satisfaction Problems (CSPs). However, formulating effective constraint models of complex planning problems is challenging, and CSPs resulting from standard approaches often require further enhancement to perform well. Common subexpression elimination is a general technique for improving constraint models, which has been shown to lead to a great reduction in instance size, solving time and search space. This paper argues that models of AI planning problems are particularly amenable to this approach. We present four case studies to substantiate this argument, three of which include novel constraint models of AI planning problems. In each, we describe the constraint model, highlight the sources of common subexpressions, and present an empirical analysis of the effect of eliminating common subexpressions.