Common Subexpressions in Constraint Models of Planning Problems

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 computationally cheap and  general technique for improving CSPs, which can lead to a great reduction in instance size, solving time and search space. In this work we identify general causes of common subexpressions from three modelling techniques often used to encode planning problems into   constraints. We present four case studies of 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 effects of eliminating common subexpressions.