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