Computing Parameter Domains as an Aid to Planning

Proceedings Of The Third Artificial Intelligence Planning Systems Conference

Volume

Proceedings Of The Third Artificial Intelligence Planning Systems Conference

Contents

We show that by inferring parameter domains of planning operators, given the definitions of the operators and the initial and goal conditions, we can often speed up the planning process. We infer parameter domains by a polynomial-time algorithm that uses forward propagation of sets of constants occurring in the initial conditions and in operator postconditions. During planning parameter domains can be used to prune operator instances whose parameter domains are inconsistent with binding constraints, and to eliminate spurious "clobbering threats" that cannot, in fact, be realized without violating domain constraints. We illustrate these applications with examples from the UCPOP test suite and from the Rochester TRAINS transportation planning domain.