Structure and Complexity in Planning with Unary Operators

TL;DR

This paper investigates how the structure of causal graphs in unary operator planning domains affects computational complexity, providing algorithms for some cases and hardness results for others.

Contribution

It offers a comprehensive analysis linking causal graph structures to planning complexity, including polynomial algorithms and hardness proofs.

Findings

Polynomial-time plan generation for domains with polytree causal graphs with bounded indegree.

Plan existence is hard for domains with directed-path singly connected DAGs.

Number of paths in the causal graph correlates with planning complexity.

Abstract

Unary operator domains -- i.e., domains in which operators have a single effect -- arise naturally in many control problems. In its most general form, the problem of STRIPS planning in unary operator domains is known to be as hard as the general STRIPS planning problem -- both are PSPACE-complete. However, unary operator domains induce a natural structure, called the domain's causal graph. This graph relates between the preconditions and effect of each domain operator. Causal graphs were exploited by Williams and Nayak in order to analyze plan generation for one of the controllers in NASA's Deep-Space One spacecraft. There, they utilized the fact that when this graph is acyclic, a serialization ordering over any subgoal can be obtained quickly. In this paper we conduct a comprehensive study of the relationship between the structure of a domain's causal graph and the complexity of planning in this domain. On the positive side, we show that a non-trivial polynomial time plan generation algorithm exists for domains whose causal graph induces a polytree with a constant bound on its node indegree. On the negative side, we show that even plan existence is hard when the graph is a directed-path singly connected DAG. More generally, we show that the number of paths in the causal graph is closely related to the complexity of planning in the associated domain. Finally we relate our results to the question of complexity of planning with serializable subgoals.