The Complexity of Planning Problems With Simple Causal Graphs

Abstract

We present three new complexity results for classes of planning problems with simple causal graphs. First, we describe a polynomial-time algorithm that uses macros to generate plans for the class 3S of planning problems with binary state variables and acyclic causal graphs. This implies that plan generation may be tractable even when a planning problem has an exponentially long minimal solution. We also prove that the problem of plan existence for planning problems with multi-valued variables and chain causal graphs is NP-hard. Finally, we show that plan existence for planning problems with binary state variables and polytree causal graphs is NP-complete.

References in corpus (2)

• Macro-FF: Improving AI Planning with Automatically Learned Macro-Operators
• Structure and Complexity in Planning with Unary Operators

Cited by in corpus (5)

• Analyzing Search Topology Without Running Any Search: On the Connection Between Causal Graphs and h+
• The Role of Macros in Tractable Planning
• Algorithms and Limits for Compact Plan Representations
• Planning over Chain Causal Graphs for Variables with Domains of Size 5 Is NP-Hard
• A Refined View of Causal Graphs and Component Sizes: SP-Closed Graph Classes and Beyond