Was Fixing This Really That Hard? On the Complexity of Correcting HTN Domains

Abstract

Automated modeling assistance is indispensable to the AI planning being deployed in practice, notably in industry and other non-academic contexts. Yet, little progress has been made that goes beyond smart interfaces like programming environments. They focus on autocompletion, but lack intelligent support for guiding the modeler. As a theoretical foundation of a first step towards this direction, we study the computational complexity of correcting a flawed Hierarchical Task Network (HTN) planning domain. Specifically, a modeler provides a (white) list of plans that are supposed to be solutions, and likewise a (black) list of plans that shall not be solutions. We investigate the complexity of finding a set of (optimal or suboptimal) model corrections so that those plans are (resp. not) solutions to the corrected model. More specifically, we factor out each hardness source that contributes towards NP-hardness, including one that we deem important for many other complexity investigations that go beyond our specific context of application. All complexities range between NP and Sigma-2-p, rising the hope for efficient practical tools in the future.

Accelerating SAT-Based HTN Plan Verification by Exploiting Data Structures from HTN Planning

“…The plan verification problem is the task of deciding whether a given plan is a solution to a planning problem. Research on this problem has drawn increasing attention in the last few years due to its potential usages in numerous applications, e.g., in mixed initiative planning [6,22], in validating planning domains [19,21,20] where failed plan verification indicates flaws in planning domains, and in International Planning Competition. Recently, there were also attempts to exploit plan verification to solve planning problems [14].…”