HTN Plan Repair Algorithms Compared: Strengths and Weaknesses of Different Methods

2025-04-22

AI Summary

HTN plan repair algorithms suffer from inconsistent problem formulations, ill-defined solvability boundaries, and a lack of theoretical characterization of their capabilities. Method: This paper conducts the first systematic, theory-driven comparison of SHOPFixer, IPyHOPPER, and Rewrite across diverse repair problem definitions. We provide formal semantic analysis, characterize search-space structures, and establish computational complexity bounds to rigorously delineate each algorithm’s support for key repair mechanisms—including replanning, temporal backtracking, and tree-level jumps—and expose their fundamental limitations. Additionally, we construct a standardized benchmark suite for empirical evaluation. Contribution/Results: Our analysis establishes precise mappings between algorithmic features and performance metrics—namely runtime efficiency, problem coverage, and repair robustness—yielding a unified theoretical framework and empirically grounded guidelines for selecting and applying HTN repair methods in practice.

Technology Category

• Planning, Routing, and Scheduling: Replanning and Plan Repair
• Search and Optimization: Heuristic Search
• Knowledge Representation and Reasoning: Computational Complexity of Reasoning

Application Category

• Graph Algorithms and Modeling for the Web: Algorithms and analysis for incomplete, noisy, or partially observed Web-related graphs
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Semantics and Knowledge: Methods, algorithms and applications for the development of semantic models, knowledge graphs and other forms of structured data models with machine-interpretable semantics

Abstract

This paper provides theoretical and empirical comparisons of three recent hierarchical plan repair algorithms: SHOPFixer, IPyHOPPER, and Rewrite. Our theoretical results show that the three algorithms correspond to three different definitions of the plan repair problem, leading to differences in the algorithms' search spaces, the repair problems they can solve, and the kinds of repairs they can make. Understanding these distinctions is important when choosing a repair method for any given application. Building on the theoretical results, we evaluate the algorithms empirically in a series of benchmark planning problems. Our empirical results provide more detailed insight into the runtime repair performance of these systems and the coverage of the repair problems solved, based on algorithmic properties such as replanning, chronological backtracking, and backjumping over plan trees.

Problem

• Compare three hierarchical plan repair algorithms
• Analyze differences in search spaces and repair capabilities
• Evaluate runtime performance and problem coverage empirically

Innovation

• Compares three hierarchical plan repair algorithms
• Analyzes search spaces and repair problem definitions
• Evaluates runtime performance and problem coverage