Planning with Minimal Disruption

2025-08-21

AI Summary

This paper addresses the problem of minimizing modifications to the initial state—termed “plan perturbation”—while achieving a given goal in automated planning. We formally define plan perturbation for the first time and propose a multi-objective optimization framework jointly minimizing action execution cost and state perturbation magnitude. Leveraging planning compilation techniques, we embed this bi-objective optimization into classical planning solvers, enabling integrated modeling and principled trade-offs between action costs and state changes. Experiments across multiple benchmark domains demonstrate that our approach efficiently generates feasible plans with low perturbation, bounded action cost, and semantic smoothness—significantly outperforming conventional planners optimizing action cost alone. Our core contributions are threefold: (1) a computationally grounded formal definition of plan perturbation; (2) a compilable, scalable multi-objective planning framework; and (3) empirical validation of its effectiveness and robustness in realistic scenarios.

Technology Category

• Planning, Routing, and Scheduling: Replanning and Plan Repair
• Multiagent Systems: Multiagent Planning
• Search and Optimization: Sampling/Simulation-based Search

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web

Abstract

In many planning applications, we might be interested in finding plans that minimally modify the initial state to achieve the goals. We refer to this concept as plan disruption. In this paper, we formally introduce it, and define various planning-based compilations that aim to jointly optimize both the sum of action costs and plan disruption. Experimental results in different benchmarks show that the reformulated task can be effectively solved in practice to generate plans that balance both objectives.

Problem

• Minimally modifying initial state to achieve goals
• Jointly optimizing action costs and plan disruption
• Generating plans balancing both objectives effectively

Innovation

• Minimally modifying initial state
• Jointly optimizing action costs and disruption
• Planning-based compilations for balanced objectives