Planning with Minimal Disruption
Summary: arXiv:2508.15358v2 Announce Type: replace
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.
Introduction
Effective planning is crucial across various domains, including robotics, logistics, and artificial intelligence. Traditionally, planners focus on finding a sequence of actions leading to a desired goal state. However, this often comes at the cost of significantly altering the initial state. The concept of plan disruption aims to address this challenge by emphasizing the need to maintain as much of the initial state as possible while still achieving the desired goals.
Defining Plan Disruption
Plan disruption can be understood as the degree to which a plan modifies the initial state. This concept is particularly important in scenarios where preserving the original conditions is desirable, such as in resource-constrained environments or when dealing with sensitive systems. The paper categorizes plan disruption into several key components:
- State Modification: The extent to which the initial state is altered.
- Action Costs: The costs associated with each action taken in the planning process.
- Goal Achievement: The successful attainment of the desired outcomes.
Joint Optimization Approach
The authors propose a joint optimization framework that seeks to minimize both action costs and plan disruption. By integrating these two objectives, planners can generate more efficient and practical solutions. The paper outlines several methodologies for achieving this optimization:
- Heuristic Search: Utilizing heuristic algorithms to explore potential plans and their associated costs.
- Linear Programming: Formulating the planning problem as a linear programming task to find optimal solutions.
- Monte Carlo Methods: Applying probabilistic techniques to evaluate a wide range of planning scenarios.
Experimental Results
The authors conducted extensive experiments across various benchmark scenarios to validate their proposed approach. The results demonstrated that the new planning formulations effectively balance action costs and plan disruption. Key findings include:
- Improved performance in benchmarks that require minimal state modifications.
- Significant reductions in overall action costs without compromising goal achievement.
- A clear advantage over traditional planning methods that do not consider plan disruption.
Conclusion
The introduction of plan disruption as a critical component of planning represents a significant advancement in the field of artificial intelligence. By focusing on both action costs and the preservation of the initial state, planners can develop more effective and realistic solutions. Future research may explore additional applications of this framework across various domains, further enhancing the capabilities of AI planning systems.