Planning with Minimal Disruption
This work addresses planning applications where minimizing changes to the initial state is important, though it appears incremental as it builds on existing planning frameworks.
The paper tackles the problem of generating plans that minimally modify the initial state to achieve goals, introducing the concept of plan disruption and defining compilations to optimize both action costs and disruption. Experimental results demonstrate that the reformulated task can be effectively solved to produce balanced plans.
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.