Planning Task Shielding: Detecting and Repairing Flaws in Planning Tasks through Turning them Unsolvable
This addresses the issue of ensuring safety in automated planning systems by preventing flawed states, though it is incremental as it builds on existing planning frameworks.
The paper tackles the problem of detecting and repairing flaws in planning tasks by introducing planning task shielding, which modifies actions to make tasks unsolvable when flawed states are identified, and proposes an optimal algorithm that achieves this with minimal modifications.
Most research in planning focuses on generating a plan to achieve a desired set of goals. However, a goal specification can also be used to encode a property that should never hold, allowing a planner to identify a trace that would reach a flawed state. In such cases, the objective may shift to modifying the planning task to ensure that the flawed state is never reached-in other words, to make the planning task unsolvable. In this paper we introduce planning task shielding: the problem of detecting and repairing flaws in planning tasks. We propose $allmin$, an optimal algorithm that solves these tasks by minimally modifying the original actions to render the planning task unsolvable. We empirically evaluate the performance of $allmin$ in shielding planning tasks of increasing size, showing how it can effectively shield the system by turning the planning task unsolvable.