A survey categorizes existing work on counterfactual reasoning in automated planning by changed elements, timing of reasoning, reasons for changes, and methods used.
Planning Task Shielding: Detecting and Repairing Flaws in Planning Tasks through Turning them Unsolvable
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abstract
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.
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cs.AI 1years
2026 1verdicts
UNVERDICTED 1representative citing papers
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Counterfactual Reasoning in Automated Planning
A survey categorizes existing work on counterfactual reasoning in automated planning by changed elements, timing of reasoning, reasons for changes, and methods used.