Computational complexity of planning and approximate planning in the presence of incompleteness
Artificial Intelligence
Communications of the ACM
Formalizing sensing actions—a transition function based approach
Artificial Intelligence
An Execution-Backtracking Approach to Debugging
IEEE Software
Polynomial-Length Planning Spans the Polynomial Hierarchy
JELIA '02 Proceedings of the European Conference on Logics in Artificial Intelligence
Mobile Agents and Logic Programming
MA '02 Proceedings of the 6th International Conference on Mobile Agents
A logic programming approach to knowledge-state planning: Semantics and complexity
ACM Transactions on Computational Logic (TOCL)
Intelligent execution monitoring in dynamic environments
Fundamenta Informaticae
Artificial Intelligence - Special issue on logical formalizations and commonsense reasoning
A survey on knowledge compilation
AI Communications
Constructing conditional plans by a theorem-prover
Journal of Artificial Intelligence Research
A new HTN planning framework for agents in dynamic environments
CLIMA IV'04 Proceedings of the 4th international conference on Computational Logic in Multi-Agent Systems
A Logic-Based Approach to Finding Explanations for Discrepancies in Optimistic Plan Execution
Fundamenta Informaticae
A Logic-Based Approach to Finding Explanations for Discrepancies in Optimistic Plan Execution
Fundamenta Informaticae
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Reversing actions is the following problem: After executing a sequence of actions, which sequence of actions brings the agent back to the state just before this execution (an action reversal). Notably, this problem is different from a vanilla planning problem since the state we have to get back to is in general unknown. It emerges, for example, if an agent needs to find out which action sequences are undoable, and which ones are committed choices. It has applications related to plan execution and monitoring in nondeterministic domains, such as recovering from a failed execution by partially undoing the plan, dynamically switching from one executed plan to another, or restarting plans. We formalize action reversal in a logic-based action framework and characterize its computational complexity. Since unsurprisingly, the problem is intractable in general, we present a knowledge compilation approach that constructs offline a reverse plan library for efficient (in some cases, linear time) on-line computation of action reversals. Our results for the generic framework can be easily applied for expressive action languages such as C+ or κ.