The computational complexity of propositional STRIPS planning
Artificial Intelligence
Reasoning about knowledge
Approximate reasoning about actions in presence of sensing and incomplete information
ILPS '97 Proceedings of the 1997 international symposium on Logic programming
AAAI '98/IAAI '98 Proceedings of the fifteenth national/tenth conference on Artificial intelligence/Innovative applications of artificial intelligence
Computational complexity of planning and approximate planning in the presence of incompleteness
Artificial Intelligence
Polynomial-Length Planning Spans the Polynomial Hierarchy
JELIA '02 Proceedings of the European Conference on Logics in Artificial Intelligence
Some Results on the Complexity of Planning with Incomplete Information
ECP '99 Proceedings of the 5th European Conference on Planning: Recent Advances in AI Planning
Conformant planning via symbolic model checking and heuristic search
Artificial Intelligence
Conformant planning for domains with constraints: a new approach
AAAI'05 Proceedings of the 20th national conference on Artificial intelligence - Volume 3
The FF planning system: fast plan generation through heuristic search
Journal of Artificial Intelligence Research
Fast and Informed Action Selection for Planning with Sensing
Current Topics in Artificial Intelligence
Extending Classical Planning to the Multi-agent Case: A Game-Theoretic Approach
ECSQARU '07 Proceedings of the 9th European Conference on Symbolic and Quantitative Approaches to Reasoning with Uncertainty
PADL '09 Proceedings of the 11th International Symposium on Practical Aspects of Declarative Languages
Compiling uncertainty away in conformant planning problems with bounded width
Journal of Artificial Intelligence Research
Complexity of planning in action formalisms based on description logics
LPAR'07 Proceedings of the 14th international conference on Logic for programming, artificial intelligence and reasoning
Compiling Uncertainty Away in Non-Deterministic Conformant Planning
Proceedings of the 2010 conference on ECAI 2010: 19th European Conference on Artificial Intelligence
Approximation of action theories and its application to conformant planning
Artificial Intelligence
State agnostic planning graphs: deterministic, non-deterministic, and probabilistic planning
Artificial Intelligence
Narrative planning: compilations to classical planning
Journal of Artificial Intelligence Research
A conformant planner based on approximation: CpA(H)
ACM Transactions on Intelligent Systems and Technology (TIST) - Special section on agent communication, trust in multiagent systems, intelligent tutoring and coaching systems
Temporal deontic action logic for the verification of compliance to norms in ASP
Proceedings of the Fourteenth International Conference on Artificial Intelligence and Law
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Even under polynomial restrictions on plan length, conformant planning remains a very hard computational problem as plan verification itself can take exponential time. This heavy price cannot be avoided in general although in many cases conformant plans are verifiable efficiently by means of simple forms of disjunctive inference. This raises the question of whether it is possible to identify and use such forms of inference for developing an efficient but incomplete planner capable of solving non-trivial problems quickly. In this work, we show that this is possible by mapping conformant into classical problems that are then solved by an off-the-shelf classical planner. The formulation is sound as the classical plans obtained are all conformant, but it is incomplete as the inverse relation does not always hold. The translation accommodates 'reasoning by cases' by means of an 'split-protect-and-merge' strategy; namely, atoms L/Xi that represent conditional beliefs 'if Xi then L' are introduced in the classical encoding, that are combined by suitable actions to yield the literal L when the disjunction X1 ∨ ... ∨ Xn holds and certain invariants in the plan are verified. Empirical results over a wide variety of problems illustrate the power of the approach.