The computational complexity of propositional STRIPS planning
Artificial Intelligence
Complexity, decidability and undecidability results for domain-independent planning
Artificial Intelligence - Special volume on planning and scheduling
Approximate reasoning about actions in presence of sensing and incomplete information
ILPS '97 Proceedings of the 1997 international symposium on Logic programming
Formalizing narratives using nested circumscription
Artificial Intelligence
Fixed-Parameter Complexity in AI and Nonmonotonic Reasoning
LPNMR '99 Proceedings of the 5th International Conference on Logic Programming and Nonmonotonic Reasoning
The complexity of facets (and some facets of complexity)
STOC '82 Proceedings of the fourteenth annual ACM symposium on Theory of computing
The complexity of facets resolved
SFCS '85 Proceedings of the 26th Annual Symposium on Foundations of Computer Science
Constructing conditional plans by a theorem-prover
Journal of Artificial Intelligence Research
Computational complexity of planning and approximate planning in presence of incompleteness
IJCAI'99 Proceedings of the 16th international joint conference on Artificial intelligence - Volume 2
Probabilistic propositional planning: representations and complexity
AAAI'97/IAAI'97 Proceedings of the fourteenth national conference on artificial intelligence and ninth conference on Innovative applications of artificial intelligence
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Planning is a very important AI problem, and it is also a very time-consuming AI problem. To get an idea of how complex different planning problems are, it is useful to describe the computational complexity of different general planning problems. This complexity has been described for problems in which planning is based on the (complete or partial) information about the current state of the system. In real-life planning problems, we can often complement the incompleteness of our explicit knowledge about the current state by using the implicit knowledge about this state which is contained in the description of the system's past behavior. For example, the information about the system's past failures is very important in planning diagnostic and repair. To describe planning which can use the information about the past, a special language L was developed in 1997 by C. Baral, M. Gelfond and A. Provetti. In this paper, we expand the known results about computational complexity of planning (including our own previous results) to this more general class of planning problems.