Planning for conjunctive goals
Artificial Intelligence
Planning as search: a quantitative approach
Artificial Intelligence
Completeness in approximation classes
Information and Computation
Planning in polynomial time: the SAS-PUBS class
Computational Intelligence
The computational complexity of propositional STRIPS planning
Artificial Intelligence
Expressive equivalence of planning formalisms
Artificial Intelligence - Special volume on planning and scheduling
Artificial Intelligence - Special volume on planning and scheduling
Random Debaters and the Hardness of Approximating Stochastic Functions
SIAM Journal on Computing
A nonapproximability result for finite function generation
Information Processing Letters
The complexity of approximating pspace-complete problems for hierarchical specifications
Nordic Journal of Computing
On Approximation Scheme Preserving Reducability and Its Applications
Proceedings of the 14th Conference on Foundations of Software Technology and Theoretical Computer Science
The complexity of dynamic languages and dynamic optimization problems
STOC '81 Proceedings of the thirteenth annual ACM symposium on Theory of computing
Learning first-order definitions of functions
Journal of Artificial Intelligence Research
Planning with abstraction hierarchies can be exponentially less efficient
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 2
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The computational complexity of planning with Strips‐style operators has received a considerable amount of interest in the literature. However, the approximability of such problems has only received minute attention. We study two Pspace‐hard optimization versions of propositional planning and provide tight upper and lower bounds on their approximability.