NP is as easy as detecting unique solutions
Theoretical Computer Science
SIAM Journal on Computing
A catalog of complexity classes
Handbook of theoretical computer science (vol. A)
Computing functions with parallel queries to NP
Theoretical Computer Science
On unique satisfiability and the threshold behavior of randomized reductions
Journal of Computer and System Sciences
Graph theoretical structures in logic programs and default theories
Theoretical Computer Science
An abstract, argumentation-theoretic approach to default reasoning
Artificial Intelligence
On the computational complexity of assumption-based argumentation for default reasoning
Artificial Intelligence
Coherence in finite argument systems
Artificial Intelligence
Preferred Arguments are Harder to Compute than Stable Extension
IJCAI '99 Proceedings of the Sixteenth International Joint Conference on Artificial Intelligence
Reducibility, randomness, and intractibility (Abstract)
STOC '77 Proceedings of the ninth annual ACM symposium on Theory of computing
Computing ideal sceptical argumentation
Artificial Intelligence
Computational properties of argument systems satisfying graph-theoretic constraints
Artificial Intelligence
A dialectic procedure for sceptical, assumption-based argumentation
Proceedings of the 2006 conference on Computational Models of Argument: Proceedings of COMMA 2006
Computational Complexity of Semi-stable Semantics in Abstract Argumentation Frameworks
JELIA '08 Proceedings of the 11th European conference on Logics in Artificial Intelligence
Manifold Answer-Set Programs for Meta-reasoning
LPNMR '09 Proceedings of the 10th International Conference on Logic Programming and Nonmonotonic Reasoning
The computational complexity of ideal semantics
Artificial Intelligence
Weighted argument systems: Basic definitions, algorithms, and complexity results
Artificial Intelligence
A computational method for defeasible argumentation based on a recursive warrant semantics
IBERAMIA'10 Proceedings of the 12th Ibero-American conference on Advances in artificial intelligence
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We analyse the computational complexity of the recently proposed ideal semantics within abstract argumentation frameworks. It is shown that while typically less tractable than credulous admissibility semantics, the natural decision problems arising with this extension-based model can, perhaps surprisingly, be decided more efficiently than sceptical admissibility semantics. In particular the task of finding the unique maximal ideal extension is easier than that of deciding if a given argument is accepted under the sceptical semantics. We provide efficient algorithmic approaches for the class of bipartite argumentation frameworks. Finally we present a number of technical results which offer strong indications that typical problems in ideal argumentation are complete for the class PNP|| : languages decidable by polynomial time algorithms allowed to make non-adaptive queries to an np oracle.