A mathematical treatment of defeasible reasoning and its implementation
Artificial Intelligence
Artificial Intelligence
Reaching agreements through argumentation: a logical model and implementation
Artificial Intelligence
Preferred answer sets for extended logic programs
Artificial Intelligence
A Reasoning Model Based on the Production of Acceptable Arguments
Annals of Mathematics and Artificial Intelligence
Inferring from Inconsistency in Preference-Based Argumentation Frameworks
Journal of Automated Reasoning
Management of Preferences in Assumption-Based Reasoning
IPMU '92 Proceedings of the 4th International Conference on Processing and Management of Uncertainty in Knowledge-Based Systems: Advanced Methods in Artificial Intelligence
Acceptability of arguments as `logical uncertainty'
ECSQARU '93 Proceedings of the European Conference on Symbolic and Quantitative Approaches to Reasoning and Uncertainty
Comparing Arguments Using Preference Orderings for Argument-Based Reasoning
ICTAI '96 Proceedings of the 8th International Conference on Tools with Artificial Intelligence
Modeling Dialogues Using Argumentation
ICMAS '00 Proceedings of the Fourth International Conference on MultiAgent Systems (ICMAS-2000)
Coherence and Flexibility in Dialogue Games for Argumentation
Journal of Logic and Computation
On the evaluation of argumentation formalisms
Artificial Intelligence
Computing ideal sceptical argumentation
Artificial Intelligence
On principle-based evaluation of extension-based argumentation semantics
Artificial Intelligence
Computational properties of argument systems satisfying graph-theoretic constraints
Artificial Intelligence
Argument based machine learning
Artificial Intelligence
Agents that argue and explain classifications
Autonomous Agents and Multi-Agent Systems
Elements of Argumentation
Preference-based argumentation: Arguments supporting multiple values
International Journal of Approximate Reasoning
Using arguments for making and explaining decisions
Artificial Intelligence
Reasoning about preferences in argumentation frameworks
Artificial Intelligence
Proceedings of the 2006 conference on Computational Models of Argument: Proceedings of COMMA 2006
Acyclic Argumentation: Attack = Conflict + Preference
Proceedings of the 2006 conference on ECAI 2006: 17th European Conference on Artificial Intelligence August 29 -- September 1, 2006, Riva del Garda, Italy
Bridging the Gap between Abstract Argumentation Systems and Logic
SUM '09 Proceedings of the 3rd International Conference on Scalable Uncertainty Management
Preferred subtheories: an extended logical framework for default reasoning
IJCAI'89 Proceedings of the 11th international joint conference on Artificial intelligence - Volume 2
On the relation between argumentation and non-monotonic coherence based entailment
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 2
SCC-recursiveness: a general schema for argumentation semantics
Artificial Intelligence
Repairing preference-based argumentation frameworks
IJCAI'09 Proceedings of the 21st international jont conference on Artifical intelligence
Extending Argumentation to Make Good Decisions
ADT '09 Proceedings of the 1st International Conference on Algorithmic Decision Theory
On defense strength of blocking defeaters in admissible sets
KSEM'07 Proceedings of the 2nd international conference on Knowledge science, engineering and management
Generalizing stable semantics by preferences
Proceedings of the 2010 conference on Computational Models of Argument: Proceedings of COMMA 2010
Refined Preference-based Argumentation Frameworks
Proceedings of the 2010 conference on Computational Models of Argument: Proceedings of COMMA 2010
On the Role of Preferences in Argumentation Frameworks
ICTAI '10 Proceedings of the 2010 22nd IEEE International Conference on Tools with Artificial Intelligence - Volume 01
A formal analysis of logic-based argumentation systems
SUM'10 Proceedings of the 4th international conference on Scalable uncertainty management
Arguing for decisions: a qualitative model of decision making
UAI'96 Proceedings of the Twelfth international conference on Uncertainty in artificial intelligence
Argumentative inference in uncertain and inconsistent knowledge bases
UAI'93 Proceedings of the Ninth international conference on Uncertainty in artificial intelligence
Identifying the Core of Logic-Based Argumentation Systems
ICTAI '11 Proceedings of the 2011 IEEE 23rd International Conference on Tools with Artificial Intelligence
Revisiting preferences and argumentation
IJCAI'11 Proceedings of the Twenty-Second international joint conference on Artificial Intelligence - Volume Volume Two
Beyond maxi-consistent argumentation operators
JELIA'12 Proceedings of the 13th European conference on Logics in Artificial Intelligence
Generalizing naive and stable semantics in argumentation frameworks with necessities and preferences
SUM'12 Proceedings of the 6th international conference on Scalable Uncertainty Management
A general account of argumentation with preferences
Artificial Intelligence
On the complexity of probabilistic abstract argumentation
IJCAI'13 Proceedings of the Twenty-Third international joint conference on Artificial Intelligence
Rich preference-based argumentation frameworks
International Journal of Approximate Reasoning
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Dung's argumentation framework consists of a set of arguments and an attack relation among them. Arguments are evaluated and acceptable sets of them, called extensions, are computed using a given semantics. Each extension represents a coherent position. In the literature, several proposals have extended this framework in order to take into account the strength of arguments. The basic idea is to ignore an attack if the attacked argument is stronger than (or preferred to) its attacker. Semantics are then applied using only the remaining attacks. In this paper, we show that those proposals behave correctly when the attack relation is symmetric. However, when it is asymmetric, conflicting extensions may be computed leading to unintended conclusions. We propose an approach that guarantees conflict-free extensions. This approach presents two novelties: the first one is that it takes into account preferences at the semantics level rather than the attack level. The idea is to extend existing semantics with preferences. In case preferences are not available or do not conflict with the attacks, the extensions of the new semantics coincide with those of the basic ones. The second novelty of our approach is that a semantics is defined as a dominance relation on the powerset of the set of arguments. The extensions (under a semantics) are the maximal elements of the dominance relation. Such an approach makes it possible not only to compute the extensions of a framework but also to compare its non-extensions. We start by proposing three dominance relations that generalize respectively stable, preferred and grounded semantics with preferences. Then, we focus on stable semantics and provide full characterizations of its dominance relations and those of its generalized versions. Complexity results are provided. Finally, we show that an instance of the proposed framework retrieves the preferred sub-theories which were proposed in the context of handling inconsistency in weighted knowledge bases.