Proceedings of the first international conference on Principles of knowledge representation and reasoning
A mathematical treatment of defeasible reasoning and its implementation
Artificial Intelligence
Artificial Intelligence
Abstract argumentation systems
Artificial Intelligence
A logic-based theory of deductive arguments
Artificial Intelligence
Defeasible reasoning with variable degrees of justification
Artificial Intelligence
Coherence in finite argument systems
Artificial Intelligence
Inferring from Inconsistency in Preference-Based Argumentation Frameworks
Journal of Automated Reasoning
Normative Argumentation and Qualitative Probability
ECSQARU/FAPR '97 Proceedings of the First International Joint Conference on Qualitative and Quantitative Practical Reasoning
On the acceptability of arguments in preference-based argumentation
UAI'98 Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence
On the evaluation of argumentation formalisms
Artificial Intelligence
Argumentation in artificial intelligence
Artificial Intelligence
On the merging of Dung's argumentation systems
Artificial Intelligence
A dialogue mechanism for public argumentation using conversation policies
Proceedings of the 7th international joint conference on Autonomous agents and multiagent systems - Volume 1
A Game-Theoretic Measure of Argument Strength for Abstract Argumentation
JELIA '08 Proceedings of the 11th European conference on Logics in Artificial Intelligence
Combining Modes of Reasoning: An Application of Abstract Argumentation
JELIA '08 Proceedings of the 11th European conference on Logics in Artificial Intelligence
Arguing over Actions That Involve Multiple Criteria: A Critical Review
ECSQARU '07 Proceedings of the 9th European Conference on Symbolic and Quantitative Approaches to Reasoning with Uncertainty
A Dialogue Mechanism for Public Argumentation Using Conversation Policies
Argumentation in Multi-Agent Systems
A Level-based Approach to Computing Warranted Arguments in Possibilistic Defeasible Programming
Proceedings of the 2008 conference on Computational Models of Argument: Proceedings of COMMA 2008
Semantics for Evidence-Based Argumentation
Proceedings of the 2008 conference on Computational Models of Argument: Proceedings of COMMA 2008
On the qualitative comparison of decisions having positive and negative features
Journal of Artificial Intelligence Research
Combining statistics and arguments to compute trust
Proceedings of the 9th International Conference on Autonomous Agents and Multiagent Systems: volume 1 - Volume 1
Lower Bounds on Argument Verification in Computational Dialectic
Proceedings of the 2010 conference on Computational Models of Argument: Proceedings of COMMA 2010
Collective annotation: perspectives for information retrieval improvement
Large Scale Semantic Access to Content (Text, Image, Video, and Sound)
Dominant decisions by argumentation agents
ArgMAS'09 Proceedings of the 6th international conference on Argumentation in Multi-Agent Systems
IJCAI'11 Proceedings of the Twenty-Second international joint conference on Artificial Intelligence - Volume Volume Three
On the outcomes of multiparty persuasion
ArgMAS'11 Proceedings of the 8th international conference on Argumentation in Multi-Agent Systems
Using argument strength for building dialectical bonsai
Annals of Mathematics and Artificial Intelligence
Hi-index | 0.01 |
Argumentation is based on the exchange and valuation of interacting arguments, followed by the selection of the most acceptable of them (for example, in order to take a decision, to make a choice). Starting from the framework proposed by Dung in 1995, our purpose is to introduce "graduality" in the selection of the best arguments, i.e. to be able to partition the set of the arguments in more than the two usual subsets of "selected" and "non-selected" arguments in order to represent different levels of selection. Our basic idea is that an argument is all the more acceptable if it can be preferred to its attackers. First, we discuss general principles underlying a "gradual" valuation of arguments based on their interactions. Following these principles, we define several valuation models for an abstract argumentation system. Then, we introduce "graduality" in the concept of acceptability of arguments. We propose new acceptability classes and a refinement of existing classes taking advantage of an available "gradual" valuation.