Graduality in Argumentation

Abstract

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.

References in corpus (2)

• On the Acceptability of Arguments in Preference-Based Argumentation
• Value Based Argumentation Frameworks

Cited by in corpus (15)

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• Measuring Inconsistency in Argument Graphs
• Comparing and extending the use of defeasible argumentation with quantitative data in real-world contexts
• A matrix approach for computing extensions of argumentation frameworks
• Extending Modular Semantics for Bipolar Weighted Argumentation (Technical Report)
• The matrices of argumentation frameworks
• Broadening Label-based Argumentation Semantics with May-Must Scales (May-Must Argumentation)
• More on extension-based semantics of argumentation
• Some Supplementaries to The Counting Semantics for Abstract Argumentation
• From Weighted Conditionals of Multilayer Perceptrons to Gradual Argumentation and Back
• Handling controversial arguments by matrix
• Trust-based Multiagent Consensus or Weightings Aggregation