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Graduality in Argumentation (1107.0045v1)

Published 30 Jun 2011 in cs.AI

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.

Citations (189)

Summary

  • The paper introduces graduality into argumentation frameworks, moving beyond binary argument selection by partitioning arguments based on nuanced acceptability levels.
  • It proposes two distinct methodologies for gradual argument valuation: a local approach based on direct attackers and a global approach considering the entire network including cycles.
  • The research refines argument acceptability using gradual valuations, introducing concepts like "cleanly-accepted" and "well-defended" arguments for richer interpretations.

Graduality in Argumentation: An Expert Overview

The paper "Graduality in Argumentation" by Claudette Cayrol and Marie-Christine Lagasquie-Schiex explores the introduction of graduality in argumentation frameworks, building upon the foundational principles established by Dung in 1995. The primary objective of this research is to transcend the binary selection of "accepted" and "rejected" arguments by introducing a nuanced partitioning approach based on the acceptability levels of arguments.

Gradual Valuation of Arguments

The authors propose two distinct methodologies for implementing graduality in argumentation valuation: the local approach and the global approach. The local approach relies on the immediate interactions between arguments, specifically focusing on direct attackers. Notably, this is illustrated through Besnard and Hunter's method and Jakobovits and Vermeir's labeling system. These methods evaluate an argument's value based on the degree and quality of these attacks, producing a comprehensive valuation model that captures various interaction dynamics.

Contrastingly, the global approach considers the entire network of arguments and the paths they form. This method records all branches leading to an argument, categorized as either attack branches (odd-length paths) or defense branches (even-length paths). Importantly, it considers cycles within the graph and uses a sophisticated tuple-based system to encapsulate branch information, further elucidating an argument's overall standing in the network.

Gradual Acceptability

In redefining the acceptability of arguments, the paper introduces notions such as "cleanly-accepted" and "well-defended" arguments. These categories seek to refine traditional acceptability levels such as skeptical and credulous acceptance by incorporating the values assigned during the gradual valuation phase. Cleanly-accepted arguments are those which belong to at least one extension without their attackers' inclusion, enhancing the distinction between simple and nuanced acceptability. Meanwhile, well-defended arguments are arguments preferred over their attackers, allowing for a more context-aware evaluation of their standing within argumentation systems.

Integrating Valuation and Acceptability

One of the key contributions of this research is the synthesis of argument valuation and acceptability, demonstrating their interdependence. The introduction of graduality extends the utility of Dung's frameworks by allowing for richer, more nuanced interpretations of argument relations and their implications. This work posits that gradual interaction-based valuations provide a more realistic and flexible approach to determining argument acceptability.

Implications and Future Directions

The implications of this research are multifaceted, affecting both theoretical developments and practical applications of argumentation systems. The introduction of graduality offers a bridge between binary and continuous valuation spectra, potentially enhancing decision-making systems across domains such as legal reasoning, negotiation support, and collaborative decision-making.

Future developments could focus on refining the computational efficiency of these models, particularly regarding cycle handling in graphs, and further exploring the potential applications of gradual acceptability in dynamic and real-world argumentation settings. Additionally, integrating probabilistic measures or machine learning models may further enhance these frameworks.

In conclusion, the paper significantly contributes to the argumentation research field by proposing a structured approach to graduality, combining robust theoretical insights with practical evaluation mechanisms.