An informant-based approach to argument strength in Defeasible Logic Programming

Author:

Cohen Andrea1,Gottifredi Sebastian1,Tamargo Luciano H.1,García Alejandro J.1,Simari Guillermo R.1

Affiliation:

1. Institute for Computer Science and Engineering (CONICET-UNS), Department of Computer Science and Engineering, Universidad Nacional del Sur, San Andrés 800 – Campus Palihue, Bahía Blanca, Buenos Aires, Argentina. E-mails: ac@cs.uns.edu.ar, sg@cs.uns.edu.ar, lt@cs.uns.edu.ar, ajg@cs.uns.edu.ar, grs@cs.uns.edu.ar

Abstract

This work formalizes an informant-based structured argumentation approach in a multi-agent setting, where the knowledge base of an agent may include information provided by other agents, and each piece of knowledge comes attached with its informant. In that way, arguments are associated with the set of informants corresponding to the information they are built upon. Our approach proposes an informant-based notion of argument strength, where the strength of an argument is determined by the credibility of its informant agents. Moreover, we consider that the strength of an argument is not absolute, but it is relative to the resolution of the conflicts the argument is involved in. In other words, the strength of an argument may vary from one context to another, as it will be determined by comparison to its attacking arguments (respectively, the arguments it attacks). Finally, we equip agents with the means to express reasons for or against the consideration of any piece of information provided by a given informant agent. Consequently, we allow agents to argue about the arguments’ strength through the construction of arguments that challenge (respectively, defeat) or are in favour of their informant agents.

Publisher

IOS Press

Subject

Artificial Intelligence,Computational Mathematics,Computer Science Applications,Linguistics and Language

Cited by 3 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Argument strength in probabilistic argumentation based on defeasible rules;International Journal of Approximate Reasoning;2022-07

2. Preface for the special issue on argument strength;Argument & Computation;2021-02-15

3. Argument Strength in Probabilistic Argumentation Using Confirmation Theory;Lecture Notes in Computer Science;2021

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