A Multi-agent Argumentation Framework to Support Collective Reasoning

Ganzer-Ripoll, Jordi; López-Sánchez, Maite; Rodŕıguez-Aguilar, Juan A.

doi:10.1007/978-3-319-57285-7_7

Cited by 4 publications

(3 citation statements)

References 12 publications

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“…The main contribution of the RM is to distinguish clearly between objective and subjective information in a simplified way, and to do this differently to many other argumentation models (as [7,45,56]), where subjective information is jointly represented with objective information in the debate structure leading to predefined relationships (such as attack or defence) that must be shared among all participants. Thus, the expressiveness of the RM is significantly higher being able to represent debates where participants do not need to agree on the relationships between different objective facts.…”

Section: Relational Modelmentioning

confidence: 99%

“…Finally, a rather different line of work (and one that seems to have no knowledge of [7,20,24]) is that of [75], which extends the QuAD framework first introduced in [12] to deal with multiple agent debates. Similarly to our work in [45,46], the QuAD-V framework in [75] allows pro and con arguments (attackers and defenders in our terminology) and agents' votes over arguments (labels). However, the main focus of the work is not on computing a collective opinion, but on the agents contribute with individually rational opinions, a weaker version of our notion of coherent labelling defined in [46].…”

Section: Social Choice Theorymentioning

confidence: 99%

See 1 more Smart Citation

A model to support collective reasoning: Formalization, analysis and computational assessment

Ganzer¹,

Criado²,

López-Sánchez³

et al. 2020

Preprint

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Inspired by e-participation systems, in this paper we propose a new model to represent human debates and methods to obtain collective conclusions from them. This model overcomes drawbacks of existing approaches by allowing users to introduce new pieces of information into the discussion, to relate them to existing pieces, and also to express their opinion on the pieces proposed by other users. In addition, our model does not assume that users' opinions are rational in order to extract information from it, an assumption that significantly limits current approaches. Instead, we define a weaker notion of rationality that characterises coherent opinions, and we consider different scenarios based on the coherence of individual opinions and the level of consensus that users have on the debate structure. Considering these two factors, we analyse the outcomes of different opinion aggregation functions that compute a collective decision based on the individual opinions and the debate structure. In particular, we demonstrate that aggregated opinions can be coherent even if there is a lack of consensus and individual opinions are not coherent. We conclude our analysis with a computational evaluation demonstrating that collective opinions can be computed efficiently for real-sized debates.

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Section: Relational Modelmentioning

confidence: 99%

Section: Social Choice Theorymentioning

confidence: 99%

A model to support collective reasoning: Formalization, analysis and computational assessment

Ganzer¹,

Criado²,

López-Sánchez³

et al. 2020

Preprint

View full text Add to dashboard Cite

show abstract

“…As mentioned in the introduction, the relational reasoning model is more expressive than existing, related, frameworks (Awad et al, 2017b;Ganzer-Ripoll et al, 2016;Leite & Martins, 2011) because it can model situations where participants do not agree on the relationships between different facts. This is captured in the model by the acceptance function.…”

Section: Introducing the Relational Reasoning Modelmentioning

confidence: 99%

A Model to Support Collective Reasoning: Formalization, Analysis and Computational Assessment

Ganzer

Criado

López-Sánchez

et al. 2023

jair

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In this paper we propose a new model to represent human debates and methods to obtain collective conclusions from them. This model overcomes two drawbacks of existing approaches. First, our model does not assume that participants agree on the structure of the debate. It does this by allowing participants to express their opinion about all aspects of the debate. Second, our model does not assume that participants’ opinions are rational, an assumption that significantly limits current approaches. Instead, we define a weaker notion of rationality that characterises coherent opinions, and we consider different scenarios based on the coherence of individual opinions and the level of consensus. We provide a formal analysis of different opinion aggregation functions that compute a collective decision based on the individual opinions and the debate structure. In particular, we demonstrate that aggregated opinions can be coherent even if there is a lack of consensus and individual opinions are not coherent. We conclude with an empirical evaluation demonstrating that collective opinions can be computed efficiently for real-sized debates.

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Quantitative Argumentation Debates with Votes for Opinion Polling

Rago

Toni

2017

PRIMA 2017: Principles and Practice of Multi-Agent Systems

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Opinion polls are used in a variety of settings to assess the opinions of a population, but they mostly hide the reasoning behind these opinions. Argumentation, as understood in AI, can be used to evaluate opinions in dialectical exchanges, transparently articulating the reasoning behind the opinions. We give a method integrating argumentation within opinion polling to empower voters to add new statements that render their opinions in the polls individually rational while at the same time justifying them. We then show how these poll results can be amalgamated to give a collectively rational set of voters in an argumentation framework. Our method relies upon Quantitative Argumentation Debate for Voting (QuAD-V) frameworks, which extend QuAD frameworks (a form of bipolar argumentation frameworks in which arguments have an intrinsic strength) with votes expressing individuals' opinions on arguments.

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A Multi-agent Argumentation Framework to Support Collective Reasoning

Cited by 4 publications

References 12 publications

A model to support collective reasoning: Formalization, analysis and computational assessment

A model to support collective reasoning: Formalization, analysis and computational assessment

A Model to Support Collective Reasoning: Formalization, Analysis and Computational Assessment

Quantitative Argumentation Debates with Votes for Opinion Polling

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