Arguing from experience using multiple groups of agents

Wardeh, Maya; Bench‐Capon, Trevor; Coenen, Frans

doi:10.1080/19462166.2010.528176

Cited by 6 publications

(5 citation statements)

References 24 publications

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“…We have identified various possible tie resolution mechanisms [51]. For example we can repeat the MABCE process with the tied parties, adopt a voting strategy or simply adopt a random resolution.…”

Section: Argumentation Treementioning

confidence: 99%

“…Thus, the notion of participation groups in PISA can be likened to teamwork. Further details regarding the nature of groups in PISA can be found in [51].…”

Section: Participation Groups Coalitions and Teams In Pisamentioning

confidence: 99%

“…The leader also : (i) guides the inter-group dialogue process, (ii) may redirect attacks at opponents other to those suggested by individual group members, or (iii) insist on a particular strategy. There are various mechanisms that we may adopt to identify a team leader and these are discussed in [51]; the most straightforward approach is to simply select the agent with the largest data set (i.e. the most extensive experience).…”

Section: Participation Groups Coalitions and Teams In Pisamentioning

confidence: 99%

“…4.3), (iii) allowing agents to join or leave a dialogue; and (iv) where there is a tie situation, initiating a tie-resolution mechanism. (Although only a simple tie resolution process is used in this paper, a number of possible mechanisms are fully discussed in [51].) Give a previously unseen instance (ϕ) that requires classification and a number of PISA participating agents equivalent to the number of possible classifications, such that each is assigned the role of promoting one of the possible classifications, the PISA dialogue proceeds as follows:…”

Section: The Pisa System For Mabcementioning

confidence: 99%

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Multi-agent based classification using argumentation from experience

Wardeh

Coenen

Bench‐Capon

2012

Auton Agent Multi-Agent Syst

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An approach to classification using a multi-agent system founded on an Argumentation from Experience paradigm is proposed. The technique is based on the idea that classification can be conducted as a process whereby a group of agents "argue" about the classification of a given case according to their experience as recorded in individual local data sets. The paper describes mechanisms whereby this can be achieved, which have been realised in the PISA framework. The framework allows both the possibility of agents operating in groups (coalitions) and migrating between groups. The proposed multi-agent classification using the Argumentation from Experience paradigm has been used to address standard, ordinal and unbalanced classification problems with good results. A full evaluation, in the context of these applications, is presented.

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Section: Argumentation Treementioning

confidence: 99%

“…Thus, the notion of participation groups in PISA can be likened to teamwork. Further details regarding the nature of groups in PISA can be found in [51].…”

Section: Participation Groups Coalitions and Teams In Pisamentioning

confidence: 99%

Section: Participation Groups Coalitions and Teams In Pisamentioning

confidence: 99%

Section: The Pisa System For Mabcementioning

confidence: 99%

See 2 more Smart Citations

Multi-agent based classification using argumentation from experience

Wardeh

Coenen

Bench‐Capon

2012

Auton Agent Multi-Agent Syst

Self Cite

View full text Add to dashboard Cite

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“…However, PISA has complicated strategy and complex argumentation process, so that the model does not have general applicability. Subsequently, the concept of collaborative group of Agents is proposed for arguing from experience in [ 15 ].…”

Section: Related Workmentioning

confidence: 99%

Argumentation Based Joint Learning: A Novel Ensemble Learning Approach

Jia

Yao

2015

PLoS ONE

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Recently, ensemble learning methods have been widely used to improve classification performance in machine learning. In this paper, we present a novel ensemble learning method: argumentation based multi-agent joint learning (AMAJL), which integrates ideas from multi-agent argumentation, ensemble learning, and association rule mining. In AMAJL, argumentation technology is introduced as an ensemble strategy to integrate multiple base classifiers and generate a high performance ensemble classifier. We design an argumentation framework named Arena as a communication platform for knowledge integration. Through argumentation based joint learning, high quality individual knowledge can be extracted, and thus a refined global knowledge base can be generated and used independently for classification. We perform numerous experiments on multiple public datasets using AMAJL and other benchmark methods. The results demonstrate that our method can effectively extract high quality knowledge for ensemble classifier and improve the performance of classification.

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