A reinforcement learning approach to improve the argument selection effectiveness in argumentation-based negotiation

Monteserin, Ariel; Amandi, Analía

doi:10.1016/j.eswa.2012.10.045

Cited by 25 publications

(16 citation statements)

References 22 publications

(42 reference statements)

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“…Another future work aims to incorporate argument selection learning to this approach, that is, to allow the agent to learn from other agents how to select an argument from the set of candidate arguments generated previously. In this way, we will integrate this approach with a reinforcement learning approach to improve the argument selection e ectiveness [19]. and low (0.5).…”

Section: Discussionmentioning

confidence: 99%

“…This is because we want to keep the focus on the predicates used by the argument generation process. For example, the trust in the opponent is a key concept used by the argument selection process [19]: if the trust in the opponent is high then the agent will prefer to utter weak arguments (appeals instead of threats) as long as arguments of these types had been previously generated. Moreover, the trust values are updated by the evaluation process when a promise is ful lled or a request is accepted, among other situations.…”

Section: Negotiation Languagementioning

confidence: 99%

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Agents that Learn How to Generate Arguments from Other Agents

Monteserin

Amandi

2014

New Gener. Comput.

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Learning how to argue is a key ability for a negotiator agent.In this paper, we propose an approach that allows agents to learn how to build arguments by observing how other agents argue in a negotiation context. Particularly, our approach enables the agent to infer the rules for argument generation that other agents apply to build their arguments.To carry out this goal, the agent stores the arguments uttered by other agents and the facts of the negotiation context where each argument is uttered. Then, an algorithm for fuzzy generalized association rules is applied to discover the desired rules. This kind of algorithm allows us (a) to obtain general rules that can be applied to di erent negotiation contexts; and (b) to deal with the uncertainty about the knowledge of what facts of the context are taken into account by the agents. The experimental results showed that it is possible to infer argument generation rules from a reduced number of observed arguments.

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Section: Discussionmentioning

confidence: 99%

Section: Negotiation Languagementioning

confidence: 99%

Agents that Learn How to Generate Arguments from Other Agents

Monteserin

Amandi

2014

New Gener. Comput.

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“…The model has to be also trained by humans. Monteserin and Amandi (2013) applied the Qlearning approach in an argumentation-based negotiation context, which is effective in both stationary and dynamic environments (e.g. when the participants characteristics change over time).…”

Section: Reinforcement Learningmentioning

confidence: 99%

Automated negotiation in environmental resource management: Review and assessment

Eshragh

Pooyandeh

Marceau

2015

Journal of Environmental Management

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“…With the development of artificial intelligence, argumentation technology as a new way of multi-agent interaction can imitate human decision-making process to realize the conflict resolution and knowledge integration, which takes advantage of group intelligence for problem solving. In recent years, argumentation with data mining technology has been extensively studied, such as argumentation in association rule mining [ 13 – 17 ], machine learning based on argumentation [ 18 , 19 ], argumentation with inductive learning and case-based reasoning [ 20 – 22 ], argumentation in reinforcement learning [ 23 , 24 ].…”

Section: Introductionmentioning

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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A reinforcement learning approach to improve the argument selection effectiveness in argumentation-based negotiation

Cited by 25 publications

References 22 publications

Agents that Learn How to Generate Arguments from Other Agents

Agents that Learn How to Generate Arguments from Other Agents

Automated negotiation in environmental resource management: Review and assessment

Argumentation Based Joint Learning: A Novel Ensemble Learning Approach

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