Program in Electrical and Computer Engineering (CPGEI), Federal University of Technology, Paraná (UTFPR), Curitiba, Brazil, e-mails: morveli.espinoza@gmail.com; tacla@utfpr.edu.br"/> Department of Computing Science of Umeå University, Umeå, Sweden, e-mail: jcnieves@cs.umu.se"/>
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2020 Volume 35
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RESEARCH ARTICLE   Open Access    

Measuring the strength of threats, rewards, and appeals in persuasive negotiation dialogues

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  • Abstract: The aim of this article is to propose a model for the measurement of the strength of rhetorical arguments (i.e., threats, rewards, and appeals), which are used in persuasive negotiation dialogues when a proponent agent tries to convince his opponent to accept a proposal. Related articles propose a calculation based on the components of the rhetorical arguments, that is, the importance of the goal of the opponent and the certainty level of the beliefs that make up the argument. Our proposed model is based on the pre-conditions of credibility and preferability stated by Guerini and Castelfranchi. Thus, we suggest the use of two new criteria for the strength calculation: the credibility of the proponent and the status of the goal of the opponent in the goal processing cycle. We use three scenarios in order to illustrate our proposal. Besides, the model is empirically evaluated and the results demonstrate that the proposed model is more efficient than previous works of the state of the art in terms of numbers of negotiation cycles, number of exchanged arguments, and number of reached agreements.
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  • Cite this article

    Mariela Morveli-Espinoza, Juan Carlos Nieves, Cesar Augusto Tacla. 2020. Measuring the strength of threats, rewards, and appeals in persuasive negotiation dialogues. The Knowledge Engineering Review 35(1), doi: 10.1017/S0269888920000405
    Mariela Morveli-Espinoza, Juan Carlos Nieves, Cesar Augusto Tacla. 2020. Measuring the strength of threats, rewards, and appeals in persuasive negotiation dialogues. The Knowledge Engineering Review 35(1), doi: 10.1017/S0269888920000405
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RESEARCH ARTICLE   Open Access    

Measuring the strength of threats, rewards, and appeals in persuasive negotiation dialogues

  • Received: 29 April 2020
  • Revised: 13 October 2020
  • Accepted: 14 October 2020
  • Published online: 12 November 2020
The Knowledge Engineering Review  35,  2020, 35(1)  |  Cite this article

Abstract: Abstract: The aim of this article is to propose a model for the measurement of the strength of rhetorical arguments (i.e., threats, rewards, and appeals), which are used in persuasive negotiation dialogues when a proponent agent tries to convince his opponent to accept a proposal. Related articles propose a calculation based on the components of the rhetorical arguments, that is, the importance of the goal of the opponent and the certainty level of the beliefs that make up the argument. Our proposed model is based on the pre-conditions of credibility and preferability stated by Guerini and Castelfranchi. Thus, we suggest the use of two new criteria for the strength calculation: the credibility of the proponent and the status of the goal of the opponent in the goal processing cycle. We use three scenarios in order to illustrate our proposal. Besides, the model is empirically evaluated and the results demonstrate that the proposed model is more efficient than previous works of the state of the art in terms of numbers of negotiation cycles, number of exchanged arguments, and number of reached agreements.

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    Acknowledgments

    • This work was partially financed by the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)—Brazil. Juan Carlos Nieves was partially supported by the European Union’s Horizon 2020 research and innovation programme under grant agreement n° 825619 (AI4EU project).

    • This is an extended version of the article accepted to be published in the Proceedings of the 17th European Conference on Multi-Agent Systems (Morveli-Espinoza et al. 2020).

    • When an agent uses rhetorical arguments to back their proposals, the negotiation is called persuasive negotiation (Ramchurn et al. 2003).

    • Strict rules are rules in classical sense, that is, the conclusion follows every time the antecedents hold whereas defeasible rules can be defeated by contrary evidence (Lam & Governatori 2011).

    • The threshold is a value used in the strength calculation model. This is better explained in Section 4.

    • The opponent modeling problem is a complex process in any strategic interaction between intelligent (human/software) agents. By making use of opponent modeling, it is possible to represent necessary information about the opponent, which may be used during the negotiation encounter. Opponent modeling can be performed either online or offline; it depends on the availability of past data. Regarding offline models, these are created before the negotiation starts by using previously obtained data from earlier negotiations. Whereas online models are constructed from knowledge that is gather during a single negotiation encounter. The existence or not of previous data about opponents changes the maintenance of opponent modeling profiles. In this sense, we believe we can use user’s profiles (like in recommending systems) and goal recognition techniques for improving the performance of our proposal.

    • Augmented reality.

    • Cialdini (2007) claims that the influence is based on six key principles: reciprocity, commitment and consistency, social proof, authority, liking, and scarcity. Furthermore, a seventh principle—called the unity principle—was added (Cialdini 2016).

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    • © The Author(s), 2020. Published by Cambridge University Press2020Cambridge University Press
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    Mariela Morveli-Espinoza, Juan Carlos Nieves, Cesar Augusto Tacla. 2020. Measuring the strength of threats, rewards, and appeals in persuasive negotiation dialogues. The Knowledge Engineering Review 35(1), doi: 10.1017/S0269888920000405
    Mariela Morveli-Espinoza, Juan Carlos Nieves, Cesar Augusto Tacla. 2020. Measuring the strength of threats, rewards, and appeals in persuasive negotiation dialogues. The Knowledge Engineering Review 35(1), doi: 10.1017/S0269888920000405
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