Strategic Sequences of Arguments for Persuasion Using Decision Trees

Persuasion is an activity that involves one party (the persuader) trying to induce another party (the persuadee) to believe or do something. For this, it can be advantageous forthe persuader to have a model of the persuadee. Recently, some proposals in the field of computational models of argument have been made for probabilistic models of what the persuadee knows about, or believes. However, these developments have not systematically harnessed established notions in decision theory for maximizing the outcome of a dialogue. To address this, we present a general framework for representing persuasion dialogues as a decision tree, and for using decision rules for selecting moves. Furthermore, we provide some empirical results showing how some well-known decision rules perform, and make observations about their general behaviour in the context of dialogues where there is uncertainty about the accuracy of the user model

Towards Computational Persuasion via Natural Language Argumentation Dialogues

Computational persuasion aims to capture the human ability to persuade through argumentation for applications such as behaviour change in healthcare (e.g. persuading people to take more exercise or eat more healthily). In this paper, we review research in computational persuasion that incorporates domain modelling (capturing arguments and counterarguments that can appear in a persuasion dialogues), user modelling (capturing the beliefs and concerns of the persuadee), and dialogue strategies (choosing the best moves for the persuader to maximize the chances that the persuadee is persuaded). We discuss evaluation of prototype systems that get the user’s counterarguments by allowing them to select them from a menu. Then we consider how this work might be enhanced by incorporating a natural language interface in the form of an argumentative chatbot

Strategic Argumentation Dialogues for Persuasion: Framework and Experiments Based on Modelling the Beliefs and Concerns of the Persuadee

Persuasion is an important and yet complex aspect of human intelligence. When undertaken through dialogue, the deployment of good arguments, and therefore counterarguments, clearly has a significant effect on the ability to be successful in persuasion. Two key dimensions for determining whether an argument is good in a particular dialogue are the degree to which the intended audience believes the argument and counterarguments, and the impact that the argument has on the concerns of the intended audience. In this paper, we present a framework for modelling persuadees in terms of their beliefs and concerns, and for harnessing these models in optimizing the choice of move in persuasion dialogues. Our approach is based on the Monte Carlo Tree Search which allows optimization in real-time. We provide empirical results of a study with human participants showing that our automated persuasion system based on this technology is superior to a baseline system that does not take the beliefs and concerns into account in its strategy.Comment: The Data Appendix containing the arguments, argument graphs, assignment of concerns to arguments, preferences over concerns, and assignment of beliefs to arguments, is available at the link http://www0.cs.ucl.ac.uk/staff/a.hunter/papers/unistudydata.zip The code is available at https://github.com/ComputationalPersuasion/MCC

Strategic argumentation dialogues for persuasion: Framework and experiments based on modelling the beliefs and concerns of the persuadee

Persuasion is an important and yet complex aspect of human intelligence. When undertaken through dialogue, the deployment of good arguments, and therefore counterarguments, clearly has a significant effect on the ability to be successful in persuasion. Two key dimensions for determining whether an argument is 'good' in a particular dialogue are the degree to which the intended audience believes the argument and counterarguments, and the impact that the argument has on the concerns of the intended audience. In this paper, we present a framework for modelling persuadees in terms of their beliefs and concerns, and for harnessing these models in optimizing the choice of move in persuasion dialogues. Our approach is based on the Monte Carlo Tree Search which allows optimization in real-time. We provide empirical results of a study with human participants that compares an automated persuasion system based on this technology with a baseline system that does not take the beliefs and concerns into account in its strategy

Updating probabilistic epistemic states in persuasion dialogues

In persuasion dialogues, the ability of the persuader to model the persuadee allows the persuader to make better choices of move. The epistemic approach to probabilistic argumentation is a promising way of modelling the persuadee’s belief in arguments, and proposals have been made for update methods that specify how these beliefs can be updated at each step of the dialogue. However, there is a need to better understand these proposals, and moreover, to gain insights into the space of possible update functions. So in this paper, we present a general framework for update functions in which we consider existing and novel update functions

Towards a framework for computational persuasion with applications in behaviour change

Persuasion is an activity that involves one party trying to induce another party to believe something or to do something. It is an important and multifaceted human facility. Obviously, sales and marketing is heavily dependent on persuasion. But many other activities involve persuasion such as a doctor persuading a patient to drink less alcohol, a road safety expert persuading drivers to not text while driving, or an online safety expert persuading users of social media sites to not reveal too much personal information online. As computing becomes involved in every sphere of life, so too is persuasion a target for applying computer-based solutions. An automated persuasion system (APS) is a system that can engage in a dialogue with a user (the persuadee) in order to persuade the persuadee to do (or not do) some action or to believe (or not believe) something. To do this, an APS aims to use convincing arguments in order to persuade the persuadee. Computational persuasion is the study of formal models of dialogues involving arguments and counterarguments, of user models, and strategies, for APSs. A promising application area for computational persuasion is in behaviour change. Within healthcare organizations, government agencies, and non-governmental agencies, there is much interest in changing behaviour of particular groups of people away from actions that are harmful to themselves and/or to others around them

Strategies in Case-Based Argumentation-Based Negotiation: An Application for the Tourism Domain

[EN] Negotiation is a key solution to find an agreement between conflicting parties especially during the purchase journey. This paper treats the negotiations between a travel agency and its customers in the domain of tourism. Both automated negotiation and argumentation are gathered to create a framework for automated agents, presenting a travel agency and its customers, to negotiate a trip and exchange arguments. Agents take advantage of their past experiences and use Case-Based Reasoning to select the best strategy to follow. We represent agents using two types of profiles, Argumentative profile that represents agents¿ ways of reasoning and Preference profile that embodies customers¿ preferences in the domain of tourism.Bouslama, R.; Jordán, J.; Heras, S.; Amor, NB. (2020). Strategies in Case-Based Argumentation-Based Negotiation: An Application for the Tourism Domain. Springer. 205-217. https://doi.org/10.1007/978-3-030-51999-5_17S205217Aamodt, A., Plaza, E.: Case-based reasoning: foundational issues, methodological variations, and system approaches. AI Commun. 7(1), 39–59 (1994)Adnan, M.H.M., Hassan, M.F., Aziz, I., Paputungan, I.V.: Protocols for agent-based autonomous negotiations: a review. In: ICCOINS, pp. 622–626. IEEE (2016)Amgoud, L., Parsons, S.: Agent dialogues with conflicting preferences. In: Meyer, J.-J.C., Tambe, M. (eds.) ATAL 2001. LNCS (LNAI), vol. 2333, pp. 190–205. Springer, Heidelberg (2002). https://doi.org/10.1007/3-540-45448-9_14Amgoud, L., Prade, H.: Generation and evaluation of different types of arguments in negotiation. In: NMR, pp. 10–15 (2004)Bouslama, R., Ayachi, R., Ben Amor, N.: A new generic framework for argumentation-based negotiation using case-based reasoning. In: Medina, J., et al. (eds.) IPMU 2018. CCIS, vol. 854, pp. 633–644. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-91476-3_52Bouslama, R., Ayachi, R., Ben Amor, N.: A new generic framework for mediated multilateral argumentation-based negotiation using case-based reasoning. In: Kern-Isberner, G., Ognjanović, Z. (eds.) ECSQARU 2019. LNCS (LNAI), vol. 11726, pp. 14–26. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-29765-7_2Dimopoulos, Y., Moraitis, P.: Advances in argumentation based negotiation. In: Negotiation and Argumentation in Multi-agent Systems: Fundamentals, Theories, Systems and Applications, pp. 82–125 (2014)Hadidi, N., Dimopoulos, Y., Moraitis, P.: Tactics and concessions for argumentation-based negotiation. In: Computational Models of Argument: Proceedings of COMMA 2012, vol. 245, pp. 285–296 (2012)Hadoux, E., Hunter, A.: Strategic sequences of arguments for persuasion using decision trees. In: AAAI (2017)Heras, S., Jordán, J., Botti, V., Julián, V.: Argue to agree: a case-based argumentation approach. IJAR 54(1), 82–108 (2013)Heras, S., Jordán, J., Botti, V., Julián, V.: Case-based strategies for argumentation dialogues in agent societies. Inf. Sci. 223, 1–30 (2013)Jennings, N.R., Faratin, P., Lomuscio, A.R., Parsons, S., Sierra, C., Wooldridge, M.: Automated negotiation: prospects, methods and challenges. Int. J. Group Decis. Negot. 10(2), 199–215 (2001)Lazar, C.M.: Internet-an aid for e-tourism. Ecoforum J. 8(1), 1–4 (2019)Lopes, F., Novais, A.Q., Coelho, H.: Bilateral negotiation in a multi-agent energy market. In: Huang, D.-S., Jo, K.-H., Lee, H.-H., Kang, H.-J., Bevilacqua, V. (eds.) ICIC 2009. LNCS, vol. 5754, pp. 655–664. Springer, Heidelberg (2009). https://doi.org/10.1007/978-3-642-04070-2_71Park, S., Tussyadiah, I., Mazanec, J., Fesenmaier, D.: Travel personae of american pleasure travelers: a network analysis. J. Travel Tour. Mark. 27, 797–811 (2010)Rahwan, I., Ramchurn, S.D., Jennings, N.R., Mcburney, P., Parsons, S., Sonenberg, L.: Argumentation-based negotiation. KER 18(4), 343–375 (2003)Rahwan, I., Sonenberg, L., McBurney, P.: Bargaining and argument-based negotiation: some preliminary comparisons. In: Rahwan, I., Moraïtis, P., Reed, C. (eds.) ArgMAS 2004. LNCS (LNAI), vol. 3366, pp. 176–191. Springer, Heidelberg (2005). https://doi.org/10.1007/978-3-540-32261-0_12Sierra, C., Jennings, N.R., Noriega, P., Parsons, S.: A framework for argumentation-based negotiation. In: Singh, M.P., Rao, A., Wooldridge, M.J. (eds.) ATAL 1997. LNCS, vol. 1365, pp. 177–192. Springer, Heidelberg (1998). https://doi.org/10.1007/BFb0026758Soh, L.K., Tsatsoulis, C.: Agent-based argumentative negotiations with case-based reasoning. In: AAAI Fall Symposium Series on Negotiation Methods for Autonomous Cooperative Systems, pp. 16–25 (2001)Sycara, K.P.: Persuasive argumentation in negotiation. Theory Decis. 28(3), 203–242 (1990). https://doi.org/10.1007/BF00162699Walton, D.: Argumentation Schemes for Presumptive Reasoning. Routledge, Abingdon (2013