Skip to main content
Fachgebiete
Automobil + Motoren Bauwesen + Immobilien Business IT + Informatik Elektrotechnik + Elektronik Energie + Nachhaltigkeit Finance + Banking Management + Führung Marketing + Vertrieb Maschinenbau + Werkstoffe Versicherung + Risiko
DE
EN
Themenseiten
Organisationspsychologie Projektmanagement Marketing Smart Manufacturing
Bücher
Zeitschriften
Weitere Formate
Podcasts Webinare Technik Webinare Wirtschaft Kongresse Veranstaltungskalender Awards
Jetzt Einzelzugang starten
Zugang für Unternehmen
Referenzkunden
ATZ-Fachkongress

Chassis.tech plus 2024


4 Kongresse in einer Veranstaltung

Treffen Sie in München die Fachleute von Chassis, Lenkung, Bremsen sowie Reifen/Räder.
Erweiterte Suche
Anmelden
nach oben
Erschienen in:
Autonomous Weapon Systems – Dangers and Need for an International Prohibition Kapitel lesen Erstes Kapitel lesen

2019 | OriginalPaper | Buchkapitel

Towards Computational Persuasion via Natural Language Argumentation Dialogues

verfasst von : Anthony Hunter, Lisa Chalaguine, Tomasz Czernuszenko, Emmanuel Hadoux, Sylwia Polberg

Erschienen in: KI 2019: Advances in Artificial Intelligence

Verlag: Springer International Publishing

Einloggen

Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.

search-config
loading …

Abstract

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.

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

  • über 102.000 Bücher
  • über 537 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Maschinenbau + Werkstoffe
  • Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 390 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Maschinenbau + Werkstoffe




 

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 340 Zeitschriften

aus folgenden Fachgebieten:

  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Versicherung + Risiko




Jetzt Wissensvorsprung sichern!

Jetzt informieren
Vorheriges Kapitel Autonomous Weapon Systems – Dangers and Need for an International Prohibition
Nächstes Kapitel Analogy-Based Preference Learning with Kernels
Literatur
1.
Andrews, P., Manandhar, S., De Boni, M.: Argumentative human computer dialogue for automated persuasion. In: Proceedings of the 9th SIGdial Workshop on Discourse and Dialogue, pp. 138–147 (2008)
2.
Atkinson, K., Bench-Capon, P., Bench-Capon, T.: Value-based argumentation for democratic decision support. In: Proceedings of ICAART 2012, pp. 23–32 (2012)
3.
Atkinson, K., Baroni, P., Giacomin, M., Hunter, A., Prakken, H., Reed, C., Simari, G.R., Thimm, M., Villata, S.: Towards artificial argumentation. AI Mag. 38(3), 25–36 (2017)CrossRef
4.
Baroni, P., Giacomin, M., Vicig, P.: On rationality conditions for epistemic probabilities in abstract argumentation. In: Proceedings of COMMA 2014, pp. 121–132 (2014)
5.
Baroni, P., Gabbay, D., Giacomin, M., van der Torre, L. (eds.): Handbook of Formal Argumentation. College Publications, London (2018)MATH
6.
Besnard, P., Hunter, A.: Elements of Argumentation. MIT Press, Cambridge (2008)CrossRef
7.
BIckmore, T., Schulman, D., Sidner, C.: Automated interventions for multiple health behviours using conversational agents. Patient Educ. Couns. 92, 142–148 (2013)CrossRef
8.
Bird, S., Klein, E., Loper, E.: Natural Language Processing with Python - Analyzing Text with the Natural Language Toolkit. O’Reilly Media, Sebastopol (2009)MATH
9.
10.
Black, E., Coles, A., Hampson, C.: Planning for persuasion. In: Proceedings of AAMAS 2017, pp. 933–942 (2017)
11.
Caminada, M., Podlaszewski, M.: Grounded semantics as persuasion dialogue. In: Proceedings of COMMA 2012, pp. 478–485. IOS Press (2012)
12.
13.
Cayrol, C., Lagasquie-Schiex, M.C.: Bipolarity in argumentation graphs: towards a better understanding. Int. J. Approx. Reason. 54(7), 876–899 (2013)MathSciNetCrossRef
14.
Chalaguine, L., et al.: Domain modelling in computational persuasion for behaviour change in healthcare. arXiv (2018). arXiv:​1802.​10054 [cs.AI]
15.
Chalaguine, L., Hamilton, F., Hunter, A., Potts, H.: Argument harvesting using chatbots. In: Proceedings of COMMA 2018, pp. 149–160 (2018)
16.
Chalaguine, L., Hamilton, F., Hunter, A., Potts, H.: Impact of argument type and concerns in argumentation with a chatbot. arXiv (2018). arXiv:​1905.​00646 [cs.AI]
17.
Fan, X., Toni, F.: Assumption-based argumentation dialogues. In: Proceedings of IJCAI 2011, pp. 198–203 (2011)
18.
Fogg, B.: Persuasive computers. In: Proceedings of CHI 1998, pp. 225–232 (1998)
19.
Grasso, F.: Exciting avocados and dull pears - combining behavioural and argumentative theory for producing effective advice. In: Proceedings of CogSci 1998, pp. 436–441 (1998)
20.
21.
Grasso, F., Cawsey, A., Jones, R.: Dialectical argumentation to solve conflicts in advice giving: a case study in the promotion of healthy nutrition. Int. J. Hum Comput. Stud. 53(6), 1077–1115 (2000)CrossRef
22.
Guerrero, E., Nieves, J., Lindgren, H.: An activity-centric argumentation framework for assistive technology aimed at improving health. Argument Comput. 7, 5–33 (2016)
23.
Habernal, I., Gurevych, I.: Which argument is more convincing? Analyzing and predicting convincingness of Web arguments using bidirectional LSTM. In: Proceedings of ACL 2016, pp. 1589–1599 (2016)
24.
Hadjinikolis, C., Siantos, Y., Modgil, S., Black, E., McBurney, P.: Opponent modelling in persuasion dialogues. In: Proceedings of IJCAI 2015, pp. 164–170 (2013)
25.
Hadoux, E., Hunter, A.: Computationally viable handling of beliefs in arguments for persuasion. In: Proceedings of ICTAI 2016, pp. 319–326 (2016)
26.
Hadoux, E., Hunter, A.: Strategic sequences of arguments for persuasion using decision trees. In: Proceedings of AAAI 2017, pp. 1128–1134 (2017)
27.
Hadoux, E., Hunter, A.: Learning and updating user models for subpopulations in persuasive argumentation using beta distributions. In: Proceedings of AAMAS 2018, pp. 1141–1149 (2018)
28.
Hadoux, E., Hunter, A.: Comfort or safety? Gathering and using the concerns of a participant for better persuasion. Argument Comput. (2019, in press)
29.
30.
Hadoux, E., Hunter, A., Polberg, S.: Strategic argumentation dialogues for persuasion: framework and experiments based on modelling the beliefs and concerns of the persuadee. Technical report, University College London (2019)
31.
Huang, S., Lin, F.: The design and evaluation of an intelligent sales agent for online persuasion and negotiation. In: Electronic Commerce Research and Applications, pp. 285–296 (2007)CrossRef
32.
Hunter, A.: A probabilistic approach to modelling uncertain logical arguments. Int. J. Approx. Reason. 54(1), 47–81 (2013)MathSciNetCrossRef
33.
Hunter, A.: Modelling the persuadee in asymmetric argumentation dialogues for persuasion. In: Proceedings of IJCAI 2015, pp. 3055–3061 (2015)
34.
35.
Hunter, A.: Two dimensional uncertainty in persuadee modelling in argumentation. In: Proceedings of ECAI 2016, pp. 150–157 (2016)
36.
Hunter, A., Polberg, S.: Empirical methods for modelling persuadees in dialogical argumentation. In: Proceedings of ICTAI 2017, pp. 382–389 (2017)
37.
Hunter, A., Polberg, S., Potyka, S.: Updating belief in arguments in epistemic graphs. In: Proceedings of KR 2018, pp. 138–147 (2018)
38.
Hunter, A., Polberg, S., Thimm, M.: Epistemic graphs for representing and reasoning with positive and negative influences of arguments. arXiv (2018). arXiv:​1802.​07489 [cs.AI]
39.
40.
Hunter, A., Thimm, M.: Optimization of dialectical outcomes in dialogical argumentation. Int. J. Approx. Reason. 78, 73–102 (2016)MathSciNetCrossRef
41.
Hunter, A.: Towards a framework for computational persuasion with applications in behaviour change, argument and computation. Argument Comput. 9(1), 15–40 (2018)CrossRef
42.
Lippi, M., Torroni, P.: Argumentation mining: state of the art and emerging trends. ACM Trans. Internet Technol. 16(2), 10 (2016)CrossRef
43.
Manning, C., Schütz, H.: Foundations of Statistical Natural Language Processing. MIT Press, Cambridge (2000)
44.
45.
Ogden, J.: Health Psychology: A Textbook, 5th edn. Open University Press, Buckingham (2012)
46.
Perez-Marin, D., Pascual-Nieto, I. (eds.): Conversational agents and natural language interaction; techniques and effective practices. Information Science Reference (2011)
47.
Polberg, S., Hunter, A.: Empirical evaluation of abstract argumentation: supporting the need for bipolar and probabilistic approaches. Int. J. Approx. Reason. 93, 487–543 (2018)MathSciNetCrossRef
48.
49.
Prakken, H.: Coherence and flexibility in dialogue games for argumentation. J. Logic Comput. 15(6), 1009–1040 (2005)MathSciNetCrossRef
50.
Prakken, H.: Formal systems for persuasion dialogue. Knowl. Eng. Rev. 21(2), 163–188 (2006)CrossRef
51.
Rahwan, I., Madakkatel, M., Bonnefon, J., Awan, R., Abdallah, S.: Behavioural experiments for assessing the abstract argumentation semantics of reinstatement. Cogn. Sci. 34(8), 1483–1502 (2010)CrossRef
52.
Rahwan, I., Simari, G. (eds.): Argumentation in Artificial Intelligence. Springer, Heidelberg (2009)
53.
Rienstra, T., Thimm, M., Oren, N.: Opponent models with uncertainty for strategic argumentation. In: Proceedings of IJCAI 2013, pp. 332–338 (2013)
54.
Rosenfeld, A., Kraus, S.: Providing arguments in discussions on the basis of the prediction of human argumentative behavior. ACM Trans. Interact. Intell. Syst. 6(4), 30:1–30:33 (2016)CrossRef
55.
Stede, M., Schneider, J.: Argumentation Mining. Morgan & Claypool, San Rafael (2019)
56.
Thimm, M.: A probabilistic semantics for abstract argumentation. In: Proceedings of ECAI 2012, vol. 242, pp. 750–755 (2012)
57.
Thimm, M.: Strategic argumentation in multi-agent systems. Künstliche Intell. 28, 159–168 (2014)CrossRef
58.
59.
Toniuc, D., Groza, A.: Climebot: an argumentative agent for climate change. In: Proceedings of ICCP 2017, pp. 63–70 (2017)
60.
Zaal, E., Mills, G., Hagen, A., Huisman, C., Hoeks, J.: Convincing conversations: Using a computer-based dialogue system to promote a plant-based diet. In: Proceedings of the Cognitive Science Conference (CogSci 2017), pp. 3627–3632 (2017)
Metadaten
Titel
Towards Computational Persuasion via Natural Language Argumentation Dialogues
verfasst von
Anthony Hunter
Lisa Chalaguine
Tomasz Czernuszenko
Emmanuel Hadoux
Sylwia Polberg
Copyright-Jahr
2019
Buch
KI 2019: Advances in Artificial Intelligence

Print ISBN: 978-3-030-30178-1

Electronic ISBN: 978-3-030-30179-8

Copyright-Jahr: 2019

DOI
https://doi.org/10.1007/978-3-030-30179-8_2

Premium Partner

    Bildnachweise