[go: up one dir, main page]
More Web Proxy on the site http://driver.im/ Skip to main content
Springer Nature Link
Log in

Arguing and negotiating using incomplete negotiators profiles

  • Published:
Autonomous Agents and Multi-Agent Systems Aims and scope Submit manuscript

Abstract

Computational argumentation has taken a predominant place in the modeling of negotiation dialogues over the last years. A competent agent participating in a negotiation process is expected to decide its next move taking into account an, often incomplete, model of its opponent. This work provides a complete computational account of argumentation-based negotiation under incomplete opponent profiles. After the agent identifies its best option, in any state of a negotiation, it looks for suitable arguments that support this option in the theory of its opponent. As the knowledge on the opponent is uncertain, the challenge is to find arguments that, ideally, support the selected option despite the uncertainty. We present a negotiation framework based on these ideas, along with experimental evidence that highlights the advantages of our approach.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
£29.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price includes VAT (United Kingdom)

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14

Similar content being viewed by others

Explore related subjects

Discover the latest articles, news and stories from top researchers in related subjects.

Notes

  1. If some variable \(x \in V\) does not explicitly belong to any \(X_i\), i.e. \(X_1 \cup \dots \cup X_n \subset V\), then it implicitly means that x can be existentially quantified at the rightmost level.

  2. Since we use the extension-based semantics defined by Dung, we consider binary acceptability statuses for arguments: an argument that is not accepted is rejected.

References

  1. Amgoud, L., & Ben-Naim, J. (2013). Ranking-based semantics for argumentation frameworks. In Scalable uncertainty management - 7th international conference, SUM 2013, Washington, DC, USA, September 16–18, 2013. Proceedings (pp. 134–147)

  2. Amgoud, L., & Cayrol, C. (2002). A reasoning model based on the production of acceptable arguments. Annals of Mathematics and Artificial Intelligence, 34(1–3), 197–215.

    Article  MathSciNet  Google Scholar 

  3. Amgoud, L., & Kaci, S. (2006). On the study of negotiation strategies. In Agent Communication II, International Workshops on Agent Communication, AC 2005 and AC 2006, Utrecht, Netherlands, July 25, 2005 and Hakodate, Japan, May 9, 2006, Selected and Revised Papers (pp. 150–163).

  4. Amgoud, L., Dimopoulos, Y., & Moraitis, P. (2007). A unified and general framework for argumentation-based negotiation. In Proceedings of the 6th international joint conference on autonomous agents and multiagent systems, (AAMAS07), p 158, https://doi.org/10.1145/1329125.1329317.

  5. Amgoud, L., Dimopoulos, Y., & Moraitis, P. (2008). Making decisions through preference-based argumentation. In Principles of knowledge representation and reasoning: proceedings of the eleventh international conference, KR 2008, Sydney, Australia, September 16–19, 2008 (pp. 113–123).

  6. Baarslag, T., Hendrikx, M. J. C., Hindriks, K. V., & Jonker, C. M. (2016a). Learning about the opponent in automated bilateral negotiation: A comprehensive survey of opponent modeling techniques. Autonomous Agents and Multi-Agent Systems, 30(5), 849–898. https://doi.org/10.1007/s10458-015-9309-1.

    Article  Google Scholar 

  7. Baarslag, T., Hendrikx, M. J. C., Hindriks, K. V., & Jonker, C. M. (2016b). A survey of opponent modeling techniques in automated negotiation. In Jonker, C. M., Marsella, S., Thangarajah, J. & Tuyls, K. (eds) Proceedings of the 2016 international conference on autonomous agents & multiagent systems, Singapore, May 9–13, 2016 (pp. 575–576). ACM.

  8. Baroni, P., Caminada, M., & Giacomin, M. (2018). Abstract argumentation frameworks and their semantics. In D. Gabbay, M. Giacomin, & L. van der Torre (Eds.), Baroni P (pp. 159–236). Handbook of Formal Argumentation: College Publications.

  9. Baumann, R., & Brewka, G. (2010). Expanding argumentation frameworks: Enforcing and monotonicity results. In Computational models of argument: proceedings of COMMA 2010, Desenzano del Garda, Italy, September 8–10, 2010 (pp. 75–86).

  10. Baumeister, D., Neugebauer, D., Rothe, J., & Schadrack, H. (2018). Verification in incomplete argumentation frameworks. Artificial Intelligence, 264, 1–26.

    Article  MathSciNet  Google Scholar 

  11. Besnard, P., & Doutre, S. (2004). Checking the acceptability of a set of arguments. In 10th international workshop on non-monotonic reasoning (NMR 2004), Whistler, Canada, June 6–8, 2004, Proceedings (pp. 59–64).

  12. Besnard, P., & Hunter, A. (2008). Elements of argumentation. Cambridge: MIT Press.

    Book  Google Scholar 

  13. Biere, A., Heule, M., van Maaren, H., & Walsh, T. (eds) (2009). Handbook of Satisfiability, Frontiers in Artificial Intelligence and Applications, vol 185, IOS Press.

  14. Black, E., & Atkinson, K. (2011). Choosing persuasive arguments for action. In 10th international conference on autonomous agents and multiagent systems (AAMAS 2011), Taipei, Taiwan, May 2–6, 2011 (Vol. 1–3, pp. 905–912).

  15. Bonzon, E., Dimopoulos, Y., & Moraitis, P. (2012). Knowing each other in argumentation-based negotiation. In International conference on autonomous agents and multiagent systems, AAMAS 2012, Valencia, Spain, June 4–8, 2012 (3 Volumes) (pp. 1413–1414).

  16. Bouyssou, D., Marchant, T., Pirlot, M., Tsoukiàs, A., & Vincke, P. (2006). Evaluation and decision models with multiple criteria: Stepping stones for the analyst, 1st edn. Springer, Boston, URL http://www.springer.com/sgw/cda/frontpage/0118554-40521-22-116132747-000.html.

  17. Coste-Marquis, S., Konieczny, S., Mailly, J. G., & Marquis, P. (2014). A translation-based approach for revision of argumentation frameworks. In Logics in artificial intelligence—14th European conference, JELIA 2014, Funchal, Madeira, Portugal, September 24–26, 2014. Proceedings (pp. 397–411).

  18. Dimopoulos, Y., & Moraitis, P. (2014). Advances in argumentation-based negotiation. Negotiation and Argumentation in Multi-Agent Systems: Fundamentals (pp. 82–125). Systems and Applications: Theories.

  19. Dimopoulos, Y,, Mailly, J. G., & Moraitis, P. (2018). Control argumentation frameworks. In Proceedings of the thirty-second AAAI conference on artificial intelligence AAAI 2018, New Orleans, USA, February 2–7, 2018 (pp. 4678–4685).

  20. Dimopoulos, Y., Mailly, J. G., & Moraitis, P. (2019). Argumentation-based negotiation with incomplete opponent profiles. In Proceedings of the 18th international conference on autonomous agents and multiagent systems, AAMAS ’19, Montreal, QC, Canada, May 13–17, 2019 (pp. 1252–1260).

  21. Dung, P. M. (1995). On the acceptability of arguments and its fundamental role in nonmonotonic reasoning, logic programming and n-person games. Artificial Intelligence, 77, 321–357.

    Article  MathSciNet  Google Scholar 

  22. Dung, P. M., Thang, P. M., & Toni, F. (2008). Towards argumentation-based contract negotiation. In Computational models of argument: proceedings of COMMA 2008, Toulouse, France, May 28–30, 2008 (pp. 134–146).

  23. Dvorák, W., & Dunne, P. E. (2018). Computational problems in formal argumentation and their complexity. In D. Gabbay, M. Giacomin, & L. van der Torre (Eds.), Baroni P (pp. 631–688). Handbook of Formal Argumentation: College Publications.

    Google Scholar 

  24. Dvorák, W., Järvisalo, M., Wallner, J. P., & Woltran, S. (2014). Complexity-sensitive decision procedures for abstract argumentation. Artificial Intelligence, 206, 53–78.

    Article  MathSciNet  Google Scholar 

  25. Egly, U., & Woltran, S. (2006). Reasoning in argumentation frameworks using quantified boolean formulas. In Computational models of argument: Proceedings of COMMA 2006, September 11–12, 2006, Liverpool, UK (pp. 133–144).

  26. Gaggl, S.A., Linsbichler, T., Maratea, M., & Woltran, S. (2020). Design and results of the second international competition on computational models of argumentation. Artificial Intelligence 279.

  27. Hadidi, N., Dimopoulos, Y., & Moraitis, P. (2010). Argumentative alternating offers. In 9th international conference on autonomous agents and multiagent systems (AAMAS 2010), Toronto, Canada, May 10–14, 2010 (Vol. 1–3, pp. 441–448).

  28. Hadidi, N., Dimopoulos, Y., & Moraitis, P. (2012). Tactics and concessions for argumentation-based negotiation. In Computational models of argument—proceedings of COMMA 2012, Vienna, Austria, September 10–12, 2012 (pp. 285–296).

  29. Hadjinikolis, C., Siantos, Y., Modgil, S., Black, E., & McBurney, P. (2013). Opponent modelling in persuasion dialogues. In IJCAI 2013, proceedings of the 23rd international joint conference on artificial intelligence, Beijing, China, August 3–9, 2013 (pp. 164–170).

  30. Hunter, A. (2015). Modelling the persuadee in asymmetric argumentation dialogues for persuasion. In Proceedings of the twenty-fourth international joint conference on artificial intelligence, IJCAI 2015, Buenos Aires, Argentina, July 25–31, 2015 (pp. 3055–3061).

  31. Kakas, A. C., & Moraitis, P. (2006). Adaptive agent negotiation via argumentation. In 5th international joint conference on autonomous agents and multiagent systems (AAMAS 2006), Hakodate, Japan, May 8–12, 2006 (pp. 384–391).

  32. Kleine Büning, H., & Bubeck, U. (2009). Theory of quantified boolean formulas. In Handbook of satisfiability (pp. 735–760). IOS Press.

  33. Lagniez, J.M., Lonca, E., & Mailly, J.G. (2015). CoQuiAAS: A constraint-based quick abstract argumentation solver. In 27th IEEE international conference on tools with artificial intelligence, ICTAI 2015, Vietri sul Mare, Italy, November 9–11, 2015 (pp. 928–935).

  34. Mancini, T. (2016). Now or never: Negotiating efficiently with unknown or untrusted counterparts. Fundamenta Informaticae, 149(1–2), 61–100. https://doi.org/10.3233/FI-2016-1443.

    Article  MathSciNet  MATH  Google Scholar 

  35. Marey, O., Bentahar, J., Asl, E. K., Mbarki, M., & Dssouli, R. (2014). Agents’ uncertainty in argumentation-based negotiation: Classification and implementation. In Proceedings of the 5th international conference on ambient systems, networks and technologies (ANT 2014) (pp. 61–68).

  36. Monteserin, A., & Amandi, A. (2013). A reinforcement learning approach to improve the argument selection effectiveness in argumentation-based negotiation. Expert Systems with Applications, 40(6), 2182–2188. https://doi.org/10.1016/j.eswa.2012.10.045.

    Article  Google Scholar 

  37. Niskanen, A., Neugebauer, D., & Järvisalo, M. (2020a). Controllability of control argumentation frameworks. In Proceedings of the twenty-ninth international joint conference on artificial intelligence, IJCAI, 2020 (pp. 1855–1861).

  38. Niskanen, A., Neugebauer, D., Järvisalo, M., & Rothe, J. (2020b). Deciding acceptance in incomplete argumentation frameworks. In Proceedings of AAAI 2020.

  39. Oren, N., & Norman, T. J. (2009). Arguing using opponent models. In Argumentation in multi-agent systems, 6th international workshop, ArgMAS 2009, Budapest, Hungary, May 12, 2009. Revised Selected and Invited Papers (pp. 160–174).

  40. Parsons, S., Sierra, C., & Jennings, N. R. (1998). Agents that reason and negotiate by arguing. Journal of Logic and Computation, 8(3), 261–292. https://doi.org/10.1093/logcom/8.3.261.

    Article  MathSciNet  MATH  Google Scholar 

  41. Pilotti, P., Casali, A., & Chesñevar, C. I. (2015). A belief revision approach for argumentation-based negotiation agents. Applied Mathematics and Computer Science, 25(3), 455–470.

    MathSciNet  MATH  Google Scholar 

  42. Rahwan, I., Ramchurn, S. D., Jennings, N. R., McBurney, P., Parsons, S., & Sonenberg, L. (2003). Argumentation-based negotiation. Knowledge Engineering Review, 18(4), 343–375. https://doi.org/10.1017/S0269888904000098.

    Article  Google Scholar 

  43. Reimer, S., Sauer, M., Marin, P., & Becker, B. (2014). QBF with soft variables. In 14th international workshop on automated verification of critical systems (AVOCS).

  44. Rienstra, T., Thimm, M., & Oren, N. (2013). Opponent models with uncertainty for strategic argumentation. In IJCAI 2013, proceedings of the 23rd international joint conference on artificial intelligence, Beijing, China, August 3–9, 2013 (pp. 332–338).

  45. Riveret, R., Rotolo, A., Sartor, G., Prakken, H., & Roth, B. (2007). Success chances in argument games: A probabilistic approach to legal disputes. In Lodder, A. R., Mommers, L. (eds) JURIX 2007: The twentieth annual conference on legal knowledge and information systems, Leiden, The Netherlands, 2007, (Vol. 165, pp. 99–108). IOS Press.

  46. Thimm, M., & Villata, S. (2017). The first international competition on computational models of argumentation: Results and analysis. Artificial Intelligence, 252, 267–294.

    Article  MathSciNet  Google Scholar 

  47. van Laar, J. A., & Krabbe, E. C. W. (2018). The role of argument in negotiation. Argumentation, 32(4), 549–567. https://doi.org/10.1007/s10503-018-9458-x.

    Article  Google Scholar 

  48. Wallner, J. P., Niskanen, A., & Järvisalo, M. (2017). Complexity results and algorithms for extension enforcement in abstract argumentation. Journal of Artificial Intelligence Research, 60, 1–40.

    Article  MathSciNet  Google Scholar 

  49. Zafari, F., & Mofakham, F. N. (2017). POPPONENT: Highly accurate, individually and socially efficient opponent preference model in bilateral multi issue negotiations (extended abstract). In Proceedings of the twenty-sixth international joint conference on artificial intelligence, IJCAI 2017, Melbourne, Australia, August 19–25, 2017 (pp. 5100–5104).

Download references

Acknowledgements

The authors would like to thank their students Toufik Ider and Mickael Lafages for their excellent work in the implementation of the proposed framework. The authors would like also to thank the reviewers for their very constructive comments that allowed to improve significantly the quality of the paper.

Author information

Authors and Affiliations

  1. Department of Computer Science, University of Cyprus, Nicosia, Cyprus

    Yannis Dimopoulos

  2. LIPADE, University of Paris, Paris, France

    Jean-Guy Mailly & Pavlos Moraitis

  3. Argument Theory, Paris, France

    Pavlos Moraitis

Authors
  1. Yannis Dimopoulos
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Jean-Guy Mailly
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Pavlos Moraitis
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Yannis Dimopoulos.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Dimopoulos, Y., Mailly, JG. & Moraitis, P. Arguing and negotiating using incomplete negotiators profiles. Auton Agent Multi-Agent Syst 35, 18 (2021). https://doi.org/10.1007/s10458-021-09493-y

Download citation

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s10458-021-09493-y

Keywords

Search

Navigation