Skip to main content
SpringerLink
Log in

Automated Negotiations Under User Preference Uncertainty: A Linear Programming Approach

  • Conference paper
  • First Online:
Agreement Technologies (AT 2018)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 11327))

Included in the following conference series:

Abstract

Autonomous agents negotiating on our behalf find applications in everyday life in many domains such as high frequency trading, cloud computing and the smart grid among others. The agents negotiate with one another to reach the best agreement for the users they represent. An obstacle in the future of automated negotiators is that the agent may not always have a priori information about the preferences of the user it represents. The purpose of this work is to develop an agent that will be able to negotiate given partial information about the user’s preferences. First, we present a new partial information model that is supplied to the agent, which is based on categorical data in the form of pairwise comparisons of outcomes instead of precise utility information. Using this partial information, we develop an estimation model that uses linear optimization and translates the information into utility estimates. We test our methods in a negotiation scenario based on a smart grid cooperative where agents participate in energy trade-offs. The results show that already with very limited information the model becomes accurate quickly and performs well in an actual negotiation setting. Our work provides valuable insight into how uncertainty affects an agent’s negotiation performance, how much information is needed to be able to formulate an accurate user model, and shows a capability of negotiating effectively with minimal user feedback.

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

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

Notes

  1. 1.

    The time range of a negotiation usually is \( \left[ 0, D \right] \) where D is the deadline in rounds or time units and is normalized to the values \( \left[ 0, 1 \right] .\)

References

  1. Aydoğan, R., Baarslag, T., Hindriks, K.V., Jonker, C.M., Yolum, P.: Heuristics for using CP-nets in utility-based negotiation without knowing utilities. Knowl. Inf. Syst. 45(2), 357–388 (2015). https://doi.org/10.1007/s10115-014-0798-z

    Article  Google Scholar 

  2. Aydogan, R., et al.: A baseline for non-linear bilateral negotiations: the full results of the agents competing in ANAC 2014. In: Intelligent Computational Systems: A Multi-Disciplinary Perspective, pp. 1–25. Bentham Science, July 2016. https://eprints.soton.ac.uk/399235/

  3. Aydoğan, R., Yolum, P.: Learning opponent’s preferences for effective negotiation: an approach based on concept learning. Auton. Agent. Multi-Agent Syst. 24(1), 104–140 (2012)

    Article  Google Scholar 

  4. Baarslag, T.: Exploring the Strategy Space of Negotiating Agents: A Framework for Bidding, Learning and Accepting in Automated Negotiation. ST. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-28243-5

    Book  Google Scholar 

  5. Baarslag, T., et al.: Evaluating practical negotiating agents: results and analysis of the 2011 international competition. Artif. Intell. 198, 73–103 (2013). https://doi.org/10.1016/j.artint.2012.09.004

    Article  Google Scholar 

  6. Baarslag, T., Gerding, E.H.: Optimal incremental preference elicitation during negotiation. In: Proceedings of the Twenty-Fourth International Joint Conference on Artificial Intelligence, IJCAI 2015, pp. 3–9. AAAI Press (2015). http://dl.acm.org/citation.cfm?id=2832249.2832250

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

    Article  Google Scholar 

  8. Baarslag, T., Kaisers, M.: The value of information in automated negotiation: a decision model for eliciting user preferences. In: Proceedings of the 16th Conference on Autonomous Agents and MultiAgent Systems, AAMAS 2017, pp. 391–400. International Foundation for Autonomous Agents and Multiagent Systems, Richland, SC (2017). http://dl.acm.org/citation.cfm?id=3091125.3091185

  9. Baarslag, T., Kaisers, M., Gerding, E.H., Jonker, C.M., Gratch, J.: Computers that negotiate on our behalf: major challenges for self-sufficient, self-directed, and interdependent negotiating agents. In: Sukthankar, G., Rodriguez-Aguilar, J.A. (eds.) AAMAS 2017. LNCS (LNAI), vol. 10643, pp. 143–163. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-71679-4_10

    Chapter  Google Scholar 

  10. Boutilier, C., Brafman, R.I., Domshlak, C., Hoos, H.H., Poole, D.: CP-nets: a tool for representing and reasoning withconditional ceteris paribus preference statements. ArXiv e-prints, June 2011

    Google Scholar 

  11. Cornelio, C., Goldsmith, J., Mattei, N., Rossi, F., Venable, K.B.: Updates and uncertainty in CP-nets. In: Cranefield, S., Nayak, A. (eds.) AI 2013. LNCS (LNAI), vol. 8272, pp. 301–312. Springer, Cham (2013). https://doi.org/10.1007/978-3-319-03680-9_32

    Chapter  Google Scholar 

  12. Fatima, S.S., Wooldridge, M., Jennings, N.R.: Optimal negotiation strategies for agents with incomplete information. In: Meyer, J.-J.C., Tambe, M. (eds.) ATAL 2001. LNCS (LNAI), vol. 2333, pp. 377–392. Springer, Heidelberg (2002). https://doi.org/10.1007/3-540-45448-9_28. http://dl.acm.org/citation.cfm?id=648208.757345

    Chapter  MATH  Google Scholar 

  13. Fatima, S.S., Wooldridge, M., Jennings, N.R.: Multi-issue negotiation under time constraints. In: Proceedings of the First International Joint Conference on Autonomous Agents and Multiagent Systems: Part 1, AAMAS 2002, pp. 143–150. ACM, New York (2002). https://doi.org/10.1145/544741.544775

  14. Greco, S., Kadziński, M., Mousseau, V., Słowiński, R.: Robust ordinal regression for multiple criteria group decision: UTAGMS-GROUP and UTADISGMS-GROUP. Decis. Support Syst. 52(3), 549–561 (2012). https://doi.org/10.1016/j.dss.2011.10.005

    Article  Google Scholar 

  15. Ito, T., Klein, M., Hattori, H.: A multi-issue negotiation protocol among agents with nonlinear utility functions. Multiagent Grid Syst. 4(1), 67–83 (2008)

    Article  Google Scholar 

  16. Jacquet-Lagreze, E., Siskos, J.: Assessing a set of additive utility functions for multicriteria decision-making, the UTA method. Eur. J. Oper. Res. 10(2), 151–164 (1982). https://doi.org/10.1016/0377-2217(82)90155-2

    Article  MATH  Google Scholar 

  17. Jonker, C.M., Robu, V., Treur, J.: An agent architecture for multi-attribute negotiation using incomplete preference information. Auton. Agent. Multi-Agent Syst. 15(2), 221–252 (2007). https://doi.org/10.1007/s10458-006-9009-y

    Article  Google Scholar 

  18. Keeney, R., Raiffa, H.: Decisions with Multiple Objectives: Preferences and Value Trade-Offs. Wiley Series in Probability and Mathematical Statistics. Applied Probability and Statistics. Cambridge University Press (1993). https://books.google.nl/books?id=GPE6ZAqGrnoC

  19. Marsa-Maestre, I., Lopez-Carmona, M.A., Velasco, J.R., Ito, T., Klein, M., Fujita, K.: Balancing utility and deal probability for auction-based negotiations in highly nonlinear utility spaces. In: IJCAI, vol. 9, pp. 214–219 (2009)

    Google Scholar 

  20. Mohammad, Y., Nakadai, S.: FastVOI: efficient utility elicitation during negotiations. In: Miller, T., Oren, N., Sakurai, Y., Noda, I., Savarimuthu, B.T.R., Cao Son, T. (eds.) PRIMA 2018. LNCS (LNAI), vol. 11224, pp. 560–567. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-03098-8_42

    Chapter  Google Scholar 

  21. Nguyen, D.V.: Global maximization of UTA functions in multi-objective optimization. Eur. J. Oper. Res. 228(2), 397–404 (2013). https://doi.org/10.1016/j.ejor.2012.06.022

    Article  MathSciNet  MATH  Google Scholar 

  22. Roszkowska, E.: The application of UTA method for support evaluation negotiation offers. Optimum Stud. Ekonomiczne 2(80), 144–162 (2016). https://doi.org/10.15290/ose.2016.02.80.11

    Article  Google Scholar 

  23. Sanchez-Anguix, V., Aydoğan, R., Baarslag, T., Jonker, C.M.: Can we reach pareto optimal outcomes using bottom-up approaches? In: Aydoğan, R., Baarslag, T., Gerding, E., Jonker, C.M., Julian, V., Sanchez-Anguix, V. (eds.) COREDEMA 2016. LNCS (LNAI), vol. 10238, pp. 19–35. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-57285-7_2

    Chapter  Google Scholar 

  24. Srinivasan, V., Shocker, A.D.: Estimating the weights for multiple attributes in a composite criterion using pairwise judgments. Psychometrika 38(4), 473–493 (1973). https://doi.org/10.1007/BF02291490

    Article  MathSciNet  MATH  Google Scholar 

Download references

Acknowledgment

This work is part of the Veni research programme with project number 639.021.751, which is financed by the Netherlands Organisation for Scientific Research (NWO).

Author information

Authors and Affiliations

  1. Centrum Wiskunde & Informatica, Science Park 123, 1098 XG, Amsterdam, The Netherlands

    Dimitrios Tsimpoukis, Tim Baarslag & Michael Kaisers

  2. Technische Universiteit Eindhoven, De Zaale, 5600 MB, Eindhoven, The Netherlands

    Dimitrios Tsimpoukis & Nikolaos G. Paterakis

  3. Department of Information and Computing Sciences, Utrecht University, Princetonplein 5, 3584 CC, Utrecht, The Netherlands

    Tim Baarslag

Authors
  1. Dimitrios Tsimpoukis
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Tim Baarslag
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Michael Kaisers
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Nikolaos G. Paterakis
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Dimitrios Tsimpoukis .

Editor information

Editors and Affiliations

  1. IMT Lille Douai, Douai, France

    Marin Lujak

Rights and permissions

Reprints and permissions

Copyright information

© 2019 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Tsimpoukis, D., Baarslag, T., Kaisers, M., Paterakis, N.G. (2019). Automated Negotiations Under User Preference Uncertainty: A Linear Programming Approach. In: Lujak, M. (eds) Agreement Technologies. AT 2018. Lecture Notes in Computer Science(), vol 11327. Springer, Cham. https://doi.org/10.1007/978-3-030-17294-7_9

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-17294-7_9

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-17293-0

  • Online ISBN: 978-3-030-17294-7

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation