Skip to main content
SpringerLink
Log in

CP-nets-based user preference learning in automated negotiation through completion and correction

  • Regular Paper
  • Published:
Knowledge and Information Systems Aims and scope Submit manuscript

Abstract

User preference learning is an important process in automated negotiation, because only when the negotiating agents are able to fully grasp the user preference information can the negotiation strategy play its due role. However, in most automated negotiation systems, user preference is assumed to be complete and correct, which is quite different from the reality. In real life, user preference is often complex and incomplete, which hinders the application of automated negotiation research in practice. To this end, this paper focuses on the learning method of user preference in negotiation. Since CP-nets can intuitively express the interdependence among negotiation issues, which have good interpretability and expansibility, they have become one of most important representations of user preference in automated negotiation. Therefore, we propose a CP-nets-based user preference learning module in negotiation framework, which consists of both passive learning and active learning methods. In passive learning, we propose an algorithm to construct complete CP-nets with incomplete user preference information. In active learning, we innovatively propose the structural query method, which improves the accuracy of preference learning represented by CP-nets with less query cost. The experimental results show that the module is effective for negotiation framework and can help users reach better agreements in negotiation.

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

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6

Similar content being viewed by others

Data availability statement

Data sharing not applicable to this article as no datasets were generated or analyzed during the current study.

References

  1. Lin K, Liu Y, Lu P et al (2022) Fuzzy constraint-based agent negotiation framework for doctor-patient shared decision-making. BMC Med Inform Decis Mak 22(1):1–17. https://doi.org/10.1186/s12911-022-01963-x

    Article  Google Scholar 

  2. Rajavel R, Thangarathanam M (2021) Agent-based automated dynamic sla negotiation framework in the cloud using the stochastic optimization approach. Appl Soft Comput 101(107):040. https://doi.org/10.1016/j.asoc.2020.107040

    Article  Google Scholar 

  3. Moghadam FS, Zarandi MHF (2022) Mitigating bullwhip effect in an agent-based supply chain through a fuzzy reverse ultimatum game negotiation module. Appl Soft Comput 116(108):278. https://doi.org/10.1016/j.asoc.2021.108278

    Article  Google Scholar 

  4. Fiedler A (2022) An agent-based negotiation protocol for supply chain finance. Comput Ind Eng 168(108):136. https://doi.org/10.1016/j.cie.2022.108136

    Article  Google Scholar 

  5. de Jonge D, Bistaffa F, Levy J (2022) Multi-objective vehicle routing with automated negotiation. Appl Intell. https://doi.org/10.1007/s10489-022-03329-2

    Article  Google Scholar 

  6. de Jonge D, Bistaffa F, Levy J (2021) A heuristic algorithm for multi-agent vehicle routing with automated negotiation. In: Proceedings of the 20th international conference on autonomous agents and multiagent systems, pp 404–412. https://doi.org/10.5555/3463952.3464004

  7. Dong Y, Luo N, Liang H (2015) Consensus building in multiperson decision making with heterogeneous preference representation structures: a perspective based on prospect theory. Appl Soft Comput 35:898–910. https://doi.org/10.1016/j.asoc.2015.03.013

    Article  Google Scholar 

  8. Zha Q, Cai J, Gu J et al (2022) Information learning-driven consensus reaching process in group decision-making with bounded rationality and imperfect information: China’s urban renewal negotiation. Appl Intell. https://doi.org/10.1007/s10489-022-04019-9

    Article  Google Scholar 

  9. Jennings NR, Faratin P, Lomuscio AR et al (2001) Automated negotiation: prospects, methods and challenges. Group Decis Negot 10(2):199–215. https://doi.org/10.1023/A:1008746126376

    Article  Google Scholar 

  10. Baarslag T (2016) Exploring the strategy space of negotiating agents: a framework for bidding, learning and accepting in automated negotiation. Springer, Cham

    Book  Google Scholar 

  11. Huelsman MA, Truszczynski M (2021) The role of model selection in preference learning. In: Proceedings of the 34th international Florida artificial intelligence research society conference. https://doi.org/10.32473/flairs.v34i1.128489

  12. Boutilier C, Brafman RI, Domshlak C et al (2004) CP-nets: a tool for representing and reasoning with conditional ceteris paribus preference statements. J Artif Intell Res 21:135–191. https://doi.org/10.1613/jair.1234

    Article  MathSciNet  MATH  Google Scholar 

  13. Baarslag T, Hindriks K, Hendrikx M, et al (2014) Decoupling negotiating agents to explore the space of negotiation strategies. In: Novel insights in agent-based complex automated negotiation, studies in computational intelligence. Springer, Tokyo, vol 535, pp 61–83. https://doi.org/10.1007/978-4-431-54758-7_4

  14. Amini M, Fathian M, Ghazanfari M (2020) A BOA-based adaptive strategy with multi-party perspective for automated multilateral negotiations. Appl Intell 50(9):2718–2748. https://doi.org/10.1007/s10489-020-01646-y

    Article  Google Scholar 

  15. Hindriks K, Tykhonov D (2008) Opponent modelling in automated multi-issue negotiation using bayesian learning. In: Proceedings of the 7th international joint conference on autonomous agents and multiagent systems, pp 326–333. https://doi.org/10.5555/1402383.1402433

  16. Malouche H, Halima YB, Ghezala HB (2022) A negotiation framework for the cloud using rough set theory-based preference prediction. Concurr Comput Pract Exp 34(22):e7149. https://doi.org/10.1002/cpe.7149

    Article  Google Scholar 

  17. Pawlak Z (1982) Rough sets. Int J Parallel Prog 11(5):341–356. https://doi.org/10.1007/bf01001956

    Article  MATH  Google Scholar 

  18. Mirzayi S, Taghiyareh F, Nassiri-Mofakham F (2022) An opponent-adaptive strategy to increase utility and fairness in agents’negotiation. Appl Intell 52(4):3587–3603. https://doi.org/10.1007/s10489-021-02638-2

    Article  Google Scholar 

  19. Baarslag T, Gerding EH (2015) Optimal incremental preference elicitation during negotiation. In: Proceedings of the 24th international joint conference on artificial intelligence, pp 3–9. https://doi.org/10.5555/2832249.2832250

  20. Baarslag T, Kaisers M (2017) The value of information in automated negotiation: a decision model for eliciting user preferences. In: Proceedings of the 16th international conference on autonomous agents and multiagent systems, pp 391–400. https://doi.org/10.5555/3091125.3091185

  21. Haddawy P, Ha V, Restificar A et al (2003) Preference elicitation via theory refinement. J Mach Learn Res 4:317–337. https://doi.org/10.5555/945365.945385

    Article  MathSciNet  MATH  Google Scholar 

  22. Towell GG, Shavlik JW (1994) Knowledge-based artificial neural networks. Artif Intell 70(1–2):119–165. https://doi.org/10.1016/0004-3702(94)90105-8

    Article  MATH  Google Scholar 

  23. Aydoğan R, Baarslag T, Fujita K, et al (2020) Challenges and main results of the automated negotiating agents competition (ANAC) 2019. In: EUMAS 2020, AT 2020: multi-agent systems and agreement technologies., lecture notes in computer science. Springer, Cham, vol 12520, pp 366–381. https://doi.org/10.1007/978-3-030-66412-1_23

  24. Aydoğan R, Yolum P (2010) Effective negotiation with partial preference information. In: Proceedings of the 9th international conference on autonomous agents and multiagent systems, pp 1605–1606. https://doi.org/10.5555/1838206.1838503

  25. Aydoğan R, Baarslag T, Hindriks KV, et al (2013) Heuristic-based approaches for CP-nets in negotiation. In: Complex automated negotiations: theories, models, and software competitions, studies in computational intelligence. Springer, Berlin, Heidelberg, vol 435, pp 113–123. https://doi.org/10.1007/978-3-642-30737-9_7

  26. Aydoğan R, Baarslag T, Hindriks KV et al (2015) Heuristics for using CP-nets in utility-based negotiation without knowing utilities. Knowl Inf Syst 45(2):357–388. https://doi.org/10.1007/s10115-014-0798-z

    Article  Google Scholar 

  27. Pȩkala B (2017) General preference structure with uncertainty data present by interval-valued fuzzy relation and used in decision making model. In: EUSFLAT 2017, IWIFSGN 2017: advances in fuzzy logic and technology 2017. Springer, Cham, vol 643, pp 150–161. https://doi.org/10.1007/978-3-319-66827-7_14

  28. Liu Z, Zhong Z, Li K et al (2018) Structure learning of conditional preference networks based on dependent degree of attributes from preference database. IEEE Access 6:27864–27872. https://doi.org/10.1109/ACCESS.2018.2837340

    Article  Google Scholar 

  29. Goldsmith J, Lang J, Truszczynski M et al (2008) The computational complexity of dominance and consistency in CP-nets. J Artif Intell Res 33:403–432. https://doi.org/10.48550/arXiv.1401.3453

    Article  MathSciNet  MATH  Google Scholar 

  30. Dimopoulos Y, Michael L, Athienitou F (2009) Ceteris paribus preference elicitation with predictive guarantees. In: Proceedings of the 21st international joint conference on artificial intelligence, pp 1890–1895. https://doi.org/10.5555/1661445.1661748

  31. Liu S, Liu J (2019) CP-nets structure learning based on mRMCR principle. IEEE Access 7:121482–121492. https://doi.org/10.1109/ACCESS.2019.2938022

    Article  Google Scholar 

  32. Cai J, Zhan J, Jiang Y (2022) Completion of user preference based on cp-nets in automated negotiation. In: Proceedings of the 14th international conference on agents and artificial intelligence, pp 383–390. https://doi.org/10.5220/0010909200003116

  33. Ramirez-Loaiza ME, Sharma M, Kumar G et al (2017) Active learning: an empirical study of common baselines. Data Min Knowl Disc 31(2):287–313. https://doi.org/10.1007/s10618-016-0469-7

    Article  MathSciNet  Google Scholar 

  34. Desreumaux L, Lemaire V (2020) Learning active learning at the crossroads? Evaluation and discussion. In: Proceedings of the 4th European conference on workshop on interactive adaptive learning, p 38. arXiv:2012.09631

  35. Koriche F, Zanuttini B (2010) Learning conditional preference networks. Artif Intell 174(11):685–703. https://doi.org/10.1016/j.artint.2010.04.019

    Article  MathSciNet  MATH  Google Scholar 

  36. Le T, Tabakhi AM, Tran-Thanh L, et al (2018) Preference elicitation with interdependency and user bother cost. In: Proceedings of the 17th international conference on autonomous agents and multiagent systems, pp 1459–1467. https://doi.org/10.5555/3237383.3237918

  37. Chevaleyre Y, Koriche F, Lang J, et al (2010) Learning ordinal preferences on multiattribute domains: The case of CP-nets. In: Preference learning. Springer, Berlin, Heidelberg, pp 273–296. https://doi.org/10.1007/978-3-642-14125-6_13

  38. Labernia F, Yger F, Mayag B et al (2018) Query-based learning of acyclic conditional preference networks from contradictory preferences. EURO J Decis Process 6(1):39–59. https://doi.org/10.1007/s40070-017-0070-3

    Article  Google Scholar 

  39. Pawlak Z (1984) On conflicts. Int J Man Mach Stud 21(2):127–134. https://doi.org/10.1016/S0020-7373(84)80062-0

    Article  MATH  Google Scholar 

  40. Skowron A, Deja R (2002) On some conflict models and conflict resolutions. Roman J Inf Sci Technol 3(1–2):69–82

    Google Scholar 

  41. Lang G, Miao D, Fujita H (2020) Three-way group conflict analysis based on pythagorean fuzzy set theory. IEEE Trans Fuzzy Syst 28(3):447–461. https://doi.org/10.1109/TFUZZ.2019.2908123

    Article  Google Scholar 

  42. Xu F, Cai M, Song H et al (2022) The selection of feasible strategies based on consistency measurement of cliques. Inf Sci 583:33–55. https://doi.org/10.1016/j.ins.2021.10.080

    Article  Google Scholar 

  43. Lang G, Yao Y (2023) Formal concept analysis perspectives on three-way conflict analysis. Int J Approx Reason 152:160–182. https://doi.org/10.1016/j.ijar.2022.10.014

    Article  MathSciNet  MATH  Google Scholar 

  44. Pawlak Z (1998) An inquiry into anatomy of conflicts. Inf Sci 109(1):65–78. https://doi.org/10.1016/S0020-0255(97)10072-X

    Article  MathSciNet  Google Scholar 

  45. Pawlak Z (2005) Some remarks on conflict analysis. Eur J Oper Res 166(3):649–654. https://doi.org/10.1016/j.ejor.2003.09.038

    Article  MATH  Google Scholar 

  46. Przybyła-Kasperek M (2020) Coalitions’ weights in a dispersed system with Pawlak conflict model. Group Decis Negot 29:549–591. https://doi.org/10.1007/s10726-020-09667-1

    Article  Google Scholar 

  47. Deja R (2002) Conflict analysis. Int J Intell Syst 17(2):235–253. https://doi.org/10.1002/int.10019

    Article  MATH  Google Scholar 

  48. Morgado A, Dodaro C, Marques-Silva J (2014) Core-guided MaxSAT with soft cardinality constraints. In: Principles and practice of constraint programming. CP 2014, Lecture Notes in Computer Science. Springer, Cham, vol 8656, pp 564–573. https://doi.org/10.1007/978-3-319-10428-7_41

  49. Ignatiev A, Morgado A, Marques-Silva J (2018) PySAT: a Python toolkit for prototyping with SAT oracles. In: Theory and applications of satisfiability testing—SAT 2018, lecture notes in computer science. Springer, Cham, vol 10929, pp 428–437. https://doi.org/10.1007/978-3-319-94144-8_26

  50. Emerson P (2013) The original borda count and partial voting. Soc Choice Welfare 40(2):353–358. https://doi.org/10.1007/s00355-011-0603-9

    Article  MathSciNet  MATH  Google Scholar 

  51. Allen TE, Goldsmith J, Justice HE, et al (2016) Generating CP-nets uniformly at random. In: Proceedings of the 30th AAAI conference on artificial intelligence, pp 872–878. https://doi.org/10.1609/aaai.v30i1.10115

  52. Faratin P, Sierra C, Jennings NR (1998) Negotiation decision functions for autonomous agents. Robot Auton Syst 24(3–4):159–182. https://doi.org/10.1016/S0921-8890(98)00029-3

    Article  Google Scholar 

Download references

Funding

This work was supported by the National Natural Science Foundation of China under Grant Nos. 62006085 and U1911201; Guangdong Province Universities Pearl River Scholar Funded Scheme (2018); the Project of Science and Technology in Guangzhou, China under Grant Nos. 202102020948 and 202007040006.

Author information

Authors and Affiliations

  1. Guangzhou Key Laboratory of Big Data and Intelligent Education, School of Computer Science, South China Normal University, Guangzhou, China

    Jianlong Cai, Jieyu Zhan & Yuncheng Jiang

  2. School of Artificial Intelligence, South China Normal University, Guangzhou, China

    Yuncheng Jiang

Authors
  1. Jianlong Cai
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Jieyu Zhan
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Yuncheng Jiang
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

All authors contributed to the study conception and design. Material preparation, data collection and analysis were performed by JC, JZ and YJ. The first draft of the manuscript was written by Jianlong Cai and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.

Corresponding author

Correspondence to Jieyu Zhan.

Ethics declarations

Conflict of interest

The authors have no competing interests to declare that are relevant to the content of this article.

Additional information

Publisher's Note

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

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Cai, J., Zhan, J. & Jiang, Y. CP-nets-based user preference learning in automated negotiation through completion and correction. Knowl Inf Syst 65, 3567–3590 (2023). https://doi.org/10.1007/s10115-023-01872-z

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10115-023-01872-z

Keywords

Search

Navigation