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The Negotiation Dialogue Game

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Book cover Dialogues with Social Robots

Part of the book series: Lecture Notes in Electrical Engineering ((LNEE,volume 427))

Abstract

This article presents the design of a generic negotiation dialogue game between two or more players. The goal is to reach an agreement, each player having his own preferences over a shared set of options. Several simulated users have been implemented. An MDP policy has been optimised individually with Fitted Q-Iteration for several user instances. Then, the learnt policies have been cross evaluated with other users. Results show strong disparity of inter-user performances. This illustrates the importance of user adaptation in negotiation-based dialogue systems.

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References

  1. Sutton, R.S., Barto, A.G.: Reinforcement Learning: An Introduction, vol. 1. MIT Press Cambridge (1998)

    Google Scholar 

  2. Levin, E., Pieraccini, R.: A stochastic model of computer-human interaction for learning dialogue strategies. In: Proceedings of the 5th European Conference on Speech Communication and Technology (Eurospeech) (1997)

    Google Scholar 

  3. Laroche, R., Putois, G., Bretier, P., Bouchon-Meunier, B.: Hybridisation of expertise and reinforcement learning in dialogue systems. In: Proceedings of the 9th Annual Conference of the International Speech Communication Association (Interspeech), pp. 2479–2482 (2009)

    Google Scholar 

  4. Lemon, O., Pietquin, O.: Data-Driven Methods for Adaptive Spoken Dialogue Systems: Computational Learning for Conversational Interfaces. Springer (2012)

    Google Scholar 

  5. English, M.S., Heeman, P.A.: Learning mixed initiative dialogue strategies by using reinforcement learning on both conversants. In: Proceedings of the Conference on Human Language Technology (HLT) (2005)

    Google Scholar 

  6. Georgila, K., Traum, D.R.: Reinforcement learning of argumentation dialogue policies in negotiation. In: Proceedings of the 11th Annual Conference of the International Speech Communication Association (Interspeech), pp. 2073–2076 (2011)

    Google Scholar 

  7. Bowling, M., Veloso, M.: Multiagent learning using a variable learning rate. Artif. Intell. 136(2), 215–250 (2002)

    Article  MathSciNet  MATH  Google Scholar 

  8. Georgila, K., Nelson, C., Traum, D.: Single-agent vs. multi-agent techniques for concurrent reinforcement learning of negotiation dialogue policies. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (ACL) (2014)

    Google Scholar 

  9. Shapley, L.S.: Stochastic games. Proc. Natl. Acad. Sci. U.S.A. 39(10), 1095 (1953)

    Article  MathSciNet  MATH  Google Scholar 

  10. Barlier, M., Perolat, J., Laroche, R., Pietquin, O.: Human-machine dialogue as a stochastic game. In: Proceedings of the 16th Annual Meeting of the Special Interest Group on Discourse and Dialogue (Sigdial) (2015)

    Google Scholar 

  11. Efstathiou, I., Lemon, O.: Learning non-cooperative dialogue behaviours. In: Proceedings of the 15th Annual Meeting of the Special Interest Group on Discourse and Dialogue (Sigdial)

    Google Scholar 

  12. Putois, G., Laroche, R., Bretier, P.: Online reinforcement learning for spoken dialogue systems: the story of a commercial deployment success. In: Proceedings of the 11th Annual Meeting of the Special Interest Group on Discourse and Dialogue, pp. 185–192. Citeseer (2010)

    Google Scholar 

  13. Laroche, R., Putois, G., Bretier, P., Aranguren, M., Velkovska, J., Hastie, H., Keizer, S., Yu, K., Jurcicek, F., Lemon, O., Young, S.: D6.4: final evaluation of classic towninfo and appointment scheduling systems. Report D6, 4 (2011)

    Google Scholar 

  14. El Asri, L., Lemonnier, R., Laroche, R., Pietquin, O., Khouzaimi, H.: Nastia: negotiating appointment setting interface. In: Proceedings of the 9th Edition of Language Resources and Evaluation Conference (LREC) (2014)

    Google Scholar 

  15. Genevay, A., Laroche, R.: Transfer learning for user adaptation in spoken dialogue systems. In: Proceedings of the 15th International Conference on Autonomous Agents and Multi-Agent Systems (AAMAS). International Foundation for Autonomous Agents and Multiagent Systems (2016)

    Google Scholar 

  16. Chandramohan, S., Geist, M., Lefèvre, F., Pietquin, O.: Co-adaptation in spoken dialogue systems. In: Proceedings of the 4th International Workshop on Spoken Dialogue Systems (IWSDS), p. 1. Paris, France (Nov 2012)

    Google Scholar 

  17. Casanueva, I., Hain, T., Christensen, H., Marxer, R., Green, P.: Knowledge transfer between speakers for personalised dialogue management. In: Proceedings of the 16th Annual Meeting of the Special Interest Group on Discourse and Dialogue (Sigdial) (2015)

    Google Scholar 

  18. Khouzaimi, H., Laroche, R., Lefevre, F.: Optimising turn-taking strategies with reinforcement learning. In: Proceedings of the 16th Annual Meeting of the Special Interest Group on Discourse and Dialogue (Sigdial) (2015)

    Google Scholar 

  19. Gordon, G.J.: Stable function approximation in dynamic programming. In: Proceedings of the 12th International Conference on Machine Learning (ICML) (1995)

    Google Scholar 

  20. Chandramohan, S., Geist, M., Pietquin, O.: Optimizing spoken dialogue management with fitted value iteration. In: Proceedings of the 10th Annual Conference of the International Speech Communication Association (Interspeech) (2010)

    Google Scholar 

  21. Ng, A.Y., Russell, S.: Algorithms for inverse reinforcement learning. In: Proceedings of the 17th International Conference on Machine Learning (ICML), pp. 663–670. Morgan Kaufmann (2000)

    Google Scholar 

  22. El Asri, L., Piot, B., Geist, M., Laroche, R., Pietquin, O.: Score-based inverse reinforcement learning. In: Proceedings of the 15th International Conference on Autonomous Agents and Multi-Agent Systems (AAMAS). International Foundation for Autonomous Agents and Multiagent Systems (2016)

    Google Scholar 

  23. Taylor, M.E., Stone, P.: Transfer learning for reinforcement learning domains: a survey. J. Mach. Learn. Res. 10, 1633–1685 (2009)

    MathSciNet  MATH  Google Scholar 

  24. Lazaric, A.: Transfer in reinforcement learning: a framework and a survey. In: Reinforcement Learning, pp. 143–173. Springer (2012)

    Google Scholar 

  25. Gašic, M., Breslin, C., Henderson, M., Kim, D., Szummer, M., Thomson, B., Tsiakoulis, P., Young, S.: POMDP-based dialogue manager adaptation to extended domains. In: Proceedings of the 14th Annual Meeting of the Special Interest Group on Discourse and Dialogue (Sigdial) (2013)

    Google Scholar 

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Correspondence to Romain Laroche .

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Laroche, R., Genevay, A. (2017). The Negotiation Dialogue Game. In: Jokinen, K., Wilcock, G. (eds) Dialogues with Social Robots. Lecture Notes in Electrical Engineering, vol 427. Springer, Singapore. https://doi.org/10.1007/978-981-10-2585-3_33

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  • DOI: https://doi.org/10.1007/978-981-10-2585-3_33

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-10-2584-6

  • Online ISBN: 978-981-10-2585-3

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