Evaluating Persuasion Strategies and Deep Reinforcement Learning methods for Negotiation Dialogue agents

Simon Keizer; Markus Guhe; Heriberto Cuayahuitl; Ioannis Efstathiou; Klaus-Peter Engelbrecht; Mihai Dobre; Alex Lascarides; Oliver Lemon

Evaluating Persuasion Strategies and Deep Reinforcement Learning methods for Negotiation Dialogue agents

Simon Keizer, Markus Guhe, Heriberto Cuayahuitl, Ioannis Efstathiou, Klaus-Peter Engelbrecht, Mihai Dobre, Alex Lascarides, Oliver Lemon

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

46 Citations (Scopus)

43 Downloads (Pure)

Abstract

In this paper we present a comparative evaluation of various negotiation strategies within an online version of the game “Settlers of Catan”. The comparison is based on human subjects playing games against artificial game-playing agents (‘bots’) which implement different negotiation dialogue strategies, using a chat dialogue interface to negotiate trades. Our results suggest that a negotiation strategy that uses persuasion, as well as a strat- egy that is trained from data using Deep Reinforcement Learning, both lead to an improved win rate against humans, compared to previous rule-based and supervised learning baseline dialogue negotiators.

Original language	English
Title of host publication	Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics
Subtitle of host publication	Volume 2, Short Papers
Publisher	Association for Computational Linguistics
Pages	480-484
Number of pages	5
ISBN (Print)	9781945626357
Publication status	Published - 1 Apr 2017
Event	15th Conference of the European Chapter of the Association for Computational Linguistics 2017 - Valencia, Spain Duration: 3 Apr 2017 → 7 Apr 2017

Conference

Conference	15th Conference of the European Chapter of the Association for Computational Linguistics 2017
Abbreviated title	EACL 2017
Country/Territory	Spain
City	Valencia
Period	3/04/17 → 7/04/17

Keywords

Dialogue systems
Machine learning
Negotiation

ASJC Scopus subject areas

Artificial Intelligence

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Keizer, S., Guhe, M., Cuayahuitl, H., Efstathiou, I., Engelbrecht, K.-P., Dobre, M., Lascarides, A., & Lemon, O. (2017). Evaluating Persuasion Strategies and Deep Reinforcement Learning methods for Negotiation Dialogue agents. In Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 2, Short Papers (pp. 480-484). Association for Computational Linguistics. http://aclweb.org/anthology/E/E17/E17-2077.pdf

@inproceedings{1005151f2215439ab65d798c4ae10e6f,

title = "Evaluating Persuasion Strategies and Deep Reinforcement Learning methods for Negotiation Dialogue agents",

abstract = "In this paper we present a comparative evaluation of various negotiation strategies within an online version of the game “Settlers of Catan”. The comparison is based on human subjects playing games against artificial game-playing agents ({\textquoteleft}bots{\textquoteright}) which implement different negotiation dialogue strategies, using a chat dialogue interface to negotiate trades. Our results suggest that a negotiation strategy that uses persuasion, as well as a strat- egy that is trained from data using Deep Reinforcement Learning, both lead to an improved win rate against humans, compared to previous rule-based and supervised learning baseline dialogue negotiators.",

keywords = "Dialogue systems, Machine learning, Negotiation",

author = "Simon Keizer and Markus Guhe and Heriberto Cuayahuitl and Ioannis Efstathiou and Klaus-Peter Engelbrecht and Mihai Dobre and Alex Lascarides and Oliver Lemon",

year = "2017",

month = apr,

day = "1",

language = "English",

isbn = "9781945626357",

pages = "480--484",

booktitle = "Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics",

publisher = "Association for Computational Linguistics",

note = "15th Conference of the European Chapter of the Association for Computational Linguistics 2017, EACL 2017 ; Conference date: 03-04-2017 Through 07-04-2017",

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Keizer, S, Guhe, M, Cuayahuitl, H, Efstathiou, I, Engelbrecht, K-P, Dobre, M, Lascarides, A & Lemon, O 2017, Evaluating Persuasion Strategies and Deep Reinforcement Learning methods for Negotiation Dialogue agents. in Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 2, Short Papers. Association for Computational Linguistics, pp. 480-484, 15th Conference of the European Chapter of the Association for Computational Linguistics 2017, Valencia, Spain, 3/04/17. <http://aclweb.org/anthology/E/E17/E17-2077.pdf>

Evaluating Persuasion Strategies and Deep Reinforcement Learning methods for Negotiation Dialogue agents. / Keizer, Simon; Guhe, Markus; Cuayahuitl, Heriberto et al.
Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 2, Short Papers. Association for Computational Linguistics, 2017. p. 480-484.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

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T1 - Evaluating Persuasion Strategies and Deep Reinforcement Learning methods for Negotiation Dialogue agents

AU - Keizer, Simon

AU - Guhe, Markus

AU - Cuayahuitl, Heriberto

AU - Efstathiou, Ioannis

AU - Engelbrecht, Klaus-Peter

AU - Dobre, Mihai

AU - Lascarides, Alex

AU - Lemon, Oliver

PY - 2017/4/1

Y1 - 2017/4/1

N2 - In this paper we present a comparative evaluation of various negotiation strategies within an online version of the game “Settlers of Catan”. The comparison is based on human subjects playing games against artificial game-playing agents (‘bots’) which implement different negotiation dialogue strategies, using a chat dialogue interface to negotiate trades. Our results suggest that a negotiation strategy that uses persuasion, as well as a strat- egy that is trained from data using Deep Reinforcement Learning, both lead to an improved win rate against humans, compared to previous rule-based and supervised learning baseline dialogue negotiators.

AB - In this paper we present a comparative evaluation of various negotiation strategies within an online version of the game “Settlers of Catan”. The comparison is based on human subjects playing games against artificial game-playing agents (‘bots’) which implement different negotiation dialogue strategies, using a chat dialogue interface to negotiate trades. Our results suggest that a negotiation strategy that uses persuasion, as well as a strat- egy that is trained from data using Deep Reinforcement Learning, both lead to an improved win rate against humans, compared to previous rule-based and supervised learning baseline dialogue negotiators.

KW - Dialogue systems

KW - Machine learning

KW - Negotiation

M3 - Conference contribution

SN - 9781945626357

SP - 480

EP - 484

BT - Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics

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ER -

Keizer S, Guhe M, Cuayahuitl H, Efstathiou I, Engelbrecht KP, Dobre M et al. Evaluating Persuasion Strategies and Deep Reinforcement Learning methods for Negotiation Dialogue agents. In Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 2, Short Papers. Association for Computational Linguistics. 2017. p. 480-484

Evaluating Persuasion Strategies and Deep Reinforcement Learning methods for Negotiation Dialogue agents

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