Multi-party Human-computer Interaction Dialogue Psychology Model Based on Stackelberg Game

HUANG Hongcheng; SU Meidan; KOU Lan; TAO Yang; HU Min

doi:10.11999/JEIT220441

Volume 45 Issue 5

May 2023

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HUANG Hongcheng, SU Meidan, KOU Lan, TAO Yang, HU Min. Multi-party Human-computer Interaction Dialogue Psychology Model Based on Stackelberg Game[J]. Journal of Electronics & Information Technology, 2023, 45(5): 1758-1765. doi: 10.11999/JEIT220441

Citation:

HUANG Hongcheng, SU Meidan, KOU Lan, TAO Yang, HU Min. Multi-party Human-computer Interaction Dialogue Psychology Model Based on Stackelberg Game[J]. Journal of Electronics & Information Technology, 2023, 45(5): 1758-1765. doi: 10.11999/JEIT220441

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Multi-party Human-computer Interaction Dialogue Psychology Model Based on Stackelberg Game

doi: 10.11999/JEIT220441

HUANG Hongcheng^{1, 2
,
,},
SU Meidan¹,
KOU Lan¹,
TAO Yang^{1, 2},
HU Min¹

1.
School of Communication and Information Engineering, Chongqing University of Posts and Telecommunications, Chongqing 400065, China
2.
Chongqing Engineering Research Center of Communication Software, Chongqing 400065, China

Funds: The National Natural Science Foundation of China (61871062)

Received Date: 2021-04-13
Rev Recd Date: 2022-06-17

Available Online: 2022-06-24

Publish Date: 2023-05-10

Abstract

Abstract

Considering the problems of the existing in the process of multi-party human-computer interaction system, such as lack of propriety and low autonomy, a dialogue psychological model based on Stackelberg game is proposed in this paper. The multi-party human-computer interaction model is used to simulate the psychological game process in interpersonal communication. Taking into account the communication characteristics between the leader and the follower in the multi-party interaction, the game model of single leader and multiple followers is adopted to formalize it. The robot is played the role of the follower and considered the benefit brought by subordinate relationship in the multi-party Stackelberg game to make the effective decision-making strategy. The experimental result shows that the robot played the role of follower is polite and replied at the right time in communication with the multi-party, which improves further the rationality and autonomy in response for the robot.
- Human-computer interaction,
- Multi-party interaction,
- Dialogue psychology,
- Stackelberg game

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References(23)

References

[1]	陈鑫, 周强. 开放型对话技术研究综述[J]. 中文信息学报, 2021, 35(11): 1–12. CHEN Xin and ZHOU Qiang. A survey of research on open domain dialogue systems[J]. Journal of Chinese Information Processing, 2021, 35(11): 1–12.
[2]	ZHU Qingfu, CUI Lei, ZHANG Weinan, et al. Retrieval-enhanced adversarial training for neural response generation[C]. The 57th Annual Meeting of the Association for Computational Linguistics, Florence, Italy, 2019: 3763–3773.
[3]	UTHUS D C and AHA D W. Multiparticipant chat analysis: A survey[J]. Artificial Intelligence, 2013, 199/200: 106–121. doi: 10.1016/j.artint.2013.02.004
[4]	张开颜, 张伟男, 刘挺. 基于深度学习的多方对话研究综述[J]. 中国科学:信息科学, 2021, 51(8): 1217–1232. doi: 10.1360/SSI-2020-0176 ZHANG Kaiyan, ZHANG Weinan, and LIU Ting. A survey of multi-party dialogue research based on deep learning[J]. Scientia Sinica Informationis, 2021, 51(8): 1217–1232. doi: 10.1360/SSI-2020-0176
[5]	KENNINGTON C, FUNAKOSHI K, TAKAHASHI Y, et al. Probabilistic multiparty dialogue management for a game master robot[C]. 2014 ACM/IEEE International Conference on Human-Robot Interaction, Bielefeld, Germany, 2014: 200–201.
[6]	ŻARKOWSKI M. Multi-party turn-taking in repeated human-robot interactions: An interdisciplinary evaluation[J]. International Journal of Social Robotics, 2019, 11(5): 693–707. doi: 10.1007/s12369-019-00603-1
[7]	DE BAYSER M G, GUERRA M A, CAVALIN P, et al. Specifying and implementing multi-party conversation rules with finite-state-automata[C]. The Thirty-Second AAAI Conference on Artificial Intelligence, New Orleans, USA, 2018: 713–719.
[8]	MALIK U, SAUNIER J, FUNAKOSHI K, et al. Who speaks next? Turn change and next speaker prediction in multimodal multiparty interaction[C]. The 2020 IEEE 32nd International Conference on Tools with Artificial Intelligence, Baltimore, USA, 2020: 349–354.
[9]	LE Ran, HU Wenpeng, SHANG Mingyue, et al. Who is speaking to whom? Learning to identify utterance addressee in multi-party conversations[C]. The 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing, Hong Kong, China, 2019: 1909–1919.
[10]	TAN Ming, WANG Dakuo, GAO Yupeng, et al. Context-aware conversation thread detection in multi-Party Chat[C]. 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing, Hong Kong, China, 2019: 6456–6461.
[11]	HU Wenpeng, CHAN Zhangming, LIU Bing, et al. GSN: A graph-structured network for multi-party dialogues[C]. The 28th International Joint Conference on Artificial Intelligence, Macao, China, 2019: 5010–5016.
[12]	DZIRI N, KAMALLOO E, MATHEWSON K, et al. Augmenting neural response generation with context-aware topical attention[C]. The First Workshop on NLP for Conversational AI, Florence, Italy, 2019: 18–31.
[13]	成驰. 一类基于Stackelberg博弈的多智能体强化学习算法[D]. [硕士论文], 南京大学, 2017. CHENG Chi. A multi-agent reinforcement learning algorithm based on stackelberg game[D]. [Master dissertation], Nanjing University, 2017.
[14]	华生. 欲望心理学: 人际交往中的心理博弈[M]. 北京: 中央编译出版社, 2016: 1–5. HUA Sheng. Psychology on Desire: Psychological Game in Interpersonal Communication[M]. Beijing: Central Compilation & Translation Press, 2016: 1–5.
[15]	赵姝, 刘晓曼, 段震, 等. 社交关系挖掘研究综述[J]. 计算机学报, 2017, 40(3): 535–555. doi: 10.11897/SP.J.1016.2017.00535 ZHAO Shu, LIU Xiaoman, DUAN Zhen, et al. A survey on social ties mining[J]. Chinese Journal of Computers, 2017, 40(3): 535–555. doi: 10.11897/SP.J.1016.2017.00535
[16]	韩程程, 李磊, 刘婷婷, 等. 语义文本相似度计算方法[J]. 华东师范大学学报:自然科学版, 2020(5): 95–112. doi: 10.3969/j.issn.1000-5641.202091011 HAN Chengcheng, LI Lei, LIU Tingting, et al. Approaches for semantic textual similarity[J]. Journal of East China Normal University: Natural Science, 2020(5): 95–112. doi: 10.3969/j.issn.1000-5641.202091011
[17]	HASAN M, RAHMAN A, KARIM R, et al. Normalized approach to find optimal number of topics in Latent Dirichlet allocation (LDA)[M]. KAISER M S, BANDYOPADHYAY A, MAHMUD M, et al. Proceedings of International Conference on Trends in Computational and Cognitive Engineering. Singapore: Springer, 2021: 341–354.
[18]	SUN Xiang, LIU Lu, AYORINDE A, et al. ED-SWE: Event detection based on scoring and word embedding in online social networks for the internet of people[J]. Digital Communications and Networks, 2021, 7(4): 559–569. doi: 10.1016/j.dcan.2021.03.006
[19]	LU Xin, DENG Yao, SUN Ting, et al. MKPM: Multi keyword-pair matching for natural language sentences[J]. Applied Intelligence, 2022, 52(2): 1878–1892. doi: 10.1007/s10489-021-02306-5
[20]	SHANMUGAPRIYA P and MARIMUTHU H. Development of chatterbot using python[J]. International Journal of Computer Applications, 2020, 176(21): 18–20. doi: 10.5120/ijca2020920184
[21]	YOUFOU. Wxpy messages[EB/OL]. https://wxpy.readthedocs.io/zh/latest/messages.html, 2021.
[22]	YANG Qichuan, HE Zhiqiang, ZHAN Zhiqiang, et al. End-to-End personalized humorous response generation in untrimmed multi-role dialogue system[J]. IEEE Access, 2019, 7: 94059–94071. doi: 10.1109/ACCESS.2019.2926830
[23]	WU Yu, WU Wei, XING Chen, et al. Sequential matching network: A new architecture for multi-turn response selection in retrieval-based chatbots[C]. The 55th Annual Meeting of the Association for Computational Linguistics, Vancouver, Canada, 2017: 496–505.