Multi-party Human-computer Interaction Dialogue Psychology Model Based on Stackelberg Game
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摘要: 针对现有的多方人机交互存在分寸感把握较差、机器人回复自主性不强的问题，该文依据心理语言学关于对话心理理论，提出一种基于主从博弈的多方人机交互对话心理模型。该模型模拟了人与人交流时交互多方的心理过程，考虑到多方交互中主导者与从属者的交流特征，采取单主多从的博弈模型加以形式化表示，并使机器人扮演从属者角色，对多方主从博弈过程中从属关系带来的收益进行考量，将这一考量结果作为机器人回复的重要决策依据。实验结果表明，扮演从属者角色的机器人在与多方进行交互时，能准确把握对话分寸，在合适的时机下进行回复，进一步提升机器人回复的合理性与自主性。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.
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表 1 收益值设置
角色类型 收益值 策略 主导者主导者 101 作为话题的发起方，展开话题作为话题的结束方，结束话题 从属者 10 发表高质量、主题相关的话语 从属者 1 发表低质量、主题无关的话语 
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算法1 基于主从博弈的多方人机交互对话心理模型 输入： $ k $次会话多方的输入内容 $ \left\{ {{C_1},{C_2},\cdots,{C_n}} \right\} $，机器人的角色 定位初始化为从属者 ${S_0}$； 输出： $ k{\text{ + 1}} $次会话时机器人的回复策略； (1) Repeat: (2)多方输入内容 $ \left\{ {{C_1},{C_2},\cdots,{C_n}} \right\} $； (3) 根据式(2)—式(11)分析多方的对话策略，计算多方的从属度； (4) 根据式(12)得到博弈方的策略-收益函数 $ Q $； (5) 根据式(13)—式(15)计算机器人作为从属者的最优回复策 略 $ \pi $； (6) 通过最优回复策略 $ \pi $得到机器人的下一次回复行为 $ A $； (7) 当机器人需要参与对话时，机器人在下一次会话中做出响 应回复； (8) 令 $ k = k{\text{ + 1}} $； (9) Until 多方停止交互输入； (10) 多方人机交互会话终止；
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表 2 友好性和合理评价标准
友好性 评价指标 +2 内容相关性好，连贯性强，语言表达自然流畅， 符合人的说话习惯 +1 内容勉强相关，连贯性一般，语言表达正确 +0 内容勉强相关，语言表达有些错误 –1 内容相关性很低，语言表达出现较多错误 –2 内容不相关，答非所问 合理性 评价指标 +2 回复的频率适度 +1 回复频率较高或较低 +0 需要用户重复对话才能理解 –1 强行打断用户之间的互动交流 –2 长时间的“尴尬”沉默
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表 3 不同对比模型的自动评测结果
模型 MRR MAP CATD 0.525 0.548 GSN 0.517 0.545 ChatterBot 0.452 0.483 MIDS 0.639 0.674 本文 0.656 0.687
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表 5 志愿者与模型交互轮数和时间统计
模型 平均交互轮数 平均交互时间(s) CATD 4.68 84.29 GSN 5.86 82.98 ChatterBot 2.95 73.68 MIDS 6.18 110.27 本文 9.45 122.58
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表 6 志愿者对各模型友好性和合理性打分统计
模型 友好性 合理性 CATD 1.42 0.87 GSN 1.38 1.21 ChatterBot 1.22 0.72 MIDS 1.60 1.38 本文 1.58 1.47
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