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首页> 外文会议>2015 International Conference on Affective Computing and Intelligent Interaction >A socio-cognitive approach to personality: Machine-learned game strategies as cues of regulatory focus
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A socio-cognitive approach to personality: Machine-learned game strategies as cues of regulatory focus

机译：对人格的社会认知方法：机器学习的游戏策略作为监管重点的线索

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摘要

Artificial agents are becoming artificial companions, interacting with the user on a long-term basis. This evolution brought new challenges to the affective computing domain, such as designing artificial agents with personalities to the benefits of the user. Endowing artificial agents with personality could help to increase the agent's believability, hence easing the interaction. This paper touches on two questions pertaining to computational personality modeling: 1/ how to produce artificial personalities which can inform personality researchers, whether from computer sciences or psychology and 2/ will behaviors produced by artificial agents be perceived by users as putting the programmed personality across as such. We propose to use a data-driven approach to endow artificial agents with personality, using the regulatory focus theory as a framework. We used machine-learned game strategies, in the form of alternative decision trees computed from human data, to convey the personality of artificial agents. We then tested whether these personalities can be perceived by users after playing a game against these agents. We used two artificial agents as controls: one randomly playing and one with an ???average / depersonalized??? strategy. On the one hand, our results show that agents' regulatory focus, when programmed, can be accurately perceived by users. On the other hand, our results also point out that personality will be perceived by users even if the agent's design does not intend to transmit one.
机译：人工代理正在成为人工伴侣，与用户进行长期互动。这种发展给情感计算领域带来了新的挑战，例如设计具有个性的人工代理以使用户受益。赋予人为特质以个性可以帮助增加特工的可信度，从而简化互动。本文涉及与计算人格建模有关的两个问题：1 /如何产生可以通知人格研究人员的人工人格，无论是计算机科学还是心理学方面的知识； 2 //用户是否会将人工代理产生的行为视为将编程的人格化因此。我们建议使用数据驱动的方法，以监管重点理论为框架，赋予人造代理人个性。我们使用了机器学习的博弈策略，以根据人类数据计算出的替代决策树的形式来传达人工代理的个性。然后，我们测试了在与这些特工进行游戏之后，用户是否可以感知这些个性。我们使用了两种人工代理作为控制：一种是随机玩游戏，另一种是“平均/非个性化”游戏。战略。一方面，我们的结果表明，通过编程，代理商的监管重点可以被用户准确地感知到。另一方面，我们的结果还指出，即使代理人的设计不打算传递个性，用户也会感觉到个性。

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