多智能体系统中情绪和气质的计算研究

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摘要翻译：
神经科学和心理学的最新进展提供了证据，表明情感现象在智力的许多层面上都存在，与认知行为回路密不可分。知觉、注意、记忆、学习、决策、适应、沟通和社会交往等方面受到影响。本研究从神经生物学、心理物理学和社会学等学科中得到启发，研究了自主机器人的构建问题，并提出了基于情绪决策机制的自主机器人的构建策略。情感建模是人工智能和认知建模中的一个相对较新的焦点。这样的模型可以理想地告知我们对人类行为的理解。我们可以看到情感计算模型的发展是一个核心研究焦点，它将促进建模、解释或影响人类行为的大量计算系统的进步。我们提出了一个基于可伸缩、灵活和模块化的情感模型，它允许在情感质量和性能之间进行运行时评估。研究结果表明，基于情绪决策机制的策略对系统性能有很强的影响，并且Agent的情绪状态与其情绪类型以及团队成员的情绪结构之间存在明显的相关性，从而表明基于情绪理论的模块化情绪规划方法是建立情绪行为多Agent系统计算思维模型的良好选择。
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英文标题：
《A Computational Study on Emotions and Temperament in Multi-Agent Systems》
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作者：
Luis Paulo Reis, Daria Barteneva, Nuno Lau
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最新提交年份：
2008
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分类信息：

一级分类：Computer Science        计算机科学
二级分类：Artificial Intelligence        人工智能
分类描述：Covers all areas of AI except Vision, Robotics, Machine Learning, Multiagent Systems, and Computation and Language (Natural Language Processing), which have separate subject areas. In particular, includes Expert Systems, Theorem Proving (although this may overlap with Logic in Computer Science), Knowledge Representation, Planning, and Uncertainty in AI. Roughly includes material in ACM Subject Classes I.2.0, I.2.1, I.2.3, I.2.4, I.2.8, and I.2.11.
涵盖了人工智能的所有领域，除了视觉、机器人、机器学习、多智能体系统以及计算和语言（自然语言处理），这些领域有独立的学科领域。特别地，包括专家系统，定理证明（尽管这可能与计算机科学中的逻辑重叠），知识表示，规划，和人工智能中的不确定性。大致包括ACM学科类I.2.0、I.2.1、I.2.3、I.2.4、I.2.8和I.2.11中的材料。
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一级分类：Computer Science        计算机科学
二级分类：Multiagent Systems        多智能体系统
分类描述：Covers multiagent systems, distributed artificial intelligence, intelligent agents, coordinated interactions. and practical applications. Roughly covers ACM Subject Class I.2.11.
涵盖多Agent系统、分布式人工智能、智能Agent、协调交互。和实际应用。大致涵盖ACM科目I.2.11类。
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一级分类：Computer Science        计算机科学
二级分类：Robotics        机器人学
分类描述：Roughly includes material in ACM Subject Class I.2.9.
大致包括ACM科目I.2.9类的材料。
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英文摘要：
  Recent advances in neurosciences and psychology have provided evidence that affective phenomena pervade intelligence at many levels, being inseparable from the cognitionaction loop. Perception, attention, memory, learning, decisionmaking, adaptation, communication and social interaction are some of the aspects influenced by them. This work draws its inspirations from neurobiology, psychophysics and sociology to approach the problem of building autonomous robots capable of interacting with each other and building strategies based on temperamental decision mechanism. Modelling emotions is a relatively recent focus in artificial intelligence and cognitive modelling. Such models can ideally inform our understanding of human behavior. We may see the development of computational models of emotion as a core research focus that will facilitate advances in the large array of computational systems that model, interpret or influence human behavior. We propose a model based on a scalable, flexible and modular approach to emotion which allows runtime evaluation between emotional quality and performance. The results achieved showed that the strategies based on temperamental decision mechanism strongly influence the system performance and there are evident dependency between emotional state of the agents and their temperamental type, as well as the dependency between the team performance and the temperamental configuration of the team members, and this enable us to conclude that the modular approach to emotional programming based on temperamental theory is the good choice to develop computational mind models for emotional behavioral Multi-Agent systems.
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PDF链接：
https://arxiv.org/pdf/0809.4784

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