A Computational Study on Emotions and Temperament in Multi-Agent Systems
A Computational Study on Emotions and Temperament in Multi-Agent Systems
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多智能体系统中情绪和气质的计算研究
DOI：
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发表时间：
2008
期刊：
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通讯作者：
N. Lau
中科院分区：
文献类型：
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作者：
Luis Paulo Reis;Daria Barteneva;N. Lau
神经科学和心理学的最新进展提供了证据，表明情感现象渗透到智力的许多层面，与认知-动作循环密不可分。感知、注意力、记忆、学习、决策、适应、沟通和社会互动是受其影响的一些方面。本论文从神经生物学、心理物理学和社会学等领域汲取灵感，探讨了构建自主机器人的问题，这些机器人能够相互交互，并基于气质决策机制构建策略。情感建模是人工智能和认知建模中相对较新的焦点。这些模型可以理想地为我们理解人类行为提供信息。我们可能会看到情绪计算模型的发展作为一个核心的研究重点，这将促进在大量的计算系统，模型，解释或影响人类行为的进步。我们提出了一个模型的基础上，一个可扩展的，灵活的和模块化的方法，情感，允许运行时的情感质量和性能之间的评估。研究结果表明，基于气质决策机制的策略对系统绩效有显著的影响，Agent的情绪状态与其气质类型、团队绩效与团队成员的气质结构之间存在明显的相关性，这使我们能够得出结论，基于气质理论的情绪编程模块化方法是开发情绪行为多计算心智模型的良好选择。代理系统。
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.