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首页> 外文期刊>International Journal of Hybrid Intelligent Systems >Combining affective intelligence with learning to improve action selection in decision-making agents
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Combining affective intelligence with learning to improve action selection in decision-making agents

机译：将情商与学习相结合，以改善决策者的行动选择

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摘要

Complex environments contain more information than either natural or artificial agents can fully process in a timely manner. Studies in neuroscience have demonstrated that natural agents utilize affect (or emotion) to filter out irrelevant inputs. In this work, we propose to integrate an affect filtering mechanism in artificial agents to improve the deliberation time for action selection in environment containing a massive number of selection options. To evaluate this model, we create two agent architectures: the first architecture is based on an active reinforcement learning algorithm and the second architecture utilizes a hybrid design with both active reinforcement learning and the affect-based filtering mechanism. We have compared the deliberation time and the overall utility score of these two agents in the same environment. The results showed that the affect-based filtering mechanism is effective in decreasing the deliberation time without compromising the agent’s utility score. The results from this study strengthen the premise that affect plays an important role in intelligent behavior.
机译：复杂的环境所包含的信息超出了自然或人工代理能够及时全面处理的范围。神经科学研究表明，自然因素会利用情感（或情感）来滤除无关紧要的输入。在这项工作中，我们建议在人工代理中集成一种影响过滤机制，以改善在包含大量选择选项的环境中进行动作选择的审议时间。为了评估该模型，我们创建了两个代理架构：第一个架构基于主动强化学习算法，第二个架构利用混合设计，同时具有主动强化学习和基于情感的过滤机制。我们已经比较了这两个代理在相同环境中的审议时间和总体效用得分。结果表明，基于情感的过滤机制可有效缩短审议时间，而不会影响代理的效用得分。这项研究的结果加强了前提，即情感在智能行为中起着重要作用。

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