SensAI +通过认知和记忆整合扩展情绪效价预测研究
SensAI+Expanse Emotional Valence Prediction Studies with Cognition and Memory Integration
摘要：
人类是情感和认知的生物，依靠记忆来获得个人和社会身份。此外，人类的二元关系需要一些共同的信念，如移情行为，以更好地互动。从这个意义上说，涉及人-主体互动的研究应该以情感、认知和记忆整合为基础。开发的人工智能体系统（SensAI+Expanse）包括机器学习算法，语言学和记忆，作为对相互作用的人的情感效价预测的认知辅助工具。此外，始终存在自适应共情分数，以便使人类参与可识别的交互结果。[...]智能体在收集数据时具有弹性，在学习中尽最大努力使其认知过程适应每个人类个体，以进行适当的情境化预测。目前的研究利用一个实现的自适应过程。此外，使用个人预测模型与特定选项的学习算法和评价指标，从以前的研究。完成的解决方案包括一个高性能的预测能力，一个有效的能源使用，和预测概率的特征重要性解释。本研究的结果表明，一些年龄范围和性别组合之间的显着的情绪效价行为差异的证据。因此，这项工作有助于人工智能代理能够协助认知科学研究。这种能力是通过预测人类在空间和时间背景下的情感效价来影响情感障碍。此外，有助于学习过程和技能适合的任务，包括经济的认知和记忆，以应对环境。最后，这些贡献包括在预测情绪效价状态方面实现了年龄和性别中立，并且每个人都有很好的表现。
英文摘要：
The humans are affective and cognitive beings relying on memories for their individual and social identities. Also, human dyadic bonds require some common beliefs such as empathetic behaviour for better interaction. In this sense, research studies involving human-agent interaction should resource on affect, cognition, and memory integration. The developed artificial agent system (SensAI+Expanse) includes machine learning algorithms, heuristics, and memory as cognition aids towards emotional valence prediction on the interacting human. Further, an adaptive empathy score is always present in order to engage the human in a recognisable interaction outcome. [...] The agent is resilient on collecting data, adapts its cognitive processes to each human individual in a learning best effort for proper contextualised prediction. The current study make use of an achieved adaptive process. Also, the use of individual prediction models with specific options of the learning algorithm and evaluation metric from a previous research study. The accomplished solution includes a highly performant prediction ability, an efficient energy use, and feature importance explanation for predicted probabilities. Results of the present study show evidence of significant emotional valence behaviour differences between some age ranges and gender combinations. Therefore, this work contributes with an artificial intelligent agent able to assist on cognitive science studies. This ability is about affective disturbances by means of predicting human emotional valence contextualised in space and time. Moreover, contributes with learning processes and heuristics fit to the task including economy of cognition and memory to cope with the environment. Finally, these contributions include an achieved age and gender neutrality on predicting emotional valence states in context and with very good performance for each individual.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
