情绪机制如何帮助认知主体的情景学习

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摘要翻译：
本文提出了CTS（Concious Tutoring System）技术，这是一种基于人脑功能的生物学上可信的认知agent。该agent能够学习和记忆事件以及任何相关信息，如相应的过程、刺激及其情感价值。我们提出的情景记忆和情景学习机制更接近神经科学中当前的多迹理论，因为它们受到它的启发[5]而不是与认知主体结合的其他机制。这是因为在我们的模型中，情绪在事件的编码和记忆中起着作用。这允许代理通过记住先前选择的受其情感机制影响的行为来改善其行为。此外，该体系结构结合了一个基于数据挖掘算法的实际内存整合过程。
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英文标题：
《How Emotional Mechanism Helps Episodic Learning in a Cognitive Agent》
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作者：
Usef Faghihi, Philippe Fournier-Viger, Roger Nkambou, Pierre Poirier,
  Andre Mayers
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最新提交年份：
2009
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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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英文摘要：
  In this paper we propose the CTS (Concious Tutoring System) technology, a biologically plausible cognitive agent based on human brain functions.This agent is capable of learning and remembering events and any related information such as corresponding procedures, stimuli and their emotional valences. Our proposed episodic memory and episodic learning mechanism are closer to the current multiple-trace theory in neuroscience, because they are inspired by it [5] contrary to other mechanisms that are incorporated in cognitive agents. This is because in our model emotions play a role in the encoding and remembering of events. This allows the agent to improve its behavior by remembering previously selected behaviors which are influenced by its emotional mechanism. Moreover, the architecture incorporates a realistic memory consolidation process based on a data mining algorithm.
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PDF链接：
https://arxiv.org/pdf/0901.4963

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