追踪情绪：基于多层次预测误差动力学的内在动机
Tracking Emotions: Intrinsic Motivation Grounded on Multi-Level Prediction Error Dynamics
摘要：
认知主体如何决定要学习的相关信息是什么，以及如何选择目标来获得这些知识？认知主体需要被激励来执行任何动作。我们讨论的情绪出现时，预期和实际的进展速度之间的差异，朝着一个目标的经验。因此，预测误差动态的跟踪与情绪有着紧密的关系。在这里，我们建议，跟踪预测误差动态允许人工智能体的内在动机，以寻求新的经验，但限制到那些产生可减少的预测误差，我们提出了一个内在的动机架构，产生的行为对自我生成的和动态的目标，并调节目标选择和开发和探索之间的平衡，通过多层次的监测预测误差动态。这种新的体系结构调节探索噪声，并根据学习系统的整体性能的动态来利用计算资源。此外，它建立了一个可能的解决方案的时间动态的目标选择。这里提出的实验结果表明，这种架构优于内在动机的方法，其中探索噪声和目标是固定的，并应用贪婪的策略。
英文摘要：
How do cognitive agents decide what is the relevant information to learn and how goals are selected to gain this knowledge? Cognitive agents need to be motivated to perform any action. We discuss that emotions arise when differences between expected and actual rates of progress towards a goal are experienced. Therefore, the tracking of prediction error dynamics has a tight relationship with emotions. Here, we suggest that the tracking of prediction error dynamics allows an artificial agent to be intrinsically motivated to seek new experiences but constrained to those that generate reducible prediction error.We present an intrinsic motivation architecture that generates behaviors towards self-generated and dynamic goals and that regulates goal selection and the balance between exploitation and exploration through multi-level monitoring of prediction error dynamics. This new architecture modulates exploration noise and leverages computational resources according to the dynamics of the overall performance of the learning system. Additionally, it establishes a possible solution to the temporal dynamics of goal selection. The results of the experiments presented here suggest that this architecture outperforms intrinsic motivation approaches where exploratory noise and goals are fixed and a greedy strategy is applied.
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。
