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How Human Inspired Learning Enhances the Behavior of Autonomous Agents

机译：人类灵感学习如何增强自治代理的行为

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An autonomous agent must deal with unforeseen situations that can’t be preprogrammed. Therefore, the agent has to make its own experiences, solutions and valuations to situations, actions and objects to be able to enhance previous actions and avoid repeating wrong actions. On the basis of the existing cognitive architecture Simulation of Mental Apparatus and Applications (SiMA) at the Institute of Computer Technology (ICT) an bionically inspired attempt of learning should be implemented in functional model of the human mind which is then used in a multi-agent simulation showing how bionically inspired cognitive architectures can get extended by learning. Due to the attempt in the project SiMA the learning function has to fit in the psychoanalytic model and therefore it has to be compatible with the way of learning that human beings do. This might also help to get a little bit closer to the understanding of how the human mind manipulates memory to show these until now unreached cognitive abilities.
机译：自主代理必须处理无法预先编程的不可预见的情况。因此，该代理必须为能力，行动和对象进行自己的体验，解决方案和估值，以便能够增强以前的行动，并避免重复错误的行动。在计算机技术研究所（ICT）在计算机技术（ICT）的现有认知体系结构模拟（ICT）的基础上，可以在人类思维的功能模型中实现仿效学习的尝试，然后在多个 - 代理仿真显示如何通过学习延长了仿制性认知架构。由于项目的尝试，Sima的学习功能必须适应精神分析模型，因此它必须与学习人类所做的方式兼容。这也可能有助于了解对人类思维如何操纵内存的理解，以显示这些直到现在不和谐的认知能力。

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