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Formulation of a lightweight hybrid AI algorithm towards self-learning autonomous systems

机译：针对自学自主系统的轻量级混合AI算法的制定

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摘要

Autonomous systems able to react and change their behaviour in response to events during operation. These established abilities are based on the preprogrammed action or actions to be taken when encounter certain states in the deployed environment. Therefore prior to deployment, a knowledge expert with comprehensive understanding of the physical system and the deployed environment must specify anticipated states and determine actions to be taken to be programmed. As an alternative, a system with self-learning capabilities allows the system to autonomously identify, differentiate and classify states and progressively determine actions. In this paper, we present, summarize and discuss the formulation of a hybrid AI algorithm which combines Q-learning and AUTOWiSARD algorithm that will allow an autonomous system to self-learn. The hybrid AI algorithm will be implemented in an autonomous mobile robot simulation and the outcome will be presented and discussed.
机译：自主系统能够在操作期间响应事件而反应和改变其行为。这些既定能力基于在部署环境中遇到某些状态时要采取的预编程行动或行动。因此，在部署之前，具有全面了解物理系统和部署环境的知识专家必须指定预期的状态并确定要进行编程的行动。作为替代方案，具有自学习能力的系统允许系统自主识别，区分和分类状态并逐步确定动作。在本文中，我们展示并讨论了混合AI算法的制定，该算法结合了Q-Learning和AutoWisard算法，该算法将允许自治系统自学。混合AI算法将在自主移动机器人仿真中实现，并且将呈现和讨论结果。

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