Abstract The development of autonomous agents that perform tasks with the same dexterity as performed by humans is one of the challenges of artificial intelligence and robotics. This motivates the research on intelligent agents, since the agent must choose the best action in a dynamic environment in order to maximise the final score. In this context, the present paper introduces a novel algorithm for Qualitative Case-Based Reasoning and Learning (QCBRL), which is a case-based reasoning system that uses qualitative spatial representations to retrieve and reuse cases by means of relations between objects in the environment. Combined with reinforcement learning, QCBRL allows the agent to learn new qualitative cases at runtime, without assuming a pre-processing step. In order to avoid cases that do not lead to the maximum performance, QCBRL executes case-base maintenance, excluding these cases and obtaining new (more suitable) ones. Experimental evaluation of QCBRL was conducted in a simulated robot-soccer environment, in a real humanoid-robot environment and on simple tasks in two distinct gridworld domains. Results show that QCBRL outperforms traditional RL methods. As a result of running QCBRL in autonomous soccer matches, the robots performed a higher average number of goals than those obtained when using pure numerical models. In the gridworlds considered, the agent was able to learn optimal and safety policies.

中文翻译：

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基于定性案例的推理和学习

摘要 开发能够以与人类相同的灵活性执行任务的自主代理是人工智能和机器人技术的挑战之一。这激发了对智能代理的研究，因为代理必须在动态环境中选择最佳动作以最大化最终分数。在此背景下，本文介绍了一种基于案例的定性推理和学习 (QCBRL) 的新算法，它是一种基于案例的推理系统，它使用定性的空间表示通过环境中对象之间的关系来检索和重用案例。 . 结合强化学习，QCBRL 允许代理在运行时学习新的定性案例，而无需假设预处理步骤。为了避免不会导致最大性能的情况，QCBRL 执行案例库维护，排除这些案例并获取新的（更合适的）案例。QCBRL 的实验评估是在模拟机器人足球环境、真实人形机器人环境和两个不同网格世界域中的简单任务中进行的。结果表明 QCBRL 优于传统的 RL 方法。由于在自主足球比赛中运行 QCBRL，机器人的平均进球数高于使用纯数值模型时获得的进球数。在考虑的网格世界中，代理能够学习最佳和安全策略。结果表明 QCBRL 优于传统的 RL 方法。由于在自主足球比赛中运行 QCBRL，机器人的平均进球数高于使用纯数值模型时获得的进球数。在考虑的网格世界中，代理能够学习最佳和安全策略。结果表明 QCBRL 优于传统的 RL 方法。由于在自主足球比赛中运行 QCBRL，机器人的平均进球数高于使用纯数值模型时获得的进球数。在考虑的网格世界中，代理能够学习最佳和安全策略。