In this paper, our focus is on using a rule-based approach to develop agents with artificial general intelligence. In rule-based systems, developing effective rules is a huge challenge, and coding rules for agents requires a large amount of manual work. Association rules mining (ARM) can be used for discovering specific rules from data sets and determining relationships between data sets. In this paper, we introduce a modified ARM method and use it to discover rules that analyse the surrounding environment and determine movements for an agent-guided vehicle that has been designed to achieve autonomous parking. The rules are created by our ARM-based method from training data gained during manual training in customised parking scenarios. In this system, data are represented in terms of fuzzy symbolic elements. We have tested our system by simulation in a virtual environment to demonstrate the effectiveness of this new approach.

在本文中，我们的重点是使用基于规则的方法来开发具有人工智能的智能体。在基于规则的系统中，开发有效的规则是一个巨大的挑战，并且为代理编写规则需要大量的手工工作。关联规则挖掘（ARM）可用于从数据集中发现特定规则并确定数据集之间的关系。在本文中，我们介绍了一种改进的ARM方法，并使用它来发现规则，以分析周围环境并确定旨在实现自动泊车的特工引导车辆的运动。这些规则是通过基于ARM的方法从自定义停车场景中的手动训练期间获得的训练数据中创建的。在该系统中，数据以模糊符号元素表示。