Development of Rule-Based Agents for Autonomous Parking Systems by Association Rules Mining

Abstract

Association Rules Mining is an approach to discover rules from data sets, and it can establish relationships among elements in a data set. Our research is focused on rule-based agents with Artificial General Intelligence (AGI), which are developed based on the overall environment to achieve functions with cognition. In this paper, we use a modified Association Rules Mining method to find out characteristic rules from data recorded in the training of customized parking scenarios. Fuzzy symbolic elements are recorded during training, and Association Rule Mining selects rules for the AI agent. Experiments have been conducted in a virtual environment to demonstrate the effectiveness of the proposed new algorithm.