A multi-layer architecture proposal for conducting trains employing CBR

This paper presents a planning approach using Case-Based Reasoning (CBR) modeled as a Subsumption Architecture to generate plans for driving trains. The main idea of a planning strategy is to generate a sequence of actions for an agent, which can use these actions to change its environment. CBR allows using prior experiences for new task assignments. In the proposed ap-proach, each previous experience (if not applicable) is adjusted us-ing one or more adaptation methods like substitutive and genetic algorithm. Our interest is to create a flexible architecture for an agent and apply it to simulate train conductions. We expect that the plans generated by this approach generate better results com-pared to another studies already developed for the area mainly considering fuel consumption and travel time.