An approach for train driving using case-based reasoning

This paper presents a computational model capable of improving the action policies for a well-defined domain. Each action policy is represented as a driving plan P, which is composed of a number of actions {a1, ... an}. These actions can be used to move a train in a stretch of railroad Sti. The plans are elaborated using a CBR approach and reusing previous solutions and learning from plans. The CBR cycle is divided in three agents that act collaboratively to build or run P, namely: Planner, Executor, and Memory. The Planner agent generates P. Executor agent is responsible for revise and apply the actions of P. During the execution, P may undergo several adjustments depending on multiple circumstances, such as environmental conditions. The modified plan P' returns to its origin end to integrate the local case base, managed by the Memory agent. This approach was evaluated on the following criteria: (i) fuel consumption, (ii) accuracy of case retrieval, and (iii) efficiency of adaptation task and application of adapted cases in real-world scenarios. The inclusion of new experiences reduced efforts Planner and Executor in their tasks, and a reduction in fuel consumption. In addition, the model shows that the increase in diversity in the case base increases the reuse of experiences in objectives-scenarios.