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.