Efficient approach for reusing and sharing train driving plans using case-based reasoning

Abstract

This paper presents an efficient collaboration approach for reusing and sharing freight train driving plans P using casebased reasoning (CBR). P is formed by a set of actions that can move a train from one end to the other in a railroad. Collaboration is established by sharing different train driving experiences in different stretches. Three agents are positioned at each end: Planner, Executor, and Memory. Planner is responsible for generating P . Executor tests/adjusts (if necessary)/executes the actions of P . Until the train reaches the end, P may undergo ∆ adjustments depending on environmental conditions. The modified plan P + ∆ is returned to the origin to be integrated into the local experience base, maintained by the Memory. The approach was evaluated according the fuel consumption, accuracy of the case recovery task, and efficiency of task adaptation and application of such cases. The expansion of the experiences reduced the efforts of both the Planner and the Executor. In addition, our approach allowed the reuse, with low effort, of the obtained experiences in similar scenarios.