An intelligent system for driving trains using Case-Based Reasoning

Borges, André Pinz; Dordal, Osmar Betazzi; Sato, Denise Maria Vecino; Avila, Braulio C.; Enembreck, Fabrício; Scalabrin, Edson Emílio

doi:10.1109/icsmc.2012.6377981

Cited by 4 publications

(2 citation statements)

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“…During the simulation, the following values are calculated for each action: consumption, journey time, resistance, force to drive the train, and others. Therefore, the fitness function considers calculations that result in the forces required to move the train considered [3,6].…”

Section: Architecturementioning

confidence: 99%

“…The use of information technology is imperative for establishing a different and sophisticated way to reduce costs without compromising quality, like in railroads [3,4]. The Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page.…”

Section: Introductionmentioning

confidence: 99%

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

Borges

Dordal

Sato

et al. 2015

Proceedings of the 30th Annual ACM Symposium on Applied Computing

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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.

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Section: Architecturementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%