Lessons learned from a simulated environment for trains conduction

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

doi:10.1109/icit.2012.6209993

Cited by 9 publications

(2 citation statements)

References 7 publications

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“…In general, under all observed situations, the effort to adapt is present, efficient, and increases over time. Table 4 contrasts the performance of human drivers (the Actual column) driving a simulator where the applied actions are determined through a Constraint Satisfaction System (DCOP column) and by an EA1 (Our column) [9]. It should be emphasized that, for all consumption values (measured in LGTT), our approach is better than that of the other competitors, with the exception of a single opportunity, where the DCOP is 5% higher.…”

Section: Methodsmentioning

confidence: 96%

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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: Methodsmentioning

confidence: 96%

“…The initial base case of the MA1 in all tested scenarios contains actual journey plans and journeys executed in a simulator [9].…”

Section: Methodsmentioning

confidence: 99%