An Agent Based Modelling Approach for Stochastic Planning Parameters

Dangelmaier, Wilhelm; Klöpper, Benjamin; Blecken, Alexander

doi:10.1007/978-3-540-74481-8_22

Cited by 2 publications

(1 citation statement)

References 7 publications

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“…This splitting of the probabilistic model enables the application of a system of distributed expert agents providing up-to-date probability distribution for each track section (for details cf. [30]). Basically, the up-to-date probability distributions are sent via message communication by the expert agents and replace their counterparts in the vehicle model.…”

Section: Simulation Resultsmentioning

confidence: 99%

Probabilistic Planning for Predictive Condition Monitoring and Adaptation Within the Self-Optimizing Energy Management of an Autonomous Railway Vehicle

Klöpper¹,

Sondermann-Wölke²,

Romaus³

2012

J. Robot. Mechatron.

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Self-optimizing mechatronic systems are a new class of technical systems. On the one hand, new challenges regarding dependability arise from their additional complexity and adaptivity. On the other hand, their abilities enable new concepts and methods to improve the dependability of mechatronic systems. This paper introduces a multi-level dependability concept for selfoptimizing mechatronic systems and shows how probabilistic planning can be used to improve the availability and reliability of systems in the operating phase. The general idea to improve the availability of autonomous systems by applying probabilistic planning methods to avoid energy shortages is exemplified on the example of an innovative railway vehicle.

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Section: Simulation Resultsmentioning

confidence: 99%