Modelling adaptive systems using plausible petri nets
Modelling adaptive systems using plausible petri nets
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发表时间：
2018-07
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通讯作者：
J. Chiachío;M. Chiachío;D. Prescott;J. Andrews
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作者：
J. Chiachío;M. Chiachío;D. Prescott;J. Andrews
使用Petri网分析和建模复杂系统时的主要挑战之一是处理不确定信息，而且能够利用这种不确定性动态地使建模的系统适应不确定(变化的)上下文条件。这种自适应依赖于Petri网的某种形式的学习能力，而这一点很难使用现有的Petri网形式来实现。本文展示了如何使用作者最近开发的一种新的Petri网范例--合乎情理的Petri网来自然地实现不确定性管理和自适应。该方法以铁路轨道资产管理为例，利用合理的Petri网，将多个轨道维护和检查活动与随机轨道几何退化过程联合建模。由此产生的专家系统被证明能够自主地适应来自噪声状态监测数据的上下文变化。这种适应是利用贝叶斯更新机制来实现的，该机制内在地实现在可信的Petri网的执行语义中。
One of the main challenges when analyzing and modelling complex systems using Petri nets is to deal with uncertain information, and moreover, to be able to use such uncertainty to dynamically adapt the modelled system to uncertain (changing) contextual conditions. Such self-adaptation relies on some form of learning capability of the Petri net, which can be hardly implemented using the existing Petri net formalisms. This paper shows how uncertainty management and self-adaptation can be achieved naturally using Plausible Petri Nets, a new Petri net paradigm recently developed by the authors. The methodology is exemplified using a case study about railway track asset management, where several track maintenance and inspection activities are modelled jointly with a stochastic track geometry degradation process using a Plausible Petri net. The resulting expert system is shown to be able to autonomously adapt to contextual changes coming from noisy condition monitoring data. This adaptation is carried out taking advantage of a Bayesian updating mechanism which is inherently implemented in the execution semantics of the Plausible Petri net.