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Hierarchical Multi-Agent Plans Using Model-Based Petri Net

机译:使用基于模型的Petri网的分层多Agent计划

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Agents evolving in complex and dynamic multi-agent systems need to plan their tasks and to adapt their behavior in order to react to unpredictable events. Therefore they must have plans that remain subject to continual updating, even during its execution. Although this property is important in dynamic environments, it is difficult to have a trade-off between the convergence towards the goal to be reached and the reaction to unanticipated events. To cope with this issue, it is needed to have a model allowing to represent plans less sensitive to execution contexts, and to support flexible execution. This paper aims to provide an adequate plan representation, by taking advantage of hierarchical plans and extensions to Petri net. The authors extend the Petri net to take into account the different abstraction levels of plans and the resources required by tasks. To cope with the interdependence of tasks, the authors include in the model, a synchronization mechanism between concurrent transitions. The proposed model can be used to reason on abstract levels of plans and to analyze, in a dynamic way, situations in which the execution of the plan can be performed in any ways, in some ways, or cannot be performed.
机译:在复杂而动态的多智能体系统中发展的智能体需要计划其任务并调整其行为,以便对不可预测的事件做出反应。因此,他们必须制定计划,即使在执行过程中也要不断进行更新。尽管此属性在动态环境中很重要,但是很难在朝着要达到的目标的收敛与对意外事件的反应之间进行权衡。为了解决这个问题,需要有一个模型来表示对执行上下文不太敏感的计划,并支持灵活的执行。本文旨在通过利用分层计划和对Petri网的扩展来提供适当的计划表示。作者扩展了Petri网,以考虑计划的不同抽象级别和任务所需的资源。为了处理任务的相互依赖性,作者在模型中包括了并发转换之间的同步机制。所提出的模型可用于推理计划的抽象级别并以动态方式分析可以以任何方式,以某种方式执行或无法执行计划执行的情况。

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