Scheduling in the presence of uncertainty is an area of interest in artificial intelligence due to the large number of applications. We study the problem of dynamic controllability (DC) of disjunctive temporal networks with uncertainty (DTNU), which seeks a strategy to satisfy all constraints in response to uncontrollable action durations. We introduce a more restricted, stronger form of controllability than DC for DTNUs, time-based dynamic controllability (TDC), and present a tree search approach to determine whether or not a DTNU is TDC. Moreover, we leverage the learning capability of a message passing neural network (MPNN) as a heuristic for tree search guidance. Finally, we conduct experiments for which the tree search shows superior results to state-of-the-art timed-game automata (TGA) based approaches. We observe that using an MPNN for tree search guidance leads to a significant increase in solving performance and scalability to harder DTNU problems.

中文翻译：

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具有不确定性的分离时间网络的基于时间的动态可控性：具有图神经网络指导的树搜索方法

由于存在大量应用程序，在存在不确定性的情况下进行调度是人工智能的一个感兴趣的领域。我们研究了具有不确定性的析取时间网络 (DTNU) 的动态可控性 (DC) 问题，该问题寻求一种策略来满足所有约束以响应不可控的动作持续时间。我们为 DTNU 引入了一种比 DC 更受限制、更强的可控性形式，即基于时间的动态可控性 (TDC)，并提出了一种树搜索方法来确定 DTNU 是否是 TDC。此外，我们利用消息传递神经网络 (MPNN) 的学习能力作为树搜索指导的启发式方法。最后，我们进行了一些实验，在这些实验中，树搜索显示出优于最先进的基于定时博弈自动机 (TGA) 的方法的结果。