不确定析取时态网络的时基动态可控性：图神经网络指导的树搜索方法
Time-based Dynamic Controllability of Disjunctive Temporal Networks with Uncertainty: A Tree Search Approach with Graph Neural Network Guidance
摘要：
不确定性环境下的调度问题是人工智能的一个研究热点。研究了具有不确定性的析取时态网络的动态可控性问题，即寻找一种策略来满足不可控动作持续时间的所有约束。我们引入了一个更严格的，更强的形式的可控性比DC DTNU，基于时间的动态可控性（TDC），并提出了一个树搜索的方法来确定是否DTNU是TDC。此外，我们利用消息传递神经网络（MPNN）的学习能力作为启发式树搜索指导。最后，我们进行实验，树搜索显示出优越的结果，以国家的最先进的时间游戏自动机（TGA）为基础的方法。我们观察到，使用MPNN树搜索指导导致解决更难的DTNU问题的性能和可扩展性显着增加。
英文摘要：
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.
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