  Planning under uncertainty is an area of interest in artificial intelligence.
We present a novel approach based on tree search and graph machine learning for
the scheduling problem known as Disjunctive Temporal Networks with Uncertainty
(DTNU). Dynamic Controllability (DC) of DTNUs seeks a reactive scheduling
strategy to satisfy temporal constraints in response to uncontrollable action
durations. We introduce new semantics for reactive scheduling: Time-based
Dynamic Controllability (TDC) and a restricted subset of TDC, R-TDC. We design
a tree search algorithm to determine whether or not a DTNU is R-TDC. Moreover,
we leverage a graph neural network as a heuristic for tree search guidance.
Finally, we conduct experiments on a known benchmark on which we show R-TDC to
retain significant completeness with regard to DC, while being faster to prove.
This results in the tree search processing fifty percent more DTNU problems in
R-TDC than the state-of-the-art DC solver does in DC with the same time budget.
We also observe that graph neural network search guidance leads to substantial
performance gains on benchmarks of more complex DTNUs, with up to eleven times
more problems solved than the baseline tree search.
