Solving Disjunctive Temporal Networks with Uncertainty under Restricted Time-Based Controllability Using Tree Search and Graph Neural Networks

Kevin Osanlou

Abstract: Scheduling under uncertainty is an area of interest in artificial intelligence. We study the problem of Dynamic Controllabil- ity (DC) of Disjunctive Temporal Networks with Uncertainty (DTNU), which seeks a reactive scheduling strategy to satisfy temporal constraints in response to uncontrollable action du- rations. We introduce new semantics for reactive scheduling: Time-based Dynamic Controllability (TDC) and a restricted subset of TDC, R-TDC. We present a tree search approach to determine whether or not a DTNU is R-TDC. Moreover, we leverage the learning capability of a Graph Neural Net- work (GNN) 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 re- gard 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 GNN tree search guidance leads to substantial performance gains on bench- marks of more complex DTNUs, with up to eleven times more problems solved than the baseline tree search.