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

Kevin Osanlou, Jeremy Frank, Andrei Bursuc, Tristan Cazenave, Eric Jacopin, Christophe GUETTIER, J. Benton

Abstract:

intelligence. We study the problem of Dynamic Controllability
(DC) of Disjunctive Temporal Networks with Uncertainty
(DTNU), which 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 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 Network
(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 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 GNN tree 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.