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

Authors

  • Kevin Osanlou NASA Ames Research Center
  • Jeremy Frank NASA Ames Research Center
  • Andrei Bursuc Valeo.ai
  • Tristan Cazenave LAMSADE, Paris-Dauphine University
  • Eric Jacopin CREC Saint-Cyr Coetquidan
  • Christophe Guettier Safran
  • J. Benton NASA Ames Research Center

DOI:

https://doi.org/10.1609/aaai.v36i9.21224

Keywords:

Planning, Routing, And Scheduling (PRS), Search And Optimization (SO), Machine Learning (ML)

Abstract

Scheduling under uncertainty is an area of interest in artificial 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.

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Published

2022-06-28

How to Cite

Osanlou, K., Frank, J., Bursuc, A., Cazenave, T., Jacopin, E., Guettier, C., & Benton, J. (2022). Solving Disjunctive Temporal Networks with Uncertainty under Restricted Time-Based Controllability Using Tree Search and Graph Neural Networks. Proceedings of the AAAI Conference on Artificial Intelligence, 36(9), 9877-9885. https://doi.org/10.1609/aaai.v36i9.21224

Issue

Section

AAAI Technical Track on Planning, Routing, and Scheduling