@article{barreiro2012europa,
  title={EUROPA: A platform for AI planning, scheduling, constraint programming, and optimization},
  author={Barreiro, Javier and Boyce, Matthew and Do, Minh and Frank, Jeremy and Iatauro, Michael and Kichkaylo, Tatiana and Morris, Paul and Ong, James and Remolina, Emilio and Smith, Tristan and others},
  journal={4th International Competition on Knowledge Engineering for Planning and Scheduling (ICKEPS)},
  year={2012},
  publisher={Citeseer}
}
@article{LUTEBERGET2022,
title = {Planning Autonomous Marine Inspection Tasks using SMT Encoding of Timelines},
journal = {IFAC-PapersOnLine},
volume = {55},
number = {31},
pages = {190-195},
year = {2022},
note = {14th IFAC Conference on Control Applications in Marine Systems, Robotics, and Vehicles CAMS 2022},
issn = {2405-8963},
doi = {https://doi.org/10.1016/j.ifacol.2022.10.430},
url = {https://www.sciencedirect.com/science/article/pii/S2405896322024764},
author = {Bjørnar Luteberget and Synne Fossøy},
keywords = {Autonomy, Task Planning, Inspection, Satisfiability Modulo Theories},
abstract = {A key challenge in autonomous vehicles is the high-level decision making required to be reactive to significant changes in the environment. Timeline-based planning is a paradigm for temporal (time-aware) task planning that can provide such high-level decisions. We present an algorithm for incrementally translating a timeline-based planning problem into satisfability modulo theories (SMT). Off-the-shelf SMT solvers have become very efficient, and using such a solver keeps our planner implementation relatively small, simple, and extensible. Compared to existing SMT translations, our approach typically produces smaller SMT problems, and our system shows promising performance on several benchmark problems. Our planning software was developed in the context of research projects on inspection operations for UUVs on subsea installations and for mobile robots on marine installations.}
}
@article{Cimatti_Micheli_Roveri_2013, title={Timelines with Temporal Uncertainty}, volume={27}, url={https://ojs.aaai.org/index.php/AAAI/article/view/8601}, DOI={10.1609/aaai.v27i1.8601}, abstractNote={ &lt;p&gt; &lt;p&gt;Timelines are a formalism to model planning domains where the  temporal aspects are predominant, and have been used in many  real-world applications. Despite their practical success, a major limitation is the inability  to model temporal uncertainty, i.e. the plan executor cannot decide  the duration of some activities.&lt;/p&gt;&lt;p&gt;In this paper we make two key contributions. First, we propose a comprehensive, semantically well founded framework that  (conservatively) extends with temporal uncertainty the state of the  art timeline approach. Second, we focus on the problem of producing time-triggered plans  that are robust with respect to temporal uncertainty, under a  bounded horizon. In this setting, we present the first complete  algorithm, and we show how it can be made practical by leveraging  the power of Satisfiability Modulo Theories.&lt;/p&gt; &lt;/p&gt; }, number={1}, journal={Proceedings of the AAAI Conference on Artificial Intelligence}, author={Cimatti, Alessandro and Micheli, Andrea and Roveri, Marco}, year={2013}, month={Jun.}, pages={195-201} }
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  title={Planning and Acting with Temporal and Hierarchical Decomposition Models}, 
  year={2014},
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  pages={115-121},
  doi={10.1109/ICTAI.2014.27}}
  
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@incollection{de2020lifted,
  title={Lifted heuristics for timeline-based planning},
  author={De Benedictis, Riccardo and Cesta, Amedeo},
  booktitle={ECAI 2020},
  pages={2330--2337},
  year={2020},
  publisher={IOS Press}
}
@inproceedings{valentini2020temporal,
  title={Temporal planning with intermediate conditions and effects},
  author={Valentini, Alessandro and Micheli, Andrea and Cimatti, Alessandro},
  booktitle={Proceedings of the AAAI Conference on Artificial Intelligence},
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}

@inproceedings{bit2018constraint,
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