@inproceedings{groshev&al18,
  title={Learning generalized reactive policies using deep neural networks},
  author={Groshev, Edward and Tamar, Aviv and Goldstein, Maxwell and Srivastava, Siddharth and Abbeel, Pieter},
  booktitle={2018 AAAI Spring Symposium Series},
  year={2018}
}

@inproceedings{issakkimuthu&al18,
  title={Training deep reactive policies for probabilistic planning problems},
  author={Issakkimuthu, Murugeswari and Fern, Alan and Tadepalli, Prasad},
  booktitle={Proceedings of the International Conference on Automated Planning and Scheduling},
  volume={28},
  year={2018}
}

@inproceedings{bajpai&al18,
  title={Transfer of deep reactive policies for {MDP} planning},
  author={Bajpai, Aniket and Garg, Sankalp and Mausam},
booktitle = {Annual Conference
               on Neural Information Processing Systems  (NeurIPS)},
  pages     = {10988--10998},
  year      = {2018}
}

@inproceedings{toyer&al18,
  title={Action schema networks: Generalised policies with deep learning},
  author={Toyer, Sam and Trevizan, Felipe and Thi{\'e}baux, Sylvie and Xie, Lexing},
  booktitle={Thirty-Second AAAI Conference on Artificial Intelligence},
  year={2018}
}

@inproceedings{mausam04,
  author    = {Mausam and
               Daniel S. Weld},
  title     = {Solving Concurrent Markov Decision Processes},
  booktitle = {Proceedings of the Nineteenth National Conference on Artificial Intelligence (AAAI)},
  pages     = {716--722},
  year      = {2004}
}

@inproceedings{garg&al19,
  title={Size independent neural transfer for {RDDL} planning},
  author={Garg, Sankalp and Bajpai, Aniket and Mausam},
  booktitle={Proceedings of the International Conference on Automated Planning and Scheduling},
  volume={29},
  pages={631--636},
  year={2019}
}

@inproceedings{garg&al20,
  title={Symbolic network: generalized neural policies for relational MDPs},
  author={Garg, Sankalp and Bajpai, Aniket and Mausam},
  booktitle={International Conference on Machine Learning},
  pages={3397--3407},
  year={2020}
}

@article{staahlberg&al21,
  title={Learning General Optimal Policies with Graph Neural Networks: Expressive Power, Transparency, and Limits},
  author={St{\aa}hlberg, Simon and Bonet, Blai and Geffner, Hector},
  journal={Proceedings of the 32nd International Conference on Automated Planning and Scheduling},
  year={2022}
}

@article{sanner10,
  title={Relational dynamic influence diagram language (rddl): Language description},
  author={Sanner, Scott},
  journal={Unpublished ms. Australian National University},
  volume={32},
  pages={27},
  year={2010},
  publisher={Citeseer}
}

@article{younes&al05,
  title={The first probabilistic track of the international planning competition},
  author={Younes, H{\aa}kan LS and Littman, Michael L and Weissman, David and Asmuth, John},
  journal={Journal of Artificial Intelligence Research},
  volume={24},
  pages={851--887},
  year={2005}
}

@inproceedings{keller&Eyerich12,
  title={PROST: Probabilistic planning based on UCT},
  author={Keller, Thomas and Eyerich, Patrick},
  booktitle={Twenty-Second International Conference on Automated Planning and Scheduling},
  year={2012}
}

@inproceedings{boutilier&al01,
  title={Symbolic dynamic programming for first-order MDPs},
  author={Boutilier, Craig and Reiter, Raymond and Price, Bob},
  booktitle={International Joint Conference on Artificial Intelligence (IJCAI)},
  volume={1},
  pages={690--700},
  year={2001}
}

@article{velivckovic&al18,
  title={Graph attention networks},
  author={Veli{\v{c}}kovi{\'c}, Petar and Cucurull, Guillem and Casanova, Arantxa and Romero, Adriana and Lio, Pietro and Bengio, Yoshua},
  journal={International Conference on Learning Representations},
  year={2018}
}

@inproceedings{mnih&al16,
  title={Asynchronous methods for deep reinforcement learning},
  author={Mnih, Volodymyr and Badia, Adria Puigdomenech and Mirza, Mehdi and Graves, Alex and Lillicrap, Timothy and Harley, Tim and Silver, David and Kavukcuoglu, Koray},
  booktitle={International conference on machine learning},
  pages={1928--1937},
  year={2016},
  organization={PMLR}
}

@inproceedings{kocsis&Szepesv06,
  title={Bandit based monte-carlo planning},
  author={Kocsis, Levente and Szepesv{\'a}ri, Csaba},
  booktitle={European conference on machine learning},
  pages={282--293},
  year={2006},
  organization={Springer}
}

@inproceedings{helmertandCarmel09,
  title={Landmarks, critical paths and abstractions: what's the difference anyway?},
  author={Helmert, Malte and Domshlak, Carmel},
  booktitle={Nineteenth International Conference on Automated Planning and Scheduling},
  year={2009}
}

@inproceedings{bonet&al19,
  title={Learning features and abstract actions for computing generalized plans},
  author={Bonet, Blai and Frances, Guillem and Geffner, Hector},
  booktitle={Proceedings of the AAAI Conference on Artificial Intelligence},
  volume={33},
  number={01},
  pages={2703--2710},
  year={2019}
}

@article{bonet&Geffner18,
  title={Features, projections, and representation change for generalized planning},
  author={Bonet, Blai and Geffner, Hector},
  journal={International Joint Conference on Artificial Intelligence (IJCAI)},
  year={2018}
}

@article{fern&al03,
  title={Approximate policy iteration with a policy language bias},
  author={Fern, Alan and Yoon, Sungwook and Givan, Robert},
  journal={Advances in neural information processing systems},
  volume={16},
  year={2003}
}

@inproceedings{guestrin&al03,
  title={Generalizing plans to new environments in relational MDPs},
  author={Guestrin, Carlos and Koller, Daphne and Gearhart, Chris and Kanodia, Neal},
  booktitle={Proceedings of the 18th international joint conference on Artificial intelligence},
  pages={1003--1010},
  year={2003}
}

@inproceedings{natarajan&al11,
  title={Imitation learning in relational domains: A functional-gradient boosting approach},
  author={Natarajan, Sriraam and Joshi, Saket and Tadepalli, Prasad and Kersting, Kristian and Shavlik, Jude},
  booktitle={Twenty-Second International Joint Conference on Artificial Intelligence},
  year={2011}
}

@inproceedings{kolobov&al09,
  title={Integrating Paradigms for Approximate Probabilistic Planning},
  author={Kolobov, Andrey},
  booktitle={(ICAPS'09) 19th International Conference on Automated Planning and Scheduling, Doctoral Consortium},
  year={2009}
}

@inproceedings{kolobov&al12,
  title={Reverse iterative deepening for finite-horizon MDPs with large branching factors},
  author={Kolobov, Andrey and Dai, Peng and Mausam, Mausam and Weld, Daniel},
  booktitle={Proceedings of the International Conference on Automated Planning and Scheduling},
  volume={22},
  pages={146--154},
  year={2012}
}

@inproceedings{mausam03,
  title={Solving Relational {MDPs} with First-Order Machine Learning},
  author={Mausam and Daniel Weld},
  booktitle={Proceedings of the Workshop on Planning under Uncertainty and Incomplete Information, at ICAPS},
  year={2003}
}

@inproceedings{bonet03,
  author    = {Blai Bonet and
               Hector Geffner},
  editor    = {Enrico Giunchiglia and
               Nicola Muscettola and
               Dana S. Nau},
  title     = {Labeled {RTDP:} Improving the Convergence of Real-Time Dynamic Programming},
  booktitle = {Proceedings of the Thirteenth International Conference on Automated
               Planning and Scheduling {(ICAPS} 2003), June 9-13, 2003, Trento, Italy},
  pages     = {12--21},
  publisher = {{AAAI}},
  year      = {2003}}

@inproceedings{bonet03b,
  author    = {Blai Bonet and
               Hector Geffner},
  editor    = {Georg Gottlob and
               Toby Walsh},
  title     = {Faster Heuristic Search Algorithms for Planning with Uncertainty and
               Full Feedback},
  booktitle = {IJCAI-03, Proceedings of the Eighteenth International Joint Conference
               on Artificial Intelligence, Acapulco, Mexico, August 9-15, 2003},
  pages     = {1233--1238},
  publisher = {Morgan Kaufmann},
  year      = {2003}
}

@book{mausam&kolobov12,
  author    = {Mausam and
               Andrey Kolobov},
  title     = {Planning with Markov Decision Processes: An {AI} Perspective},
  series    = {Synthesis Lectures on Artificial Intelligence and Machine Learning},
  publisher = {Morgan {\&} Claypool Publishers},
  year      = {2012}
}

@article{keyder&al08,
  title={The HMDPP planner for planning with probabilities},
  author={Keyder, Emil and Geffner, Hector},
  journal={Sixth International Planning Competition at ICAPS},
  volume={8},
  year={2008},
  publisher={Citeseer}
}


@InProceedings{sharma&al22,
  title = 	 {SymNet 2.0: Effectively handling Non-Fluents and Actions in Generalized Neural Policies for RDDL Relational MDPs},
  author =       {Sharma, Vishal and Arora, Daman and Gei\ss{}er, Florian and Mausam and Singla, Parag},
  booktitle = 	 {Proceedings of the Thirty-Eighth Conference on Uncertainty in Artificial Intelligence},
  pages = 	 {1771--1781},
  year = 	 {2022},
  volume = 	 {180},
  series = 	 {Proceedings of Machine Learning Research},
  month = 	 {01--05 Aug},
  publisher =    {PMLR},
  pdf = 	 {https://proceedings.mlr.press/v180/sharma22a/sharma22a.pdf},
  url = 	 {https://proceedings.mlr.press/v180/sharma22a.html},
  abstract = 	 {Relational MDPs (RMDPs) compactly represent an infinite set of MDPs with an unbounded number of objects. Solving an RMDP requires a generalized policy that applies to all instances of a domain. Recently, Garg et al. proposed SymNet for this task– it constructs a graph neural network that shares parameters across all instances in a domain, thus making it applicable to any instance in a zero-shot manner. Our analysis of SymNet reveals that it performs no better than random on 1/4th of planning competition domains. The key reasons are its design choices: it misses important information during graph construction, leading to (1) poor generalizability, and (2) potential non-identifiability of different actions. In response, our solution, SymNet2.0, substantially augments SymNet’s graph construction approach by introducing additional nodes and edges which allow a better transfer of important information about a domain. It also improves SymNet’s action decoders with relevant information from objects to make different actions identifiable during scoring. Extensive experiments on twelve competition domains, where we use imitation learning over data generated from the PROST planner, demonstrate that SymNet2.0 performs vastly better than SymNet. Interestingly, even though SymNet2.0 is trained over data from PROST, it outperforms the planner on several test instances due to former’s ability to scale to large instances in a zero-shot manner.}
}


@article{wu&al20,
  title={Graph information bottleneck},
  author={Wu, Tailin and Ren, Hongyu and Li, Pan and Leskovec, Jure},
  journal={Advances in Neural Information Processing Systems},
  volume={33},
  pages={20437--20448},
  year={2020}
}


@inproceedings{alon&yahav21,
title={On the Bottleneck of Graph Neural Networks and its Practical Implications},
author={Uri Alon and Eran Yahav},
booktitle={International Conference on Learning Representations},
year={2021},
url={https://openreview.net/forum?id=i80OPhOCVH2}
}


@inproceedings{li&al18,
author = {Li, Qimai and Han, Zhichao and Wu, Xiao-Ming},
title = {Deeper Insights into Graph Convolutional Networks for Semi-Supervised Learning},
year = {2018},
isbn = {978-1-57735-800-8},
publisher = {AAAI Press},
booktitle = {Proceedings of the Thirty-Second AAAI Conference on Artificial Intelligence and Thirtieth Innovative Applications of Artificial Intelligence Conference and Eighth AAAI Symposium on Educational Advances in Artificial Intelligence},
articleno = {433},
numpages = {8},
location = {New Orleans, Louisiana, USA},
}

@inproceedings{zambetta&al22,
  title={Oracle-SAGE: Planning Ahead in Graph-Based Deep Reinforcement Learning},
  author={Zambetta, Fabio and Thangarajah, John},
  year = {2022},
  booktitle = {Proceedings of the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases},
}

@article{staahlberg&al22,
  title={Learning generalized policies without supervision using gnns},
  author={St{\aa}hlberg, Simon and Bonet, Blai and Geffner, Hector},
  journal={Proceedings of the 19th International Conference on Principles of Knowledge Representation and Reasoning},
  year={2022}
}

@article{hafner&al18,
  title={Learning Latent Dynamics for Planning from Pixels},
  author={Hafner, Danijar and Lillicrap, Timothy and Fischer, Ian and Villegas, Ruben and Ha, David and Lee, Honglak and Davidson, James},
  journal={arXiv preprint arXiv:1811.04551},
  year={2018}
}

@article{palm&al18,
  title={Recurrent relational networks},
  author={Palm, Rasmus and Paquet, Ulrich and Winther, Ole},
  journal={Advances in neural information processing systems},
  volume={31},
  year={2018}
}


@InProceedings{Xu&al18,
  title = 	 {Representation Learning on Graphs with Jumping Knowledge Networks},
  author =       {Xu, Keyulu and Li, Chengtao and Tian, Yonglong and Sonobe, Tomohiro and Kawarabayashi, Ken-ichi and Jegelka, Stefanie},
  booktitle = 	 {Proceedings of the 35th International Conference on Machine Learning},
  pages = 	 {5453--5462},
  year = 	 {2018},
  editor = 	 {Dy, Jennifer and Krause, Andreas},
  volume = 	 {80},
  series = 	 {Proceedings of Machine Learning Research},
  month = 	 {10--15 Jul},
  publisher =    {PMLR},
  pdf = 	 {http://proceedings.mlr.press/v80/xu18c/xu18c.pdf},
  url = 	 {https://proceedings.mlr.press/v80/xu18c.html},
}

@article{ying&al18,
  title={Hierarchical graph representation learning with differentiable pooling},
  author={Ying, Zhitao and You, Jiaxuan and Morris, Christopher and Ren, Xiang and Hamilton, Will and Leskovec, Jure},
  journal={Advances in neural information processing systems},
  volume={31},
  year={2018}
}

@inproceedings{silver&al21,
  title={Planning with learned object importance in large problem instances using graph neural networks},
  author={Silver, Tom and Chitnis, Rohan and Curtis, Aidan and Tenenbaum, Joshua B and Lozano-P{\'e}rez, Tom{\'a}s and Kaelbling, Leslie Pack},
  booktitle={Proceedings of the AAAI conference on artificial intelligence},
  volume={35},
  pages={11962--11971},
  year={2021}
}
