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  volume={518},
  number={7540},
  pages={529--533},
  year={2015},
  publisher={Nature Publishing Group}
}

@article{gilks2005m,
  title={M arkov Chain M onte C arlo},
  author={Gilks, Walter R},
  journal={Encyclopedia of biostatistics},
  volume={4},
  year={2005},
  publisher={Wiley Online Library}
}

@INPROCEEDINGS{6728533,
  title={Probabilistic online POMDP decision making for lane changes in fully automated driving},
  author={Ulbrich, Simon and Maurer, Markus},
  booktitle={International IEEE Conference on Intelligent Transportation Systems (ITSC)},
  pages={2063--2067},
  year={2013},
  organization={IEEE}
}

@inproceedings{ijcai2017-664,
  title={Online decision-making for scalable autonomous systems},
  author={Wray, Kyle Hollins and Witwicki, Stefan J and Zilberstein, Shlomo},
  booktitle={Proceedings of the 26th International Joint Conference on Artificial Intelligence},
  pages={4768--4774},
  year={2017}
}

@article{DBLP:journals/corr/abs-1909-13870,
  author = {Rohan Chitnis and Tom{\'{a}}s Lozano{-}P{\'{e}}rez},
  title= {Learning Compact Models for Planning with Exogenous Processes},
  journal = {CoRR},
  volume= {abs/1909.13870},
  year= {2019},
  url= {http://arxiv.org/abs/1909.13870},
  archivePrefix = {arXiv},
  eprint    = {1909.13870},
  timestamp = {Wed, 02 Oct 2019 13:04:08 +0200},
  biburl    = {https://dblp.org/rec/journals/corr/abs-1909-13870.bib},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}

@inproceedings{chitnis2020learning,
  title={Learning compact models for planning with exogenous processes},
  author={Chitnis, Rohan and Lozano-P{\'e}rez, Tom{\'a}s},
  booktitle={CoRL},
  pages={813--822},
  year={2020},
  organization={PMLR}
}


@InProceedings{pmlr-v80-srinivas18b,
  title = 	 {Universal Planning Networks: Learning Generalizable Representations for Visuomotor Control},
  author =       {Srinivas, Aravind and Jabri, Allan and Abbeel, Pieter and Levine, Sergey and Finn, Chelsea},
  booktitle = 	 {ICML},
  year = 	 {2018}
}

@inproceedings{jaakkola1994reinforcement,
  title={Reinforcement learning algorithm for partially observable Markov decision problems},
  author={Jaakkola, Tommi and Singh, Satinder P and Jordan, Michael I},
  booktitle={Proceedings of the 7th International Conference on Neural Information Processing Systems},
  pages={345--352},
  year={1994}
}

@inproceedings{speer2017conceptnet,
  title={ConceptNet 5.5: an open multilingual graph of general knowledge},
  author={Speer, Robyn and Chin, Joshua and Havasi, Catherine},
  booktitle={Proceedings of the Thirty-First AAAI Conference on Artificial Intelligence},
  pages={4444--4451},
  year={2017}
}

@inproceedings{brown2020language,
 author = {Brown, Tom and Mann, Benjamin and Ryder, Nick and Subbiah, Melanie and Kaplan, Jared D and Dhariwal, Prafulla and Neelakantan, Arvind and Shyam, Pranav and Sastry, Girish and Askell, Amanda and Agarwal, Sandhini and Herbert-Voss, Ariel and Krueger, Gretchen and Henighan, Tom and Child, Rewon and Ramesh, Aditya and Ziegler, Daniel and Wu, Jeffrey and Winter, Clemens and Hesse, Chris and Chen, Mark and Sigler, Eric and Litwin, Mateusz and Gray, Scott and Chess, Benjamin and Clark, Jack and Berner, Christopher and McCandlish, Sam and Radford, Alec and Sutskever, Ilya and Amodei, Dario},
 booktitle = {Advances in Neural Information Processing Systems},
 title = {Language Models are Few-Shot Learners},
 year = {2020}
}

@inproceedings{amiri2019augmenting,
  title={Augmenting knowledge through statistical, goal-oriented human-robot dialog},
  author={Amiri, Saeid and Bajracharya, Sujay and Goktolgal, Cihangir and Thomason, Jesse and Zhang, Shiqi},
  booktitle={2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)},
  year={2019}
}

@article{Zhang_Zeng_Lu_Wu_Zhang_2022, title={Efficient Dialog Policy Learning by Reasoning with Contextual Knowledge}, volume={36}, url={https://ojs.aaai.org/index.php/AAAI/article/view/21421}, DOI={10.1609/aaai.v36i10.21421}, abstractNote={Goal-oriented dialog policy learning algorithms aim to learn a dialog policy for selecting language actions based on the current dialog state. Deep reinforcement learning methods have been used for dialog policy learning. This work is motivated by the observation that, although dialog is a domain with rich contextual knowledge, reinforcement learning methods are ill-equipped to incorporate such knowledge into the dialog policy learning process. In this paper, we develop a deep reinforcement learning framework for goal-oriented dialog policy learning that learns user preferences from user goal data, while leveraging commonsense knowledge from people. The developed framework has been evaluated using a realistic dialog simulation platform. Compared with baselines from the literature and the ablations of our approach, we see significant improvements in learning efficiency and the quality of the computed action policies.}, number={10}, journal={Proceedings of the AAAI Conference on Artificial Intelligence}, author={Zhang, Haodi and Zeng, Zhichao and Lu, Keting and Wu, Kaishun and Zhang, Shiqi}, year={2022}, month={Jun.}, pages={11667-11675} }

@article{ding2022robot,
  title={Robot Task Planning and Situation Handling in Open Worlds},
  author={Ding, Yan and Zhang, Xiaohan and Amiri, Saeid and Cao, Nieqing and Yang, Hao and Esselink, Chad and Zhang, Shiqi},
  journal={arXiv preprint arXiv:2210.01287},
  year={2022}
}

@article{liu2023llm+,
  title={Llm+ p: Empowering large language models with optimal planning proficiency},
  author={Liu, Bo and Jiang, Yuqian and Zhang, Xiaohan and Liu, Qiang and Zhang, Shiqi and Biswas, Joydeep and Stone, Peter},
  journal={arXiv preprint arXiv:2304.11477},
  year={2023}
}