@InProceedings{hsiao2024surrogate,
title = {Surrogate {B}ayesian Networks for Approximating Evolutionary Games},
author = {Hsiao, Vincent and S Nau, Dana and Pezeshki, Bobak and Dechter, Rina},
booktitle = {Proceedings of The 27th International Conference on Artificial Intelligence and Statistics},
pages = {2566--2574},
year = {2024},
editor = {Dasgupta, Sanjoy and Mandt, Stephan and Li, Yingzhen},
volume = {238},
series = {Proceedings of Machine Learning Research},
month = {02--04 May},
publisher = {PMLR},
pdf = {https://proceedings.mlr.press/v238/hsiao24a/hsiao24a.pdf},
url = {https://proceedings.mlr.press/v238/hsiao24a.html},
abstract = {Spatial evolutionary games are used to model large systems of interacting agents. In earlier work, a method was developed using Bayesian Networks to approximate the population dynamics in these games. One of the advantages of the Bayesian Network modeling approach is that it is possible to smoothly adjust the size of the network to get more accurate approximations. However, scaling the method up can be intractable if the number of strategies in the evolutionary game increases. In this paper, we propose a new method for computing more accurate approximations by using surrogate Bayesian Networks. Instead of computing inference on larger networks directly, we perform inference on a much smaller surrogate network extended with parameters that exploit the symmetry inherent to the domain. We learn the parameters on the surrogate network using KL-divergence as the loss function. We illustrate the value of this method empirically through a comparison on several evolutionary games.}
}
@inproceedings{DBLP:conf/ijcai/RazeghiKLBAD21,
author = {Yasaman Razeghi and
Kalev Kask and
Yadong Lu and
Pierre Baldi and
Sakshi Agarwal and
Rina Dechter},
title = {Deep Bucket Elimination},
booktitle = {Proceedings of the Thirtieth International Joint Conference on Artificial
Intelligence, {IJCAI} 2021, Virtual Event / Montreal, Canada, 19-27
August 2021},
pages = {4235--4242},
year = {2021},
crossref = {DBLP:conf/ijcai/2021},
url = {https://doi.org/10.24963/ijcai.2021/582},
doi = {10.24963/ijcai.2021/582},
timestamp = {Wed, 25 Aug 2021 17:11:16 +0200},
biburl = {https://dblp.org/rec/conf/ijcai/RazeghiKLBAD21.bib},
bibsource = {dblp computer science bibliography, https://dblp.org}
}
@inproceedings{DBLP:conf/atal/HsiaoPND21,
author = {Vincent Hsiao and
Xinyue Pan and
Dana S. Nau and
Rina Dechter},
editor = {Frank Dignum and
Alessio Lomuscio and
Ulle Endriss and
Ann Now{\'{e}}},
title = {Approximating Spatial Evolutionary Games using Bayesian Networks},
booktitle = {{AAMAS} '21: 20th International Conference on Autonomous Agents and
Multiagent Systems, Virtual Event, United Kingdom, May 3-7, 2021},
pages = {1533--1535},
publisher = {{ACM}},
year = {2021},
url = {https://dl.acm.org/doi/10.5555/3463952.3464150},
timestamp = {Tue, 08 Jun 2021 17:02:09 +0200},
biburl = {https://dblp.org/rec/conf/atal/HsiaoPND21.bib},
bibsource = {dblp computer science bibliography, https://dblp.org}
}
@inproceedings{DBLP:conf/aaai/00010D21,
author = {Junkyu Lee and
Radu Marinescu and
Rina Dechter},
title = {Submodel Decomposition Bounds for Influence Diagrams},
booktitle = {Thirty-Fifth {AAAI} Conference on Artificial Intelligence, {AAAI}
2021, Thirty-Third Conference on Innovative Applications of Artificial
Intelligence, {IAAI} 2021, The Eleventh Symposium on Educational Advances
in Artificial Intelligence, {EAAI} 2021, Virtual Event, February 2-9,
2021},
pages = {12147--12157},
year = {2021},
crossref = {DBLP:conf/aaai/2021},
url = {https://ojs.aaai.org/index.php/AAAI/article/view/17442},
timestamp = {Sat, 05 Jun 2021 01:00:00 +0200},
biburl = {https://dblp.org/rec/conf/aaai/00010D21.bib},
bibsource = {dblp computer science bibliography, https://dblp.org}
}
@inproceedings{DBLP:conf/aaai/00020D21,
author = {Radu Marinescu and
Junkyu Lee and
Rina Dechter},
title = {A New Bounding Scheme for Influence Diagrams},
booktitle = {Thirty-Fifth {AAAI} Conference on Artificial Intelligence, {AAAI}
2021, Thirty-Third Conference on Innovative Applications of Artificial
Intelligence, {IAAI} 2021, The Eleventh Symposium on Educational Advances
in Artificial Intelligence, {EAAI} 2021, Virtual Event, February 2-9,
2021},
pages = {12158--12165},
year = {2021},
crossref = {DBLP:conf/aaai/2021},
url = {https://ojs.aaai.org/index.php/AAAI/article/view/17443},
timestamp = {Sat, 05 Jun 2021 01:00:00 +0200},
biburl = {https://dblp.org/rec/conf/aaai/00020D21.bib},
bibsource = {dblp computer science bibliography, https://dblp.org}
}
@inproceedings{DBLP:conf/ijcai/KaskPBID20,
author = {Kalev Kask and
Bobak Pezeshki and
Filjor Broka and
Alexander T. Ihler and
Rina Dechter},
title = {Scaling Up {AND/OR} Abstraction Sampling},
booktitle = {Proceedings of the Twenty-Ninth International Joint Conference on
Artificial Intelligence, {IJCAI} 2020},
pages = {4266--4274},
year = {2020},
crossref = {DBLP:conf/ijcai/2020},
url = {https://doi.org/10.24963/ijcai.2020/589},
doi = {10.24963/ijcai.2020/589},
timestamp = {Mon, 20 Jul 2020 12:38:52 +0200},
biburl = {https://dblp.org/rec/conf/ijcai/KaskPBID20.bib},
bibsource = {dblp computer science bibliography, https://dblp.org}
}
@inproceedings{DBLP:conf/aaai/Lee20a,
author = {Junkyu Lee},
title = {Submodel Decomposition for Solving Limited Memory Influence Diagrams
(Student Abstract)},
booktitle = {The Thirty-Fourth {AAAI} Conference on Artificial Intelligence, {AAAI}
2020, The Thirty-Second Innovative Applications of Artificial Intelligence
Conference, {IAAI} 2020, The Tenth {AAAI} Symposium on Educational
Advances in Artificial Intelligence, {EAAI} 2020, New York, NY, USA,
February 7-12, 2020},
pages = {13851--13852},
year = {2020},
crossref = {DBLP:conf/aaai/2020},
url = {https://aaai.org/ojs/index.php/AAAI/article/view/7198},
timestamp = {Tue, 02 Feb 2021 07:59:43 +0100},
biburl = {https://dblp.org/rec/conf/aaai/Lee20a.bib},
bibsource = {dblp computer science bibliography, https://dblp.org}
}