Robust Model Equivalence using Stochastic Bisimulation for N-Agent Interactive DIDs

Muthukumaran Chandrasekaran, Junhuan Zhang, Prashant Doshi, Yifeng Zeng
Proceedings of the 33rd Conference on Uncertainty in Artificial Intelligence, PMLR R15:661-670, 2017.

Abstract

I-DIDs suffer disproportionately from the curse of dimensionality dominated by the exponential growth in the number of models over time. Previ- ous methods for scaling I-DIDs identify notions of equivalence between models, such as behav- ioral equivalence (BE). But, this requires that the models be solved first. Also, model space com- pression across agents has not been previously investigated. We present a way to compress the space of models across agents, possibly with dif- ferent frames, and do so without having to solve them first, using stochastic bisimulation. We test our approach on two non-cooperative partially observable domains with up to 20 agents.

Cite this Paper

BibTeX
@InProceedings{pmlr-vR15-chandrasekaran17a, title = {Robust Model Equivalence using Stochastic Bisimulation for N-Agent Interactive DIDs}, author = {Chandrasekaran, Muthukumaran and Zhang, Junhuan and Doshi, Prashant and Zeng, Yifeng}, booktitle = {Proceedings of the 33rd Conference on Uncertainty in Artificial Intelligence}, pages = {661--670}, year = {2017}, editor = {Elidan, Gal and Kersting, Kristian}, volume = {R15}, series = {Proceedings of Machine Learning Research}, month = {11--15 Aug}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/r15/main/assets/chandrasekaran17a/chandrasekaran17a.pdf}, url = {https://proceedings.mlr.press/r15/chandrasekaran17a.html}, abstract = {I-DIDs suffer disproportionately from the curse of dimensionality dominated by the exponential growth in the number of models over time. Previ- ous methods for scaling I-DIDs identify notions of equivalence between models, such as behav- ioral equivalence (BE). But, this requires that the models be solved first. Also, model space com- pression across agents has not been previously investigated. We present a way to compress the space of models across agents, possibly with dif- ferent frames, and do so without having to solve them first, using stochastic bisimulation. We test our approach on two non-cooperative partially observable domains with up to 20 agents.}, note = {Reissued by PMLR on 04 October 2026.} }
Endnote
%0 Conference Paper %T Robust Model Equivalence using Stochastic Bisimulation for N-Agent Interactive DIDs %A Muthukumaran Chandrasekaran %A Junhuan Zhang %A Prashant Doshi %A Yifeng Zeng %B Proceedings of the 33rd Conference on Uncertainty in Artificial Intelligence %C Proceedings of Machine Learning Research %D 2017 %E Gal Elidan %E Kristian Kersting %F pmlr-vR15-chandrasekaran17a %I PMLR %P 661--670 %U https://proceedings.mlr.press/r15/chandrasekaran17a.html %V R15 %X I-DIDs suffer disproportionately from the curse of dimensionality dominated by the exponential growth in the number of models over time. Previ- ous methods for scaling I-DIDs identify notions of equivalence between models, such as behav- ioral equivalence (BE). But, this requires that the models be solved first. Also, model space com- pression across agents has not been previously investigated. We present a way to compress the space of models across agents, possibly with dif- ferent frames, and do so without having to solve them first, using stochastic bisimulation. We test our approach on two non-cooperative partially observable domains with up to 20 agents. %Z Reissued by PMLR on 04 October 2026.
APA
Chandrasekaran, M., Zhang, J., Doshi, P. & Zeng, Y.. (2017). Robust Model Equivalence using Stochastic Bisimulation for N-Agent Interactive DIDs. Proceedings of the 33rd Conference on Uncertainty in Artificial Intelligence, in Proceedings of Machine Learning Research R15:661-670 Available from https://proceedings.mlr.press/r15/chandrasekaran17a.html. Reissued by PMLR on 04 October 2026.

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