Approximate Solutions of Interactive Dynamic Influence Diagrams Using Model Clustering
Abstract
Interactive dynamic influence diagrams (I-DIDs) offer a transparent and semantically clear representation for the sequential decision-making problem over multiple time steps in the presence of other interacting agents. Solving I-DIDs exactly involves knowing the solutions of possible models of the other agents, which increase exponentially with the number of time steps. We present a method of solving I-DIDs approximately by limiting the number of other agents ’ candidate models at each time step to a constant. We do this by clustering the models and selecting a representative set from the clusters. We discuss the error bound of the approximation technique and demonstrate its empirical performance.
Cite
Text
Zeng et al. "Approximate Solutions of Interactive Dynamic Influence Diagrams Using Model Clustering." AAAI Conference on Artificial Intelligence, 2007.Markdown
[Zeng et al. "Approximate Solutions of Interactive Dynamic Influence Diagrams Using Model Clustering." AAAI Conference on Artificial Intelligence, 2007.](https://mlanthology.org/aaai/2007/zeng2007aaai-approximate/)BibTeX
@inproceedings{zeng2007aaai-approximate,
title = {{Approximate Solutions of Interactive Dynamic Influence Diagrams Using Model Clustering}},
author = {Zeng, Yifeng and Doshi, Prashant and Chen, Qiongyu},
booktitle = {AAAI Conference on Artificial Intelligence},
year = {2007},
pages = {782-787},
url = {https://mlanthology.org/aaai/2007/zeng2007aaai-approximate/}
}