@article{Wu_Zilberstein_Chen_2018, title={Privacy-Preserving Policy Iteration for Decentralized POMDPs}, volume={32}, url={https://ojs.aaai.org/index.php/AAAI/article/view/11584}, DOI={10.1609/aaai.v32i1.11584}, abstractNote={ <p> We propose the first privacy-preserving approach to address the privacy issues that arise in multi-agent planning problems modeled as a Dec-POMDP. Our solution is a distributed message-passing algorithm based on trials, where the agents’ policies are optimized using the cross-entropy method. In our algorithm, the agents’ private information is protected using a public-key homomorphic cryptosystem. We prove the correctness of our algorithm and analyze its complexity in terms of message passing and encryption/decryption operations. Furthermore, we analyze several privacy aspects of our algorithm and show that it can preserve the agent privacy of non-neighbors, model privacy, and decision privacy. Our experimental results on several common Dec-POMDP benchmark problems confirm the effectiveness of our approach. </p> }, number={1}, journal={Proceedings of the AAAI Conference on Artificial Intelligence}, author={Wu, Feng and Zilberstein, Shlomo and Chen, Xiaoping}, year={2018}, month={Apr.} }