Reinforcement mechanism design
Reinforcement mechanism design
Pingzhong Tang
Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence
Early Career. Pages 5146-5150.
https://doi.org/10.24963/ijcai.2017/739
We put forward a modeling and algorithmic framework to design and optimize mechanisms in dynamic industrial environments where a designer can make use of the data generated in the process to automatically improve future design. Our solution, coined reinforcement mechanism design, is rooted in game theory but incorporates recent AI techniques to get rid of nonrealistic modeling assumptions and to make automated optimization feasible. We instantiate our framework on the key application scenarios of Baidu and Taobao, two of the largest mobile app companies in China. For the Taobao case, our framework automatically designs mechanisms that allocate buyer impressions for the e-commerce website; for the Baidu case, our framework automatically designs dynamic reserve pricing schemes of advertisement auctions of the search engine. Experiments show that our solutions outperform the state-of-the-art alternatives and those currently deployed, under both scenarios.
Keywords:
Agent-based and Multi-agent Systems: Economic paradigms, auctions and market-based systems
Agent-based and Multi-agent Systems: Engineering methods, platforms, languages and tools
Machine Learning: Reinforcement Learning