Multi-agent mobile applications play an essential role in mobile applications and have attracted more and more researchers'attention.Previous work has always focused on multi-agent applications with perfect information.Researchers are usually based on human-designed rules to provide decision-making searching services.However,existing methods for solving perfect-information mobile applications can-not be directly applied to imperfect-information mo-bile applications.Here,we take the Contact Bridge,a multi-agent application with imperfect information,for the case study.We propose an enhanced search-ing strategy to deal with multi-agent applications with imperfect information.We design a self-training bid-ding system model and apply a Recurrent Neural Net-work(RNN)to model the bidding process.The bridge system model consists of two parts,a bidding predic-tion system based on imitation learning to get a con-tract quickly and a visualization system for hands un-derstanding to realize regular communication between players.Then,to dynamically analyze the impact of other players'unknown hands on our final reward,we design a Monte Carlo sampling algorithm based on the bidding system model(BSM)to deal with imperfect information.At the same time,a double-dummy anal-ysis model is designed to efficiently evaluate the re-sults of sampling.Experimental results indicate that our searching strategy outperforms the top rule-based mobile applications.

Xiaoyu Zhang;Wei Liu;Fangchun Yang

School of Computer Science(National Pilot Software Engineering School),Beijing University of Posts and Telecommunications,Beijing 100876,China;State Key Laboratory of Networking and Switching Technology(Beijing University of Posts and Telecommunications),Beijing 100876,China

中国通信（英文版）

[1]Xiaoyu Zhang;Wei Liu;Fangchun Yang-.An Enhanced Searching Strategy for Multi-Agent Mobile Applications)[J].中国通信（英文版）,2022(11):282-296

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