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 informati...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 cannot be directly applied to imperfect-information mobile applications.Here,we take the Contact Bridge,a multi-agent application with imperfect information,for the case study.We propose an enhanced searching strategy to deal with multi-agent applications with imperfect information.We design a self-training bidding system model and apply a Recurrent Neural Network(RNN)to model the bidding process.The bridge system model consists of two parts,a bidding prediction system based on imitation learning to get a contract quickly and a visualization system for hands understanding 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 analysis model is designed to efficiently evaluate the results of sampling.Experimental results indicate that our searching strategy outperforms the top rule-based mobile applications.展开更多
基金supported by the Funds for Creative Research Groups of China under No.61921003 and Snyrey Bridge Company.
文摘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 cannot be directly applied to imperfect-information mobile applications.Here,we take the Contact Bridge,a multi-agent application with imperfect information,for the case study.We propose an enhanced searching strategy to deal with multi-agent applications with imperfect information.We design a self-training bidding system model and apply a Recurrent Neural Network(RNN)to model the bidding process.The bridge system model consists of two parts,a bidding prediction system based on imitation learning to get a contract quickly and a visualization system for hands understanding 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 analysis model is designed to efficiently evaluate the results of sampling.Experimental results indicate that our searching strategy outperforms the top rule-based mobile applications.
