An enhanced searching strategy for multi-agent mobile applications

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.