Modeling the interactive relationships of agents is critical to improving the collaborative capability of a multiagent system. Some methods model these by predefined rules. However, due to the nonstationary problem, the interactive relationship changes over time and cannot be well captured by rules. Other methods adopt a simple mechanism such as an attention network to select the neighbors the current agent should collaborate with. However, in large-scale multiagent systems, collaborative relationships are too complicated to be described by a simple attention network. We propose an adaptive and gated graph attention network (AGGAT), which models the interactive relationships between agents in a cascaded manner. In the AGGAT, we first propose a graph-based hard attention network that roughly filters irrelevant agents. Then, normal soft attention is adopted to decide the importance of each neighbor. Finally, gated attention further refines the collaborative relationship of agents. By using cascaded attention, the collaborative relationship of agents is precisely learned in a coarse-to-fine style. Extensive experiments are conducted on a variety of cooperative tasks. The results indicate that our proposed method outperforms state-of-the-art baselines.

Imperfect information games have served as benchmarks and milestones in fields of artificial intelligence (AI) and game theory for decades. Sensing and exploiting information to effectively describe the game environment is of critical importance for game solving, besides computing or approximating an optimal strategy. Reconnaissance blind chess (RBC), a new variant of chess, is a quintessential game of imperfect information where the players actions are definitely unobserved by the opponent. This characteristic of RBC exponentially expands the scale of the information set and extremely invokes uncertainty of the game environment. In this paper, we introduce a novel sense method, Heuristic Search of Uncertainty Control (HSUC), to significantly reduce the uncertainty of real-time information set. The key idea of HSUC is to consider the whole uncertainty of the environment rather than predicting the opponents strategy. Furthermore, we realize a practical framework for RBC game that incorporates our HSUC method with Monte Carlo Tree Search (MCTS). In the experiments, HSUC has shown better effectiveness and robustness than comparison opponents in information sensing. It is worth mentioning that our RBC game agent has won the first place in terms of uncertainty management in NeurIPS 2019 RBC tournament.