Advances in artificial intelligence create new opportunities for computers to support humans as peers in hybrid teams in several complex problem-solving situations. This paper proposes a decision-making architecture for adaptively informing decisions in human-computer collaboration for large-scale competitive problems under dynamic environments. The proposed architecture integrates methods from sequence learning, model predictive control, and game theory. Computers in this architecture learn objectives and strategies from experimental data to support humans with strategic decisions while operational decisions are made by humans. The paper also presents data-driven methods for partitioning tasks among a team of computers in this architecture. The generalized methodology is illustrated on the real-time strategy game Starcraft II. The results from this application show that low-performing players can benefit from the game-theoretic decision support whereas this support can be overly conservative for high-performing players. The proposed approach provides safe though suboptimal suggestions particularly against an opponent with an unknown level of expertise. The results further show that problem solution with a team of computers based on non-intuitive task partitioning significantly improves the quality of decisions compared to an all-in-one solution with a single computer.

人工智能的进步为计算机提供了新的机会，可以在几种复杂的解决问题的情况下为混合团队中的同伴提供支持。本文提出了一种决策体系结构，用于在动态环境下自适应地通知人机协作中的大规模竞争性问题的决策。所提出的体系结构集成了来自序列学习，模型预测控制和博弈论的方法。这种体系结构中的计算机从实验数据中学习目标和策略，以通过战略决策为人类提供支持，而操作决策则由人类做出。本文还介绍了数据驱动的方法，用于在此体系结构的计算机团队之间分配任务。实时策略游戏《星际争霸II》中说明了通用方法。该应用程序的结果表明，表现欠佳的玩家可以从游戏理论决策支持中受益，而对于表现良好的玩家而言，这种支持可能过于保守。所提出的方法提供了安全但次优的建议，尤其是针对专业水平未知的对手。结果进一步表明，与单台计算机的多合一解决方案相比，基于非直观任务划分的计算机团队的问题解决方案显着提高了决策质量。所提出的方法提供了安全但次优的建议，尤其是针对专业水平未知的对手。结果进一步表明，与单台计算机的多合一解决方案相比，基于非直观任务划分的计算机团队的问题解决方案显着提高了决策质量。所提出的方法提供了安全但次优的建议，尤其是针对专业水平未知的对手。结果进一步表明，与单台计算机的多合一解决方案相比，基于非直观任务划分的计算机团队的问题解决方案显着提高了决策质量。