对抗董事会：滚动地平线进化算法对抗流行病
Playing Against the Board: Rolling Horizon Evolutionary Algorithms Against Pandemic
摘要：
竞技棋盘游戏为人工智能提供了丰富多样的测试平台。本文认为，协同棋盘游戏对人工智能提出了不同的挑战，因为它必须平衡短期风险缓解与长期获胜策略。合作棋盘游戏要求所有玩家协调他们不同的力量或集中他们的资源，以克服棋盘和随机规则带来的不断升级的挑战。本文以协同棋盘游戏Pandemic为例，提出了一种滚动时域进化算法。大流行病博弈状态的变化是随机的，但这种变化是可预测的，这种复杂的变化方式需要许多专门设计的前向模型、决策的宏观行动表示以及进化算法的遗传操作的修复函数。探索乐观与悲观的游戏状态评估，不同的突变率和事件视野的算法的变体进行了比较，对基线层次的政策代理。结果表明，通过短期推出的渐进方法可以更好地解释董事会可能引入的未来危险，并防范它们。结果突出了协作棋盘游戏对人工智能构成的挑战类型，特别是在处理多玩家协作交互方面。
英文摘要：
Competitive board games have provided a rich and diverse testbed for artificial intelligence. This paper contends that collaborative board games pose a different challenge to artificial intelligence as it must balance short-term risk mitigation with long-term winning strategies. Collaborative board games task all players to coordinate their different powers or pool their resources to overcome an escalating challenge posed by the board and a stochastic ruleset. This paper focuses on the exemplary collaborative board game Pandemic and presents a rolling horizon evolutionary algorithm designed specifically for this game. The complex way in which the Pandemic game state changes in a stochastic but predictable way required a number of specially designed forward models, macro-action representations for decision-making, and repair functions for the genetic operations of the evolutionary algorithm. Variants of the algorithm which explore optimistic versus pessimistic game state evaluations, different mutation rates and event horizons are compared against a baseline hierarchical policy agent. Results show that an evolutionary approach via short-horizon rollouts can better account for the future dangers that the board may introduce, and guard against them. Results highlight the types of challenges that collaborative board games pose to artificial intelligence, especially for handling multi-player collaboration interactions.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
