多AI在迭代的石头剪刀布游戏中与人类竞争并获胜
Multi-AI competing and winning against humans in iterated Rock-Paper-Scissors game
摘要：
预测和模拟人类行为并发现人类决策过程中的趋势是社会科学的一个主要问题。石头剪子布（RPS）是许多博弈论问题和现实世界竞争中的基本策略问题。找到击败特定人类对手的正确方法是具有挑战性的。在这里，我们使用一个人工智能（人工智能）算法的基础上马尔可夫模型的一个固定的记忆长度（简称为“单一人工智能”），以竞争对人类的迭代RPS游戏。我们通过组合多个具有不同固定记忆长度的马尔可夫模型（简称“多AI”）来建模和预测人类的竞争行为，并开发了一种具有可变参数的多AI架构，以适应不同的竞争策略。我们引入了一个名为“焦点长度”的参数（一个正数，如5或10）来控制我们的多AI的速度和灵敏度，以适应对手的策略变化。焦点长度是多AI在确定哪个单AI具有最佳性能并应选择参加下一场比赛时应查看的先前回合数。我们对52个不同的人进行了实验，每个人都对一个特定的多AI模型连续进行了300轮比赛，并证明我们的策略可以战胜95%以上的人类对手。
英文摘要：
Predicting and modeling human behavior and finding trends within human decision-making processes is a major problem of social science. Rock Paper Scissors (RPS) is the fundamental strategic question in many game theory problems and real-world competitions. Finding the right approach to beat a particular human opponent is challenging. Here we use an AI (artificial intelligence) algorithm based on Markov Models of one fixed memory length (abbreviated as "single AI") to compete against humans in an iterated RPS game. We model and predict human competition behavior by combining many Markov Models with different fixed memory lengths (abbreviated as "multi-AI"), and develop an architecture of multi-AI with changeable parameters to adapt to different competition strategies. We introduce a parameter called "focus length" (a positive number such as 5 or 10) to control the speed and sensitivity for our multi-AI to adapt to the opponent's strategy change. The focus length is the number of previous rounds that the multi-AI should look at when determining which Single-AI has the best performance and should choose to play for the next game. We experimented with 52 different people, each playing 300 rounds continuously against one specific multi-AI model, and demonstrated that our strategy could win against more than 95% of human opponents.
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。
