论文标题
在迭代的岩纸剪辑游戏中，多ai竞争并赢得了与人类的胜利
Multi-AI competing and winning against humans in iterated Rock-Paper-Scissors game
论文作者
论文摘要
预测和建模人类的行为并在人类决策过程中发现趋势是社会科学的主要问题。岩石剪刀（RPS）是许多游戏理论问题和现实世界中的基本战略问题。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，我们使用一种基于一个固定记忆长度（缩写为“单个AI”）的Markov模型的AI（人工智能）算法在迭代的RPS游戏中与人竞争。我们通过结合许多具有不同固定记忆长度的马尔可夫模型（缩写为“多AI”），并开发具有可变参数的多ai架构以适应不同的竞争策略的架构，从而对人类竞争行为进行建模和预测。我们引入了一个称为“焦点长度”（诸如5或10的正数）的参数，以控制我们多AI的速度和灵敏度，以适应对手的策略变化。焦点长度是确定哪个单ai具有最佳性能，并且应该选择在下一场比赛时应考虑的先前一轮的数量。我们对52个人进行了实验，每个人都会与一种特定的多AI模型连续打300发，并证明我们的策略可以击败超过95％的人类对手。
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.
