Multi-AI competing and winning against humans in iterated Rock-Paper-Scissors game
This work addresses the challenge of modeling human decision-making in game theory, with potential applications in social science and competitive scenarios, though it is incremental as it builds on existing Markov model approaches.
The researchers tackled the problem of predicting human behavior in strategic games by developing a multi-AI system based on Markov models to compete against humans in iterated Rock-Paper-Scissors, achieving a win rate of over 95% against 52 human opponents in 300-round games.
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.