Abstract
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.
Highlights
The Rock-Paper-Scissors (RPS) game has been widely used to study competitive phenomena in society and biology, such as ecological interactions, the maintenance of biodiversity in ecological s ystems[1,2,3,4,5,6,7,8,9] and price dispersion of markets[10,11]
Through the experiments we found that different models work best against different human opponent’s competition strategies and the prediction results vary greatly so we built the 1st -5th order Markov chain models (i.e. AI-1 to AI-5) with different memory lengths for exploiting different human competition strategies
We have introduced a multi-AI model that wins against human in iterated Rock Paper Scissors (RPS) games and experimentally confirmed our results
Summary
The Rock-Paper-Scissors (RPS) game has been widely used to study competitive phenomena in society and biology, such as ecological interactions, the maintenance of biodiversity in ecological s ystems[1,2,3,4,5,6,7,8,9] and price dispersion of markets[10,11]. Previous research has found that there is a social circle in human competitive strategy when playing iterated RPS games[12]. In this article we proposed a multi-AI algorithm that can exploit human strategy and win against human players in the same iterated RPS games and we conduct experiments with human players to confirm the results. A Markov chain is a stochastic model describing a sequence of possible events in which the probability of each event depends only on the states attained in the previous events. Previous research has used Markov process model to describe the stochastic evolution dynamic of the Rock–Scissors–Paper G ame[21]. The iterated RPS game is considered as a Markov process and Markov chains are built throughout the process of 300 rounds of competition.
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