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. 2020 Aug 17;10(1):13873.
doi: 10.1038/s41598-020-70544-7.

Multi-AI competing and winning against humans in iterated Rock-Paper-Scissors game

Lei Wang  1 , Wenbin Huang  1   2 , Yuanpeng Li  1   2 , Julian Evans  1 , Sailing He  3   4   5
Affiliations

Multi-AI competing and winning against humans in iterated Rock-Paper-Scissors game

Lei Wang et al. Sci Rep. .

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.

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Conflict of interest statement

The authors declare no competing interests.

Figures

Figure 1
Figure 1
300 rounds AI competition results for 4 typical players.
Figure 2
Figure 2
Total scores for multi-AI competing against different players in 300 rounds game.
Figure 3
Figure 3
Game results (total scores) of Multi-10AI competing with human in 300 rounds.
Figure 4
Figure 4
Game results (total scores) of multi-AI with Markov chain lengths 1–5 competing with 8 typical players in 300 rounds.
See this image and copyright information in PMC

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