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

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 paramet

SUBMITTER: Wang L 

PROVIDER: S-EPMC7431549 | biostudies-literature | 2020 Aug

REPOSITORIES: biostudies-literature