Developing a Repeated Multi-agent Constant-Sum Game Algorithm Using Human Computation

Abstract

In repeated multi-agent constant-sum games, each player's objective is to maximize control over a finite set of resources. We introduce Tens potter, an easy-to-use publicly-available game designed to allow human players to compete as agents against a machine algorithm. The algorithm learns play strategies from humans, reduces them to nine basic strategies, and uses this knowledge to build and adapt its collusion strategy. We use a tournament format to test our algorithm against human players as well as against other established multi-agent algorithms taken from the literature. Through these tournament experiments, we demonstrate how learning techniques adapted using human computation -- information obtained from both human and machine inputs -- can contribute to the development of an algorithm able to defeat two well-established multi-agent machine algorithms in tournament play.