Using Artificial Intelligence Techniques for Strategy Generation in the Commons Game

Abstract

Abstract. In this paper, we consider the use of artificial intelligence techniques to aid in discovery of winning strategies for the Commons Game (CG).The game represents a common scenario in which multiple parties share the use of a self-replenishing resource. The resource deteriorates quickly if used indiscriminately. If used responsibly, however, the resource thrives. We consider the scenario one player uses hill climbing or particle swarm optimization to select the course of action, while the remaining N − 1 players use a fixed probability vector. We show that hill climbing and particle swarm optimization consistently generate winning strategies.

Learning automata and the Commons Game

“…As previously stated, there has been little prior work done with regards to the application of computational/AI techniques within the confines of the Commons Game [79,80]. Consequently, the choice the investigation techniques, hill climbing and particle swarm optimization, used in this work was inspired by previous work in applications of AI to gaming problems.…”

“…At the same time, Faysse [21] provided an analysis of the different variations of commons-types games. Recently, Verkhogliad and Oommen have provided some initial results on strategy discovery in the Commons Game [79,80].…”