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Winning Isn't Everything: Training Human-Like Agents for Playtesting and Game AI

Research paper by Yunqi Zhao, Igor Borovikov, Ahmad Beirami, Jason Rupert, Caedmon Somers, Jesse Harder, Fernando de Mesentier Silva, John Kolen, Jervis Pinto, Reza Pourabolghasem, Harold Chaput, James Pestrak, Mohsen Sardari, Long Lin, Navid Aghdaie, et al.

Indexed on: 27 Mar '19Published on: 25 Mar '19Published in: arXiv - Computer Science - Artificial Intelligence



Abstract

Recently, there have been several high-profile achievements of agents learning to play games against humans and beat them. We consider an alternative approach that instead addresses game design for a better player experience by training human-like game agents. Specifically, we study the problem of training game agents in service of the development processes of the game developers that design, build, and operate modern games. We highlight some of the ways in which we think intelligent agents can assist game developers to understand their games, and even to build them. Our early results using the proposed agent framework mark a few steps toward addressing the unique challenges that game developers face.