GAIG Game AI Research Group @ QMUL

Tribes: A New Turn-Based Strategy Game for AI

2020
Diego Perez Liebana and Yu-Jhen Hsu and Stavros Emmanouilidis and Bobby Khaleque and Raluca Gaina

Abstract

This paper introduces Tribes, a new turn-based strategy game framework. Tribes is a multi-player, multi-agent, stochastic and partially observable game that involves strategic and tactical combat decisions. A good playing strategy requires the management of a technology tree, build orders and economy. The framework provides a Forward Model, which can be used by Statistical Forward Planning methods. This paper describes the framework and the opportunities for Game AI research it brings. We further provide an analysis on the action space of this game, as well as benchmarking a series of agents (rule based, one step look-ahead, Monte Carlo, Monte Carlo Tree Search, and Rolling Horizon Evolution) to study their relative playing strength. Results show that although some of these agents can play at a decent level, they are still far from human playing strength.
Github: https://github.com/GAIGResearch/Tribes 

Cite this work

@inproceedings{perez2020tribes,
author= {Diego Perez Liebana and Yu-Jhen Hsu and Stavros Emmanouilidis and Bobby Khaleque and Raluca Gaina},
title= {{Tribes: A New Turn-Based Strategy Game for AI}},
year= {2020},
booktitle= {{Sixteenth AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment (AIIDE-20)}},
abstract= {This paper introduces Tribes, a new turn-based strategy game framework. Tribes is a multi-player, multi-agent, stochastic and partially observable game that involves strategic and tactical combat decisions. A good playing strategy requires the management of a technology tree, build orders and economy. The framework provides a Forward Model, which can be used by Statistical Forward Planning methods. This paper describes the framework and the opportunities for Game AI research it brings. We further provide an analysis on the action space of this game, as well as benchmarking a series of agents (rule based, one step look-ahead, Monte Carlo, Monte Carlo Tree Search, and Rolling Horizon Evolution) to study their relative playing strength. Results show that although some of these agents can play at a decent level, they are still far from human playing strength.},
}

Similar Posts

Comments