Decision Making from Confidence Measurement on the Reward Growth using Supervised Learning - A Study Intended for Large-scale Video Games

D. Taralla, Z. Qiu, A. Sutera, R. Fonteneau, D. Ernst

2016

Abstract

Video games have become more and more complex over the past decades. Today, players wander in visuallyand option- rich environments, and each choice they make, at any given time, can have a combinatorial number of consequences. However, modern artificial intelligence is still usually hard-coded, and as the game environments become increasingly complex, this hard-coding becomes exponentially difficult. Recent research works started to let video game autonomous agents learn instead of being taught, which makes them more intelligent. This contribution falls under this very perspective, as it aims to develop a framework for the generic design of autonomous agents for large-scale video games. We consider a class of games for which expert knowledge is available to define a state quality function that gives how close an agent is from its objective. The decision making policy is based on a confidence measurement on the growth of the state quality function, computed by a supervised learning classification model. Additionally, no stratagems aiming to reduce the action space are used. As a proof of concept, we tested this simple approach on the collectible card game Hearthstone and obtained encouraging results.

Paper Citation

in Harvard Style

Taralla D., Qiu Z., Sutera A., Fonteneau R. and Ernst D. (2016). Decision Making from Confidence Measurement on the Reward Growth using Supervised Learning - A Study Intended for Large-scale Video Games . In Proceedings of the 8th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART, ISBN 978-989-758-172-4, pages 264-271. DOI: 10.5220/0005666202640271

in Bibtex Style

@conference{icaart16,
author={D. Taralla and Z. Qiu and A. Sutera and R. Fonteneau and D. Ernst},
title={Decision Making from Confidence Measurement on the Reward Growth using Supervised Learning - A Study Intended for Large-scale Video Games},
booktitle={Proceedings of the 8th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART,},
year={2016},
pages={264-271},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005666202640271},
isbn={978-989-758-172-4},
}

in EndNote Style

TY - CONF
JO - Proceedings of the 8th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART,
TI - Decision Making from Confidence Measurement on the Reward Growth using Supervised Learning - A Study Intended for Large-scale Video Games
SN - 978-989-758-172-4
AU - Taralla D.
AU - Qiu Z.
AU - Sutera A.
AU - Fonteneau R.
AU - Ernst D.
PY - 2016
SP - 264
EP - 271
DO - 10.5220/0005666202640271