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Authors: D. Taralla ; Z. Qiu ; A. Sutera ; R. Fonteneau and D. Ernst

Affiliation: University of Liège, Belgium

ISBN: 978-989-758-172-4

Keyword(s): Artificial Intelligence, Decision Making, Video Games, Hearthstone, Supervised Learning, ExtraTrees.

Related Ontology Subjects/Areas/Topics: Artificial Intelligence ; Computational Intelligence ; Evolutionary Computing ; Formal Methods ; Informatics in Control, Automation and Robotics ; Intelligent Control Systems and Optimization ; Knowledge Discovery and Information Retrieval ; Knowledge-Based Systems ; Machine Learning ; Model-Based Reasoning ; Planning and Scheduling ; Simulation and Modeling ; Soft Computing ; Symbolic Systems ; Uncertainty in AI

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 cl assification 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. (More)

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Paper citation in several formats:
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

@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},
}

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

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