Galvan, Edgar (2020) Statistical Tree-based Population Seeding for Rolling Horizon EAs in General Video Game Playing. arXiv.org. ISSN 2331-8422

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Abstract

Multiple Artificial Intelligence (AI) methods have been proposed over recent years to create controllers to play multiple video games of different nature and complexity without revealing the specific mechanics of each of these games to the AI methods. In recent years, Evolutionary Algorithms (EAs) employing rolling horizon mechanisms have achieved extraordinary results in these type of problems. However, some limitations are present in Rolling Horizon EAs making it a grand challenge of AI. These limitations include the wasteful mechanism of creating a population and evolving it over a fraction of a second to propose an action to be executed by the game agent. Another limitation is to use a scalar value (fitness value) to direct evolutionary search instead of accounting for a mechanism that informs us how a particular agent behaves during the rolling horizon simulation. In this work, we address both of these issues. We introduce the use of a statistical tree that tackles the latter limitation. Furthermore, we tackle the former limitation by employing a mechanism that allows us to seed part of the population using Monte Carlo Tree Search, a method that has dominated multiple General Video Game AI competitions. We show how the proposed novel mechanism, called Statistical Tree-based Population Seeding, achieves better results compared to vanilla Rolling Horizon EAs in a set of 20 games, including 10 stochastic and 10 deterministic games.

Item Type:	Article
Additional Information:	Cite as: Galván, E., Gorshkova, O., Mooney, P., Ameneyro, F.V. & Cuevas, E. 2020, Statistical Tree-based Population Seeding for Rolling Horizon EAs in General Video Game Playing, Cornell University Library, arXiv.org, Ithaca.
Keywords:	Artificial intelligence; Computer & video games; Computer simulation; Evolutionary algorithms; Horizon; Population; Population (statistical);
Academic Unit:	Faculty of Science and Engineering > Computer Science Faculty of Science and Engineering > Research Institutes > Hamilton Institute
Item ID:	17463
Identification Number:	https://doi.org/10.48550/arXiv.2008.13253
Depositing User:	Edgar Galvan
Date Deposited:	24 Aug 2023 13:30
Journal or Publication Title:	arXiv.org
Publisher:	Cornell University Library, arXiv.org
Refereed:	Yes
URI:	Publisher
Use Licence:	This item is available under a Creative Commons Attribution Non Commercial Share Alike Licence (CC BY-NC-SA). Details of this licence are available here

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