Ensemble decision systems for general video game playing

Damien Anderson; Philip Rodgers; John Levine; Cristina Guerrero-Romero; Diego Perez-Liebana

doi:10.1109/CIG.2019.8848089

Ensemble decision systems for general video game playing

Damien Anderson^*, Philip Rodgers, John Levine, Cristina Guerrero-Romero, Diego Perez-Liebana

^*Corresponding author for this work

Computing

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

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Abstract

Ensemble Decision Systems offer a unique form of decision making that allows a collection of algorithms to reason together about a problem. Each individual algorithm has its own inherent strengths and weaknesses, and often it is difficult to overcome the weaknesses, while retaining the strengths. Instead of altering the properties of the algorithm, the Ensemble Decision System augments the performance with other algorithms that have complementing strengths. This work outlines different options for building an Ensemble Decision System as well as providing analysis on its performance compared to the individual components of the system with interesting results, showing an increase in the generality of the algorithms without significantly impeding performance.

Original language	English
Title of host publication	2019 IEEE Conference on Games (CoG)
Publisher	IEEE
Number of pages	8
ISBN (Electronic)	978-1-7281-1884-0
ISBN (Print)	978-1-7281-1885-7
DOIs	https://doi.org/10.1109/CIG.2019.8848089
Publication status	Published - 26 Sep 2019

Publication series

Name
ISSN (Print)	2325-4270
ISSN (Electronic)	2325-4289

Keywords

GVGAI
GVGP
Ensemble Decision Systems
Game AI

Documents and Links

10.1109/CIG.2019.8848089

Anderson, D. et al (2019) Ensemble decision systems for general video game playing
Copyright © 2019 IEEE. This is a post-peer review, pre-copy edited version of the article and may differ slightly from the final published version of the work. This work is made available in line with the publisher policy. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works.
Author accepted manuscript, 326 KB

Cite this

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AU - Levine, John

AU - Guerrero-Romero, Cristina

AU - Perez-Liebana, Diego

N1 - Acceptance in SAN (author confirmed this is the acceptance confirmation). ET 13/2/20 Changed template to proceedings ET 10/2/20 AAM: no embargo - found copy via Unpaywall and uploaded. ET 10/2/20 In REF policy scope - paper has ISSN. ET

PY - 2019/9/26

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N2 - Ensemble Decision Systems offer a unique form of decision making that allows a collection of algorithms to reason together about a problem. Each individual algorithm has its own inherent strengths and weaknesses, and often it is difficult to overcome the weaknesses, while retaining the strengths. Instead of altering the properties of the algorithm, the Ensemble Decision System augments the performance with other algorithms that have complementing strengths. This work outlines different options for building an Ensemble Decision System as well as providing analysis on its performance compared to the individual components of the system with interesting results, showing an increase in the generality of the algorithms without significantly impeding performance.

AB - Ensemble Decision Systems offer a unique form of decision making that allows a collection of algorithms to reason together about a problem. Each individual algorithm has its own inherent strengths and weaknesses, and often it is difficult to overcome the weaknesses, while retaining the strengths. Instead of altering the properties of the algorithm, the Ensemble Decision System augments the performance with other algorithms that have complementing strengths. This work outlines different options for building an Ensemble Decision System as well as providing analysis on its performance compared to the individual components of the system with interesting results, showing an increase in the generality of the algorithms without significantly impeding performance.

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KW - Ensemble Decision Systems

KW - Game AI

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DO - 10.1109/CIG.2019.8848089

M3 - Conference contribution

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BT - 2019 IEEE Conference on Games (CoG)

PB - IEEE

ER -

Ensemble decision systems for general video game playing

Abstract

Publication series

Keywords

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