Rinascimento: Optimising statistical forward planning agents for playing splendor

Ivan BRAVI; Diego PÉREZ-LIÉBANA; Simon M. LUCAS; Jialin LIU

doi:10.1109/CIG.2019.8848028

Rinascimento: Optimising statistical forward planning agents for playing splendor

Ivan BRAVI, Diego PÉREZ-LIÉBANA, Simon M. LUCAS, Jialin LIU

Research output: Book Chapters | Papers in Conference Proceedings › Conference paper (refereed) › Referred Conference Paper › peer-review

10 Citations (Scopus)

Abstract

Game-based benchmarks have been playing an essential role in the development of Artificial Intelligence (AI) techniques. Providing diverse challenges is crucial to push research toward innovation and understanding in modern techniques. Rinascimento provides a parameterised partially-observable mul-tiplayer card-based board game, these parameters can easily modify the rules, objectives and items in the game. We describe the framework in all its features and the game-playing challenge providing baseline game-playing AIs and analysis of their skills. We reserve to agents' hyper-parameter tuning a central role in the experiments highlighting how it can heavily influence the performance. The base-line agents contain several additional contribution to Statistical Forward Planning algorithms.

Original language	English
Title of host publication	Proceedings of the IEEE Conference on Games 2019, CoG 2019
Publisher	IEEE Computer Society
Number of pages	8
ISBN (Electronic)	9781728118840
DOIs	https://doi.org/10.1109/CIG.2019.8848028
Publication status	Published - Aug 2019
Externally published	Yes
Event	2019 IEEE Conference on Games, CoG 2019 - London, United Kingdom Duration: 20 Aug 2019 → 23 Aug 2019

Publication series

Name	IEEE Conference on Computatonal Intelligence and Games, CIG
Volume	2019-August
ISSN (Print)	2325-4270
ISSN (Electronic)	2325-4289

Conference

Conference	2019 IEEE Conference on Games, CoG 2019
Country/Territory	United Kingdom
City	London
Period	20/08/19 → 23/08/19

Bibliographical note

Publisher Copyright:
© 2019 IEEE.

Funding

This work was funded by the EPSRC CDT in Intelligent Games and Game Intelligence (IGGI) EP/L015846/1.

Keywords

Artificial general intelligence
Benchmark
Game-playing
Hyper-parameter optimisation

Access to Document

10.1109/CIG.2019.8848028

Cite this

@inproceedings{02f9cef7df954cf88fb9c877a0fccc70,

title = "Rinascimento: Optimising statistical forward planning agents for playing splendor",

abstract = "Game-based benchmarks have been playing an essential role in the development of Artificial Intelligence (AI) techniques. Providing diverse challenges is crucial to push research toward innovation and understanding in modern techniques. Rinascimento provides a parameterised partially-observable mul-tiplayer card-based board game, these parameters can easily modify the rules, objectives and items in the game. We describe the framework in all its features and the game-playing challenge providing baseline game-playing AIs and analysis of their skills. We reserve to agents' hyper-parameter tuning a central role in the experiments highlighting how it can heavily influence the performance. The base-line agents contain several additional contribution to Statistical Forward Planning algorithms.",

keywords = "Artificial general intelligence, Benchmark, Game-playing, Hyper-parameter optimisation",

author = "Ivan BRAVI and Diego P{\'E}REZ-LI{\'E}BANA and LUCAS, {Simon M.} and Jialin LIU",

note = "Publisher Copyright: {\textcopyright} 2019 IEEE.; 2019 IEEE Conference on Games, CoG 2019 ; Conference date: 20-08-2019 Through 23-08-2019",

year = "2019",

month = aug,

doi = "10.1109/CIG.2019.8848028",

language = "English",

series = "IEEE Conference on Computatonal Intelligence and Games, CIG",

publisher = "IEEE Computer Society",

booktitle = "Proceedings of the IEEE Conference on Games 2019, CoG 2019",

address = "United States",

}

BRAVI, I, PÉREZ-LIÉBANA, D, LUCAS, SM & LIU, J 2019, Rinascimento: Optimising statistical forward planning agents for playing splendor. in Proceedings of the IEEE Conference on Games 2019, CoG 2019., 8848028, IEEE Conference on Computatonal Intelligence and Games, CIG, vol. 2019-August, IEEE Computer Society, 2019 IEEE Conference on Games, CoG 2019, London, United Kingdom, 20/08/19. https://doi.org/10.1109/CIG.2019.8848028

Rinascimento: Optimising statistical forward planning agents for playing splendor. / BRAVI, Ivan; PÉREZ-LIÉBANA, Diego; LUCAS, Simon M. et al.
Proceedings of the IEEE Conference on Games 2019, CoG 2019. IEEE Computer Society, 2019. 8848028 (IEEE Conference on Computatonal Intelligence and Games, CIG; Vol. 2019-August).

Research output: Book Chapters | Papers in Conference Proceedings › Conference paper (refereed) › Referred Conference Paper › peer-review

TY - GEN

T1 - Rinascimento: Optimising statistical forward planning agents for playing splendor

AU - BRAVI, Ivan

AU - PÉREZ-LIÉBANA, Diego

AU - LUCAS, Simon M.

AU - LIU, Jialin

PY - 2019/8

Y1 - 2019/8

N2 - Game-based benchmarks have been playing an essential role in the development of Artificial Intelligence (AI) techniques. Providing diverse challenges is crucial to push research toward innovation and understanding in modern techniques. Rinascimento provides a parameterised partially-observable mul-tiplayer card-based board game, these parameters can easily modify the rules, objectives and items in the game. We describe the framework in all its features and the game-playing challenge providing baseline game-playing AIs and analysis of their skills. We reserve to agents' hyper-parameter tuning a central role in the experiments highlighting how it can heavily influence the performance. The base-line agents contain several additional contribution to Statistical Forward Planning algorithms.

AB - Game-based benchmarks have been playing an essential role in the development of Artificial Intelligence (AI) techniques. Providing diverse challenges is crucial to push research toward innovation and understanding in modern techniques. Rinascimento provides a parameterised partially-observable mul-tiplayer card-based board game, these parameters can easily modify the rules, objectives and items in the game. We describe the framework in all its features and the game-playing challenge providing baseline game-playing AIs and analysis of their skills. We reserve to agents' hyper-parameter tuning a central role in the experiments highlighting how it can heavily influence the performance. The base-line agents contain several additional contribution to Statistical Forward Planning algorithms.

KW - Artificial general intelligence

KW - Benchmark

KW - Game-playing

KW - Hyper-parameter optimisation

UR - http://www.scopus.com/inward/record.url?scp=85070657320&partnerID=8YFLogxK

U2 - 10.1109/CIG.2019.8848028

DO - 10.1109/CIG.2019.8848028

M3 - Conference paper (refereed)

AN - SCOPUS:85070657320

T3 - IEEE Conference on Computatonal Intelligence and Games, CIG

BT - Proceedings of the IEEE Conference on Games 2019, CoG 2019

PB - IEEE Computer Society

T2 - 2019 IEEE Conference on Games, CoG 2019

Y2 - 20 August 2019 through 23 August 2019

ER -

Rinascimento: Optimising statistical forward planning agents for playing splendor

Abstract

Publication series

Conference

Bibliographical note

Funding

Keywords

Access to Document

Other files and links

Fingerprint

Cite this