Generating Angry Birds-Like Levels with Domino Effects Using Constrained Novelty Search

Febri Abdullah; Pujana Paliyawan; Ruck Thawonmas; Fitra A. Bachtiar

doi:10.1109/CoG47356.2020.9231547

Generating Angry Birds-Like Levels with Domino Effects Using Constrained Novelty Search

Febri Abdullah
, Pujana Paliyawan
, Ruck Thawonmas^*
, Fitra A. Bachtiar

^*Corresponding author for this work

Faculty of Computer Science

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

6 Citations (Scopus)

Abstract

This paper proposes a method to generate interesting Angry Birds-like game levels featuring the Rube Goldberg machine (RGM) mechanism using Constrained Novelty Search (CNS). An RGM level in Angry Birds emphasizes a domino effect, which allows it to be completed by only one bird shooting. By evolving the feasible population and infeasible population in CNS, our results show that the entropy of block-type frequencies is higher than the entropy by our previous generator and that two requirements to achieve playable RGM levels-the 100% stability and the perfect-shot rate-are met. The results indicate that the proposed method can generate levels with more diversity than our previous generator while maintaining their playability.

Original language	English
Title of host publication	IEEE Conference on Games, CoG 2020
Publisher	IEEE Computer Society
Pages	698-701
Number of pages	4
ISBN (Electronic)	9781728145334
DOIs	https://doi.org/10.1109/CoG47356.2020.9231547
Publication status	Published - Aug 2020
Event	2020 IEEE Conference on Games, CoG 2020 - Virtual, Osaka, Japan Duration: 24 Aug 2020 → 27 Aug 2020

Publication series

Name	IEEE Conference on Computatonal Intelligence and Games, CIG
Volume	2020-August
ISSN (Print)	2325-4270
ISSN (Electronic)	2325-4289

Conference

Conference	2020 IEEE Conference on Games, CoG 2020
Country/Territory	Japan
City	Virtual, Osaka
Period	24/08/20 → 27/08/20

Keywords

Angry Birds
Constrained Novelty Search
Procedural Content Generation
Rube Goldberg Machine

Access to Document

10.1109/CoG47356.2020.9231547

Cite this

Abdullah, F., Paliyawan, P., Thawonmas, R., & Bachtiar, F. A. (2020). Generating Angry Birds-Like Levels with Domino Effects Using Constrained Novelty Search. In IEEE Conference on Games, CoG 2020 (pp. 698-701). Article 9231547 (IEEE Conference on Computatonal Intelligence and Games, CIG; Vol. 2020-August). IEEE Computer Society. https://doi.org/10.1109/CoG47356.2020.9231547

@inproceedings{e3ba2984a6cd456e996e990906ed5cd4,

title = "Generating Angry Birds-Like Levels with Domino Effects Using Constrained Novelty Search",

abstract = "This paper proposes a method to generate interesting Angry Birds-like game levels featuring the Rube Goldberg machine (RGM) mechanism using Constrained Novelty Search (CNS). An RGM level in Angry Birds emphasizes a domino effect, which allows it to be completed by only one bird shooting. By evolving the feasible population and infeasible population in CNS, our results show that the entropy of block-type frequencies is higher than the entropy by our previous generator and that two requirements to achieve playable RGM levels-the 100\% stability and the perfect-shot rate-are met. The results indicate that the proposed method can generate levels with more diversity than our previous generator while maintaining their playability.",

keywords = "Angry Birds, Constrained Novelty Search, Procedural Content Generation, Rube Goldberg Machine",

author = "Febri Abdullah and Pujana Paliyawan and Ruck Thawonmas and Bachtiar, \{Fitra A.\}",

note = "Publisher Copyright: {\textcopyright} 2020 IEEE.; 2020 IEEE Conference on Games, CoG 2020 ; Conference date: 24-08-2020 Through 27-08-2020",

year = "2020",

month = aug,

doi = "10.1109/CoG47356.2020.9231547",

language = "English",

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

publisher = "IEEE Computer Society",

pages = "698--701",

booktitle = "IEEE Conference on Games, CoG 2020",

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Abdullah, F, Paliyawan, P, Thawonmas, R & Bachtiar, FA 2020, Generating Angry Birds-Like Levels with Domino Effects Using Constrained Novelty Search. in IEEE Conference on Games, CoG 2020., 9231547, IEEE Conference on Computatonal Intelligence and Games, CIG, vol. 2020-August, IEEE Computer Society, pp. 698-701, 2020 IEEE Conference on Games, CoG 2020, Virtual, Osaka, Japan, 24/08/20. https://doi.org/10.1109/CoG47356.2020.9231547

Generating Angry Birds-Like Levels with Domino Effects Using Constrained Novelty Search. / Abdullah, Febri; Paliyawan, Pujana; Thawonmas, Ruck et al.
IEEE Conference on Games, CoG 2020. IEEE Computer Society, 2020. p. 698-701 9231547 (IEEE Conference on Computatonal Intelligence and Games, CIG; Vol. 2020-August).

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

TY - GEN

T1 - Generating Angry Birds-Like Levels with Domino Effects Using Constrained Novelty Search

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AU - Paliyawan, Pujana

AU - Thawonmas, Ruck

AU - Bachtiar, Fitra A.

PY - 2020/8

Y1 - 2020/8

N2 - This paper proposes a method to generate interesting Angry Birds-like game levels featuring the Rube Goldberg machine (RGM) mechanism using Constrained Novelty Search (CNS). An RGM level in Angry Birds emphasizes a domino effect, which allows it to be completed by only one bird shooting. By evolving the feasible population and infeasible population in CNS, our results show that the entropy of block-type frequencies is higher than the entropy by our previous generator and that two requirements to achieve playable RGM levels-the 100% stability and the perfect-shot rate-are met. The results indicate that the proposed method can generate levels with more diversity than our previous generator while maintaining their playability.

AB - This paper proposes a method to generate interesting Angry Birds-like game levels featuring the Rube Goldberg machine (RGM) mechanism using Constrained Novelty Search (CNS). An RGM level in Angry Birds emphasizes a domino effect, which allows it to be completed by only one bird shooting. By evolving the feasible population and infeasible population in CNS, our results show that the entropy of block-type frequencies is higher than the entropy by our previous generator and that two requirements to achieve playable RGM levels-the 100% stability and the perfect-shot rate-are met. The results indicate that the proposed method can generate levels with more diversity than our previous generator while maintaining their playability.

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KW - Constrained Novelty Search

KW - Procedural Content Generation

KW - Rube Goldberg Machine

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ER -

Generating Angry Birds-Like Levels with Domino Effects Using Constrained Novelty Search

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Keywords

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