Pitako - Recommending game design elements in Cicero

Tiago Machado; Dan Gopstein; Andy Nealen; Julian Togelius

doi:10.1109/CIG.2019.8848081

Pitako - Recommending game design elements in Cicero

Tiago Machado, Dan Gopstein, Andy Nealen, Julian Togelius

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

Abstract

Recommender Systems are widely and successfully applied in e-commerce. Could they be used for designƒ In this paper, we introduce Pitako¹, a tool that applies the Recommender System concept to assist humans in creative tasks. More specifically, Pitako provides suggestions by taking games designed by humans as inputs, and recommends mechanics and dynamics as outputs. Pitako is implemented as a new system within the mixed-initiative AI-based Game Design Assistant, Cicero. This paper discusses the motivation behind the implementation of Pitako as well as its technical details and presents usage examples. We believe that Pitako can influence the use of recommender systems to help humans in their daily tasks.

Original language	English (US)
Title of host publication	IEEE Conference on Games 2019, CoG 2019
Publisher	IEEE Computer Society
ISBN (Electronic)	9781728118840
DOIs	https://doi.org/10.1109/CIG.2019.8848081
State	Published - Aug 2019
Event	2019 IEEE Conference on Games, CoG 2019 - London, United Kingdom Duration: Aug 20 2019 → Aug 23 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	8/20/19 → 8/23/19

Keywords

AI-Game Design Assistant
Exploratory design
Frequent Itemset Data Mining
Recommender Systems

ASJC Scopus subject areas

Artificial Intelligence
Computer Graphics and Computer-Aided Design
Computer Vision and Pattern Recognition
Human-Computer Interaction
Software

Access to Document

10.1109/CIG.2019.8848081

Cite this

Machado, T, Gopstein, D, Nealen, A & Togelius, J 2019, Pitako - Recommending game design elements in Cicero. in IEEE Conference on Games 2019, CoG 2019., 8848081, IEEE Conference on Computatonal Intelligence and Games, CIG, vol. 2019-August, IEEE Computer Society, 2019 IEEE Conference on Games, CoG 2019, London, United Kingdom, 8/20/19. https://doi.org/10.1109/CIG.2019.8848081

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title = "Pitako - Recommending game design elements in Cicero",

abstract = "Recommender Systems are widely and successfully applied in e-commerce. Could they be used for designƒ In this paper, we introduce Pitako1, a tool that applies the Recommender System concept to assist humans in creative tasks. More specifically, Pitako provides suggestions by taking games designed by humans as inputs, and recommends mechanics and dynamics as outputs. Pitako is implemented as a new system within the mixed-initiative AI-based Game Design Assistant, Cicero. This paper discusses the motivation behind the implementation of Pitako as well as its technical details and presents usage examples. We believe that Pitako can influence the use of recommender systems to help humans in their daily tasks.",

keywords = "AI-Game Design Assistant, Exploratory design, Frequent Itemset Data Mining, Recommender Systems",

author = "Tiago Machado and Dan Gopstein and Andy Nealen and Julian Togelius",

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.2019.8848081",

language = "English (US)",

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

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AB - Recommender Systems are widely and successfully applied in e-commerce. Could they be used for designƒ In this paper, we introduce Pitako1, a tool that applies the Recommender System concept to assist humans in creative tasks. More specifically, Pitako provides suggestions by taking games designed by humans as inputs, and recommends mechanics and dynamics as outputs. Pitako is implemented as a new system within the mixed-initiative AI-based Game Design Assistant, Cicero. This paper discusses the motivation behind the implementation of Pitako as well as its technical details and presents usage examples. We believe that Pitako can influence the use of recommender systems to help humans in their daily tasks.

KW - AI-Game Design Assistant

KW - Exploratory design

KW - Frequent Itemset Data Mining

KW - Recommender Systems

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M3 - Conference contribution

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T3 - IEEE Conference on Computatonal Intelligence and Games, CIG

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Pitako - Recommending game design elements in Cicero

Abstract

Publication series

Conference

Keywords

ASJC Scopus subject areas

Access to Document

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