TY - GEN
T1 - Pitako - Recommending game design elements in Cicero
AU - Machado, Tiago
AU - Gopstein, Dan
AU - Nealen, Andy
AU - Togelius, Julian
N1 - Funding Information:
Tiago Machado is supported by the Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPQ), under the Science without Borders scholarship 202859/2015-0. We also want to thank to our intern students and collaborators, Angela Wang, Katherine LosCalzo, Katalina Park, and ZhongHeng Li.
Publisher Copyright:
© 2019 IEEE.
PY - 2019/8
Y1 - 2019/8
N2 - 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.
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
UR - http://www.scopus.com/inward/record.url?scp=85073107058&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85073107058&partnerID=8YFLogxK
U2 - 10.1109/CIG.2019.8848081
DO - 10.1109/CIG.2019.8848081
M3 - Conference contribution
AN - SCOPUS:85073107058
T3 - IEEE Conference on Computatonal Intelligence and Games, CIG
BT - 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 -