9 research outputs found
Deep learning for video game playing
In this article, we review recent Deep Learning advances in the context of
how they have been applied to play different types of video games such as
first-person shooters, arcade games, and real-time strategy games. We analyze
the unique requirements that different game genres pose to a deep learning
system and highlight important open challenges in the context of applying these
machine learning methods to video games, such as general game playing, dealing
with extremely large decision spaces and sparse rewards
MAPiS 2019 - First MAP-i Seminar: proceedings
This book contains a selection of Informatics papers accepted for presentation and discussion at “MAPiS 2019 - First MAP-i
Seminar”, held in Aveiro, Portugal, January 31, 2019. MAPiS is the first conference organized by the MAP-i first year students,
in the context of the Seminar course. The MAP-i Doctoral Programme in Computer Science is a joint Doctoral Programme in
Computer Science of the University of Minho, the University of Aveiro and the University of Porto. This programme aims to
form highly-qualified professionals, fostering their capacity and knowledge to the research area.
This Conference was organized by the first grade students attending the Seminar Course. The aim of the course was to introduce
concepts which are complementary to scientific and technological education, but fundamental to both completing a PhD
successfully and entailing a career on scientific research. The students had contact with the typical procedures and difficulties of
organizing and participate in such a complex event. These students were in charge of the organization and management of all the
aspects of the event, such as the accommodation of participants or revision of the papers. The works presented in the Conference
and the papers submitted were also developed by these students, fomenting their enthusiasm regarding the investigation in the
Informatics area. (...)publishe
Automated iterative game design
Computational systems to model aspects of iterative game design were proposed, encompassing: game generation, sampling behaviors in a game, analyzing game behaviors for patterns, and iteratively altering a game design. Explicit models of the actions in games as planning operators allowed an intelligent system to reason about how actions and action sequences affect gameplay and to create new mechanics. Metrics to analyze differences in player strategies were presented and were able to identify flaws in game designs. An intelligent system learned design knowledge about gameplay and was able to reduce the number of design iterations needed during playtesting a game to achieve a design goal.
Implications for how intelligent systems augment and automate human game design practices are discussed.Ph.D