R
Raph Koster
Publications - 15
Citations - 1666
Raph Koster is an academic researcher. The author has contributed to research in topics: Computer science & Game mechanics. The author has an hindex of 5, co-authored 7 publications receiving 1581 citations.
Papers
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Book
A Theory of Fun for Game Design
Raph Koster,William Wright +1 more
TL;DR: The 10th anniversary edition of this classic book as mentioned in this paper takes a deep look at the influences that underlie modern video games, and examines the elements they share with traditional games such as checkers.
Book
Developing Online Games: An Insider's Guide
TL;DR: In this paper, the authors provide a soup-to-nuts overview of what it takes to successfully design, develop and manage an online game, including case studies of the successes and failures of today's most well-known online games.
Patent
System and Method for Enabling Characters to be Manifested Within A Plurality of Different Virtual Spaces
TL;DR: In this article, a system and method for providing virtual spaces, where a character associated with a user can be manifested within instances of a plurality of the different virtual spaces is presented, where the character can be transferred by the corresponding user between instances of different virtual space and controlled by the user to interact with the different VMs.
Patent
System for providing virtual spaces with separate places and/or acoustic areas
TL;DR: In this article, the sonic characteristics of a virtual space are determined according to a hierarchy of acoustic areas within the virtual space, and a set of parameters and/or characteristics for a given one of the places may be different from the set(s) of parameters or characteristics that correspond to other places in the space.
Journal ArticleDOI
Human-centred mechanism design with Democratic AI
Raph Koster,Jan Balaguer,Andrea Tacchetti,Ari Weinstein,Tina Zhu,Oliver Hauser,Duncan Williams,Lucy Campbell-Gillingham,Phoebe Thacker,Matthew Botvinick,Christopher Summerfield +10 more
TL;DR: In this article , a human-in-the-loop research pipeline called Democratic AI is developed, in which reinforcement learning is used to design a social mechanism that humans prefer by majority.