In this game, coordinated groups of players can leverage pre-established conventions to great effect, but playing in an ad-hoc setting requires agents to adapt to its partner's strategies with no previous coordination.
Hanabi is a cooperative game that challenges exist-ing AI techniques due to its focus on modeling the mental states ofother players to interpret and predict their behavior.
Game development is a complex task involving multiple disciplines and technologies.
In this paper, we introduce Pitako1, a tool that applies the Recommender System concept to assist humans in creative tasks.
In complex scenarios where a model of other actors is necessary to predict and interpret their actions, it is often desirable that the model works well with a wide variety of previously unknown actors.
The extent to which these games benchmark consist of fair competition between human and AI is also a matter of debate.
Hanabi is a cooperative card game with hidden information that has won important awards in the industry and received some recent academic attention.
We propose the following question: what game-like interactive system would provide a good environment for measuring the impact and success of a co-creative, cooperative agent?
This paper introduces a fully automatic method for generating video game tutorials.
We describe a search-based approach to generating new levels for bullet hell games, which are action games characterized by and requiring avoidance of a very large amount of projectiles.
This survey explores Procedural Content Generation via Machine Learning (PCGML), defined as the generation of game content using machine learning models trained on existing content.
People enjoy encounters with generative software, but rarely are they encouraged to interact with, understand or engage with it.