Search Results for author:
Found 13 papers, 3 papers with code
Generating and Adapting to Diverse Ad-Hoc Cooperation Agents in Hanabi
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.
Evaluating the Rainbow DQN Agent in Hanabi with Unseen Partners
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.
Evaluation of a Recommender System for Assisting Novice Game Designers
Game development is a complex task involving multiple disciplines and technologies.
Pitako -- Recommending Game Design Elements in Cicero
In this paper, we introduce Pitako1, a tool that applies the Recommender System concept to assist humans in creative tasks.
Diverse Agents for Ad-Hoc Cooperation in Hanabi
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.
Leveling the Playing Field -- Fairness in AI Versus Human Game Benchmarks
The extent to which these games benchmark consist of fair competition between human and AI is also a matter of debate.
Towards Game-based Metrics for Computational Co-creativity
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?
Evolving Agents for the Hanabi 2018 CIG Competition
Hanabi is a cooperative card game with hidden information that has won important awards in the industry and received some recent academic attention.
Generating Levels That Teach Mechanics
The automatic generation of game tutorials is a challenging AI problem.
AtDelfi: Automatically Designing Legible, Full Instructions For Games
This paper introduces a fully automatic method for generating video game tutorials.
Talakat: Bullet Hell Generation through Constrained Map-Elites
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.
Procedural Content Generation via Machine Learning (PCGML)
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.
PCG-Based Game Design Patterns
People enjoy encounters with generative software, but rarely are they encouraged to interact with, understand or engage with it.
