14 research outputs found
Generating and Adapting to Diverse Ad-Hoc Cooperation Agents in Hanabi
Hanabi is a cooperative game that brings the problem of modeling other
players to the forefront. 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 an agent in this setting requires a diverse population
of potential partners, but so far, the behavioral diversity of agents has not
been considered in a systematic way. This paper proposes Quality Diversity
algorithms as a promising class of algorithms to generate diverse populations
for this purpose, and generates a population of diverse Hanabi agents using
MAP-Elites. We also postulate that agents can benefit from a diverse population
during training and implement a simple "meta-strategy" for adapting to an
agent's perceived behavioral niche. We show this meta-strategy can work better
than generalist strategies even outside the population it was trained with if
its partner's behavioral niche can be correctly inferred, but in practice a
partner's behavior depends and interferes with the meta-agent's own behavior,
suggesting an avenue for future research in characterizing another agent's
behavior during gameplay.Comment: arXiv admin note: text overlap with arXiv:1907.0384
Pitako -- Recommending Game Design Elements in Cicero
Recommender Systems are widely and successfully applied in e-commerce. Could
they be used for design? 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.Comment: Paper accepted in the IEEE Conference on Games 2019 (COG 2019
Generating Levels That Teach Mechanics
The automatic generation of game tutorials is a challenging AI problem. While
it is possible to generate annotations and instructions that explain to the
player how the game is played, this paper focuses on generating a gameplay
experience that introduces the player to a game mechanic. It evolves small
levels for the Mario AI Framework that can only be beaten by an agent that
knows how to perform specific actions in the game. It uses variations of a
perfect A* agent that are limited in various ways, such as not being able to
jump high or see enemies, to test how failing to do certain actions can stop
the player from beating the level.Comment: 8 pages, 7 figures, PCG Workshop at FDG 2018, 9th International
Workshop on Procedural Content Generation (PCG2018
Evaluation of a Recommender System for Assisting Novice Game Designers
Game development is a complex task involving multiple disciplines and
technologies. Developers and researchers alike have suggested that AI-driven
game design assistants may improve developer workflow. We present a recommender
system for assisting humans in game design as well as a rigorous human subjects
study to validate it. The AI-driven game design assistance system suggests game
mechanics to designers based on characteristics of the game being developed. We
believe this method can bring creative insights and increase users'
productivity. We conducted quantitative studies that showed the recommender
system increases users' levels of accuracy and computational affect, and
decreases their levels of workload.Comment: The 15th AAAI Conference on Artificial Intelligence and Interactive
Digital Entertainment (AIIDE 19
Catch-Up: A Game in Which the Lead Alternates
Catch-Up is a two-player game in which the players' scores remain close throughout the game, making the eventual winner -- if there is one -- hard to predict. Because neither player can build up an insurmountable lead, its play creates tension and drama, even between players of different skill. We show how the game is played, demonstrate that its simple rules lead to complex game dynamics, analyse some of its most important properties, and discuss possible extensions. (This paper was published at the Game & Puzzle Design in 2015, and it was uploaded onto MPRA for archival purposes.