19 research outputs found
Illuminating Mario Scenes in the Latent Space of a Generative Adversarial Network
Generative adversarial networks (GANs) are quickly becoming a ubiquitous
approach to procedurally generating video game levels. While GAN generated
levels are stylistically similar to human-authored examples, human designers
often want to explore the generative design space of GANs to extract
interesting levels. However, human designers find latent vectors opaque and
would rather explore along dimensions the designer specifies, such as number of
enemies or obstacles. We propose using state-of-the-art quality diversity
algorithms designed to optimize continuous spaces, i.e. MAP-Elites with a
directional variation operator and Covariance Matrix Adaptation MAP-Elites, to
efficiently explore the latent space of a GAN to extract levels that vary
across a set of specified gameplay measures. In the benchmark domain of Super
Mario Bros, we demonstrate how designers may specify gameplay measures to our
system and extract high-quality (playable) levels with a diverse range of level
mechanics, while still maintaining stylistic similarity to human authored
examples. An online user study shows how the different mechanics of the
automatically generated levels affect subjective ratings of their perceived
difficulty and appearance.Comment: Accepted to AAAI 202
Say "Sul Sul!" to SimSim, A Sims-Inspired Platform for Sandbox Game AI
This paper proposes environment design in the life simulation game The Sims
as a novel platform and challenge for testing divergent search algorithms. In
this domain, which includes a minimal viability criterion, the goal is to
furnish a house with objects that satisfy the physical needs of a simulated
agent. Importantly, the large number of objects available to the player
(whether human or automated) affords a wide variety of solutions to the
underlying design problem. Empirical studies in a novel open source simulator
called SimSim investigate the ability of novelty-based evolutionary algorithms
to effectively generate viable environment designs.Comment: 7 pages, Accepted as poster to AIIDE 202
Summarizing Strategy Card Game AI Competition
This paper concludes five years of AI competitions based on Legends of Code
and Magic (LOCM), a small Collectible Card Game (CCG), designed with the goal
of supporting research and algorithm development. The game was used in a number
of events, including Community Contests on the CodinGame platform, and Strategy
Card Game AI Competition at the IEEE Congress on Evolutionary Computation and
IEEE Conference on Games. LOCM has been used in a number of publications
related to areas such as game tree search algorithms, neural networks,
evaluation functions, and CCG deckbuilding. We present the rules of the game,
the history of organized competitions, and a listing of the participant and
their approaches, as well as some general advice on organizing AI competitions
for the research community. Although the COG 2022 edition was announced to be
the last one, the game remains available and can be played using an online
leaderboard arena
Finding Game Levels with the Right Difficulty in a Few Trials through Intelligent Trial-and-Error
Methods for dynamic difficulty adjustment allow games to be tailored to
particular players to maximize their engagement. However, current methods often
only modify a limited set of game features such as the difficulty of the
opponents, or the availability of resources. Other approaches, such as
experience-driven Procedural Content Generation (PCG), can generate complete
levels with desired properties such as levels that are neither too hard nor too
easy, but require many iterations. This paper presents a method that can
generate and search for complete levels with a specific target difficulty in
only a few trials. This advance is enabled by through an Intelligent
Trial-and-Error algorithm, originally developed to allow robots to adapt
quickly. Our algorithm first creates a large variety of different levels that
vary across predefined dimensions such as leniency or map coverage. The
performance of an AI playing agent on these maps gives a proxy for how
difficult the level would be for another AI agent (e.g. one that employs Monte
Carlo Tree Search instead of Greedy Tree Search); using this information, a
Bayesian Optimization procedure is deployed, updating the difficulty of the
prior map to reflect the ability of the agent. The approach can reliably find
levels with a specific target difficulty for a variety of planning agents in
only a few trials, while maintaining an understanding of their skill landscape.Comment: To be presented in the Conference on Games 202
Covariance Matrix Adaptation for the Rapid Illumination of Behavior Space
We focus on the challenge of finding a diverse collection of quality
solutions on complex continuous domains. While quality diver-sity (QD)
algorithms like Novelty Search with Local Competition (NSLC) and MAP-Elites are
designed to generate a diverse range of solutions, these algorithms require a
large number of evaluations for exploration of continuous spaces. Meanwhile,
variants of the Covariance Matrix Adaptation Evolution Strategy (CMA-ES) are
among the best-performing derivative-free optimizers in single-objective
continuous domains. This paper proposes a new QD algorithm called Covariance
Matrix Adaptation MAP-Elites (CMA-ME). Our new algorithm combines the
self-adaptation techniques of CMA-ES with archiving and mapping techniques for
maintaining diversity in QD. Results from experiments based on standard
continuous optimization benchmarks show that CMA-ME finds better-quality
solutions than MAP-Elites; similarly, results on the strategic game Hearthstone
show that CMA-ME finds both a higher overall quality and broader diversity of
strategies than both CMA-ES and MAP-Elites. Overall, CMA-ME more than doubles
the performance of MAP-Elites using standard QD performance metrics. These
results suggest that QD algorithms augmented by operators from state-of-the-art
optimization algorithms can yield high-performing methods for simultaneously
exploring and optimizing continuous search spaces, with significant
applications to design, testing, and reinforcement learning among other
domains.Comment: Accepted to GECCO 202