8 research outputs found
Tile Pattern KL-Divergence for Analysing and Evolving Game Levels
This paper provides a detailed investigation of using the Kullback-Leibler
(KL) Divergence as a way to compare and analyse game-levels, and hence to use
the measure as the objective function of an evolutionary algorithm to evolve
new levels. We describe the benefits of its asymmetry for level analysis and
demonstrate how (not surprisingly) the quality of the results depends on the
features used. Here we use tile-patterns of various sizes as features.
When using the measure for evolution-based level generation, we demonstrate
that the choice of variation operator is critical in order to provide an
efficient search process, and introduce a novel convolutional mutation operator
to facilitate this. We compare the results with alternative generators,
including evolving in the latent space of generative adversarial networks, and
Wave Function Collapse. The results clearly show the proposed method to provide
competitive performance, providing reasonable quality results with very fast
training and reasonably fast generation.Comment: 8 pages plus references. Proceedings of GECCO 201
Recognizing Affiliation: Using Behavioural Traces to Predict the Quality of Social Interactions in Online Games
Online social interactions in multiplayer games can be supportive and
positive or toxic and harmful; however, few methods can easily assess
interpersonal interaction quality in games. We use behavioural traces to
predict affiliation between dyadic strangers, facilitated through their social
interactions in an online gaming setting. We collected audio, video, in-game,
and self-report data from 23 dyads, extracted 75 features, trained Random
Forest and Support Vector Machine models, and evaluated their performance
predicting binary (high/low) as well as continuous affiliation toward a
partner. The models can predict both binary and continuous affiliation with up
to 79.1% accuracy (F1) and 20.1% explained variance (R2) on unseen data, with
features based on verbal communication demonstrating the highest potential. Our
findings can inform the design of multiplayer games and game communities, and
guide the development of systems for matchmaking and mitigating toxic behaviour
in online games.Comment: CHI '2
Evolving Mario levels in the latent space of a deep convolutional generative adversarial network
© 2018 Copyright held by the owner/author(s). Generative Adversarial Networks (GANs) are a machine learning approach capable of generating novel example outputs across a space of provided training examples. Procedural Content Generation (PCG) of levels for video games could benefit from such models, especially for games where there is a pre-existing corpus of levels to emulate. This paper trains a GAN to generate levels for Super Mario Bros using a level from the Video Game Level Corpus. The approach successfully generates a variety of levels similar to one in the original corpus, but is further improved by application of the Covariance Matrix Adaptation Evolution Strategy (CMA-ES). Specifically, various fitness functions are used to discover levels within the latent space of the GAN that maximize desired properties. Simple static properties are optimized, such as a given distribution of tile types. Additionally, the champion A* agent from the 2009 Mario AI competition is used to assess whether a level is playable, and how many jumping actions are required to beat it. These fitness functions allow for the discovery of levels that exist within the space of examples designed by experts, and also guide the search towards levels that fulfill one or more specified objectives
Addressing the fundamental tension of PCGML with discriminative learning
Procedural content generation via machine learning (PCGML) is typically
framed as the task of fitting a generative model to full-scale examples of a
desired content distribution. This approach presents a fundamental tension: the
more design effort expended to produce detailed training examples for shaping a
generator, the lower the return on investment from applying PCGML in the first
place. In response, we propose the use of discriminative models (which capture
the validity of a design rather the distribution of the content) trained on
positive and negative examples. Through a modest modification of
WaveFunctionCollapse, a commercially-adopted PCG approach that we characterize
as using elementary machine learning, we demonstrate a new mode of control for
learning-based generators. We demonstrate how an artist might craft a focused
set of additional positive and negative examples by critique of the generator's
previous outputs. This interaction mode bridges PCGML with mixed-initiative
design assistance tools by working with a machine to define a space of valid
designs rather than just one new design
The database of the PREDICTS (Projecting Responses of Ecological Diversity In Changing Terrestrial Systems) project
The database of the PREDICTS (Projecting Responses of Ecological Diversity In Changing Terrestrial Systems) project
The PREDICTS project—Projecting Responses of Ecological Diversity In Changing Terrestrial Systems (www.predicts.org.uk)—has collated from published studies a large, reasonably representative database of comparable samples of biodiversity from multiple sites that differ in the nature or intensity of human impacts relating to land use. We have used this evidence base to develop global and regional statistical models of how local biodiversity responds to these measures. We describe and make freely available this 2016 release of the database, containing more than 3.2 million records sampled at over 26,000 locations and representing over 47,000 species. We outline how the database can help in answering a range of questions in ecology and conservation biology. To our knowledge, this is the largest and most geographically and taxonomically representative database of spatial comparisons of biodiversity that has been collated to date; it will be useful to researchers and international efforts wishing to model and understand the global status of biodiversity