848 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
Neural Network Guided Evolution of L-system Plants
A Lindenmayer system is a parallel rewriting system that generates graphic shapes using several rules. Genetic programming (GP) is an evolutionary algorithm that evolves expressions. A convolutional neural network(CNN) is a type of neural network which is useful for image recognition and classification. The goal of this thesis will be to generate different styles of L-system based 2D images of trees from scratch using genetic programming. The system will use a convolutional neural network to evaluate the trees and produce a fitness value for genetic programming. Different architectures of CNN are explored. We analyze the performance of the system and show the capabilities of the combination of CNN and GP. We show that a variety of interesting tree images can be automatically evolved. We also found that the success of the system highly depends on CNN training, as well as the form of the GP's L-system language representation
Making CNNs for Video Parsing Accessible
The ability to extract sequences of game events for high-resolution e-sport
games has traditionally required access to the game's engine. This serves as a
barrier to groups who don't possess this access. It is possible to apply deep
learning to derive these logs from gameplay video, but it requires
computational power that serves as an additional barrier. These groups would
benefit from access to these logs, such as small e-sport tournament organizers
who could better visualize gameplay to inform both audience and commentators.
In this paper we present a combined solution to reduce the required
computational resources and time to apply a convolutional neural network (CNN)
to extract events from e-sport gameplay videos. This solution consists of
techniques to train a CNN faster and methods to execute predictions more
quickly. This expands the types of machines capable of training and running
these models, which in turn extends access to extracting game logs with this
approach. We evaluate the approaches in the domain of DOTA2, one of the most
popular e-sports. Our results demonstrate our approach outperforms standard
backpropagation baselines.Comment: 11 pages, 6 figures, Foundations of Digital Games 201
Learning the Designer's Preferences to Drive Evolution
This paper presents the Designer Preference Model, a data-driven solution
that pursues to learn from user generated data in a Quality-Diversity
Mixed-Initiative Co-Creativity (QD MI-CC) tool, with the aims of modelling the
user's design style to better assess the tool's procedurally generated content
with respect to that user's preferences. Through this approach, we aim for
increasing the user's agency over the generated content in a way that neither
stalls the user-tool reciprocal stimuli loop nor fatigues the user with
periodical suggestion handpicking. We describe the details of this novel
solution, as well as its implementation in the MI-CC tool the Evolutionary
Dungeon Designer. We present and discuss our findings out of the initial tests
carried out, spotting the open challenges for this combined line of research
that integrates MI-CC with Procedural Content Generation through Machine
Learning.Comment: 16 pages, Accepted and to appear in proceedings of the 23rd European
Conference on the Applications of Evolutionary and bio-inspired Computation,
EvoApplications 202
Deep Learning Concepts for Evolutionary Art
A deep convolutional neural network (CNN) trained on millions of images forms a very high-level abstract overview of any given target image. Our primary goal is to use this high-level content information of a given target image to guide the automatic evolution of images. We use genetic programming (GP) to evolve procedural textures. We incorporate a pre-trained deep CNN model into the fitness. We are not performing any training, but rather, we pass a target image through the pre-trained deep CNN and use its the high-level representation as the fitness guide for evolved images. We develop a preprocessing strategy called Mean Minimum Matrix Strategy (MMMS) which reduces the dimensions and identifies the most relevant high-level activation maps. The technique using reduced activation matrices for a fitness shows promising results. GP is able to guide the evolution of textures such that they have shared characteristics with the target image. We also experiment with the fully connected “classifier” layers of the deep CNN. The evolved images are able to achieve high confidence scores from the deep CNN module for some tested target images. Finally, we implement our own shallow convolutional neural network with a fixed set of filters. Experiments show that the basic CNN had limited effectiveness, likely due to the lack of training. In conclusion, the research shows the potential for using deep learning concepts in evolutionary art. As deep CNN models become better understood, they will be able to be used more effectively for evolutionary art
EMPIRICAL RESEARCH ON HUMAN-AI COLLABORATIVE ARCHITECTURAL DESIGN PROCESS THROUGH A DEEP LEARNING APPROACH
北九州市立大学博士(工学)The purpose of this thesis is to explore how AI technologies intervene in the architectural design process and to discuss the importance and approaches that drive the paradigm shift towards human-AI collaboration in architectural design. The research is conducted from two perspectives: theoretical and practical. At the theoretical level, how AI technologies affect architectural design through technological evolution is analyzed, as well as the advantages, disadvantages and trends of different AI networks in sustainably analyzing and optimizing different kinds of architectural designs. Further, based on this, the methodology of how to develop a reflection on the nature of technology and data is discussed. At the practical level, AI methods that are inventive and capable of performance-based design are constructed and trained. And the basic process of human-AI collaborative architectural design is presented with an empirical study. The results of this thesis not only provide a theoretical reference and methodological basis for future research on human-AI collaborative architectural design at a broader and higher level but also attempt to explore new ideas and methods for the field of architectural design during the evolution of the old and new paradigms, ultimately realizing the purpose of sustainable development of the B&C industry.doctoral thesi
Deep Learning based Recommender System: A Survey and New Perspectives
With the ever-growing volume of online information, recommender systems have
been an effective strategy to overcome such information overload. The utility
of recommender systems cannot be overstated, given its widespread adoption in
many web applications, along with its potential impact to ameliorate many
problems related to over-choice. In recent years, deep learning has garnered
considerable interest in many research fields such as computer vision and
natural language processing, owing not only to stellar performance but also the
attractive property of learning feature representations from scratch. The
influence of deep learning is also pervasive, recently demonstrating its
effectiveness when applied to information retrieval and recommender systems
research. Evidently, the field of deep learning in recommender system is
flourishing. This article aims to provide a comprehensive review of recent
research efforts on deep learning based recommender systems. More concretely,
we provide and devise a taxonomy of deep learning based recommendation models,
along with providing a comprehensive summary of the state-of-the-art. Finally,
we expand on current trends and provide new perspectives pertaining to this new
exciting development of the field.Comment: The paper has been accepted by ACM Computing Surveys.
https://doi.acm.org/10.1145/328502
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