162 research outputs found
Optimizing visual properties of game content through neuroevolution
This paper presents a search-based approach to generating game content that satisfies both gameplay requirements and user-expressed aesthetic criteria. Using evolutionary constraint satisfaction, we search for spaceships (for a space combat game) represented as compositional patternproducing networks. While the gameplay requirements are satisfied by ad-hoc defined constraints, the aesthetic evaluation function can also be informed by human aesthetic judgement. This is achieved using indirect interactive evolution, where an evaluation function re-weights an array of aesthetic criteria based on the choices of a human player. Early results show that we can create aesthetically diverse and interesting spaceships while retaining in-game functionality.peer-reviewe
Adapting models of visual aesthetics for personalized content creation
This paper introduces a search-based approach to
personalized content generation with respect to visual aesthetics.
The approach is based on a two-step adaptation procedure
where (1) the evaluation function that characterizes the content
is adjusted to match the visual aesthetics of users and (2) the
content itself is optimized based on the personalized evaluation
function. To test the efficacy of the approach we design fitness
functions based on universal properties of visual perception,
inspired by psychological and neurobiological research. Using
these visual properties we generate aesthetically pleasing 2D
game spaceships via neuroevolutionary constrained optimization
and evaluate the impact of the designed visual properties on the
generated spaceships. The offline generated spaceships are used
as the initial population of an interactive evolution experiment in
which players are asked to choose spaceships according to their
visual taste: the impact of the various visual properties is adjusted
based on player preferences and new content is generated online
based on the updated computational model of visual aesthetics
of the player. Results are presented which show the potential of
the approach in generating content which is based on subjective
criteria of visual aesthetics.Thanks to all the participants of the interactive evolution
experiement. The research was supported, in part, by the
FP7 ICT project SIREN (project no: 258453) and by the
Danish Research Agency, Ministry of Science, Technology
and Innovation project AGameComIn; project number: 274-
09-0083.peer-reviewe
Neuroevolutionary constrained optimization for content creation
This paper presents a constraint-based procedural
content generation (PCG) framework used for the creation of
novel and high-performing content. Specifically, we examine
the efficiency of the framework for the creation of spaceship
design (hull shape and spaceship attributes such as weapon and
thruster types and topologies) independently of game physics
and steering strategies. According to the proposed framework,
the designer picks a set of requirements for the spaceship
that a constrained optimizer attempts to satisfy. The constraint
satisfaction approach followed is based on neuroevolution;
Compositional Pattern-Producing Networks (CPPNs) which
represent the spaceship’s design are trained via a constraintbased
evolutionary algorithm. Results obtained in a number
of evolutionary runs using a set of constraints and objectives
show that the generated spaceships perform well in movement,
combat and survival tasks and are also visually appealing.peer-reviewe
Deep learning for video game playing
In this article, we review recent Deep Learning advances in the context of
how they have been applied to play different types of video games such as
first-person shooters, arcade games, and real-time strategy games. We analyze
the unique requirements that different game genres pose to a deep learning
system and highlight important open challenges in the context of applying these
machine learning methods to video games, such as general game playing, dealing
with extremely large decision spaces and sparse rewards
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Discovering multi-purpose modules through deep multitask learning
Machine learning scientists aim to discover techniques that can be applied across diverse sets of problems. Such techniques need to exploit regularities that are shared across tasks. This begs the question: What shared regularity is not yet being exploited? Complex tasks may share structure that is difficult for humans to discover. The goal of deep multitask learning is to discover and exploit this structure automatically by training a joint model across tasks. To this end, this dissertation introduces a deep multitask learning framework for collecting generic functional modules that are used in different ways to solve different problems. Within this framework, a progression of systems is developed based on assembling shared modules into task models and leveraging the complementary advantages of gradient descent and evolutionary optimization. In experiments, these systems confirm that modular sharing improves performance across a range of application areas, including general video game playing, computer vision, natural language processing, and genomics; yielding state-of-the-art results in several cases. The conclusion is that multi-purpose modules discovered by deep multitask learning can exceed those developed by humans in performance and generality.Computer Science
Co-creating game content using an adaptive model of user taste
Mixed-initiative procedural content generation can augment
and assist human creativity by allowing the algorithm
to take care of the mechanisable parts of content creation,
such as consistency and playability checking. But it can also
enhance human creativity by suggesting new directions and
structures, which the designer can choose to adopt or not.peer-reviewe
Evolutionary design of deep neural networks
Mención Internacional en el título de doctorFor three decades, neuroevolution has applied evolutionary computation to the optimization of
the topology of artificial neural networks, with most works focusing on very simple architectures.
However, times have changed, and nowadays convolutional neural networks are the industry and
academia standard for solving a variety of problems, many of which remained unsolved before the
discovery of this kind of networks.
Convolutional neural networks involve complex topologies, and the manual design of these
topologies for solving a problem at hand is expensive and inefficient. In this thesis, our aim is to
use neuroevolution in order to evolve the architecture of convolutional neural networks.
To do so, we have decided to try two different techniques: genetic algorithms and grammatical
evolution. We have implemented a niching scheme for preserving the genetic diversity, in order
to ease the construction of ensembles of neural networks. These techniques have been validated
against the MNIST database for handwritten digit recognition, achieving a test error rate of 0.28%,
and the OPPORTUNITY data set for human activity recognition, attaining an F1 score of 0.9275.
Both results have proven very competitive when compared with the state of the art. Also, in all
cases, ensembles have proven to perform better than individual models.
Later, the topologies learned for MNIST were tested on EMNIST, a database recently introduced
in 2017, which includes more samples and a set of letters for character recognition. Results have
shown that the topologies optimized for MNIST perform well on EMNIST, proving that architectures
can be reused across domains with similar characteristics.
In summary, neuroevolution is an effective approach for automatically designing topologies for
convolutional neural networks. However, it still remains as an unexplored field due to hardware
limitations. Current advances, however, should constitute the fuel that empowers the emergence of
this field, and further research should start as of today.This Ph.D. dissertation has been partially supported by the Spanish Ministry of Education, Culture and Sports under FPU fellowship with identifier FPU13/03917.
This research stay has been partially co-funded by the Spanish Ministry of Education, Culture and Sports under FPU short stay grant with identifier EST15/00260.Programa Oficial de Doctorado en Ciencia y Tecnología InformáticaPresidente: María Araceli Sanchís de Miguel.- Secretario: Francisco Javier Segovia Pérez.- Vocal: Simon Luca
Deep Innovation Protection: Confronting the Credit Assignment Problem in Training Heterogeneous Neural Architectures
Deep reinforcement learning approaches have shown impressive results in a
variety of different domains, however, more complex heterogeneous architectures
such as world models require the different neural components to be trained
separately instead of end-to-end. While a simple genetic algorithm recently
showed end-to-end training is possible, it failed to solve a more complex 3D
task. This paper presents a method called Deep Innovation Protection (DIP) that
addresses the credit assignment problem in training complex heterogenous neural
network models end-to-end for such environments. The main idea behind the
approach is to employ multiobjective optimization to temporally reduce the
selection pressure on specific components in multi-component network, allowing
other components to adapt. We investigate the emergent representations of these
evolved networks, which learn to predict properties important for the survival
of the agent, without the need for a specific forward-prediction loss
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Evolutionary neural architecture search for deep learning
Deep neural networks (DNNs) have produced state-of-the-art results in many benchmarks and problem domains.
However, the success of DNNs depends on the proper configuration of its architecture and hyperparameters.
DNNs are often not used to their full potential because it is difficult to determine what architectures and hyperparameters should be used.
While several approaches have been proposed, computational complexity of searching large design spaces makes them impractical for large modern DNNs.
This dissertation introduces an efficient evolutionary algorithm (EA) for simultaneous optimization of DNN architecture and hyperparameters.
It builds upon extensive past research of evolutionary optimization of neural network structure.
Various improvements to the core algorithm are introduced, including:
(1) discovering DNN architectures of arbitrary complexity;
(1) generating modular, repetitive modules commonly seen in state-of-the-art DNNs;
(3) extending to the multitask learning and multiobjective optimization domains;
(4) maximizing performance and reducing wasted computation through asynchronous evaluations.
Experimental results in image classification, image captioning, and multialphabet character recognition show that the approach is able to evolve networks that are competitive with or even exceed hand-designed networks.
Thus, the method enables an automated and streamlined process to optimize DNN architectures for a given problem and can be widely applied to solve harder tasks.Computer Science
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