6,919 research outputs found
Feature selection and novelty in computational aesthetics
[Abstract] An approach for exploring novelty in expression-based evolutionary art systems is presented. The framework is composed of a feature extractor, a classifier, an evolutionary engine and a supervisor. The evolutionary engine exploits shortcomings of the classifier, generating misclassified instances. These instances update the training set and the classifier is re-trained. This iterative process forces the evolutionary algorithm to explore new paths leading to the creation of novel imagery. The experiments presented and analyzed herein explore different feature selection methods and indicate the validity of the approach.Portugal. Fundação para a CiĂȘncia e a Tecnologia; PTDC/EIAâEIA/115667/2009Galicia.ConsellerĂa de InnovaciĂłn, Industria e Comercio ; PGIDIT10TIC105008P
Multi-agent evolutionary systems for the generation of complex virtual worlds
Modern films, games and virtual reality applications are dependent on
convincing computer graphics. Highly complex models are a requirement for the
successful delivery of many scenes and environments. While workflows such as
rendering, compositing and animation have been streamlined to accommodate
increasing demands, modelling complex models is still a laborious task. This
paper introduces the computational benefits of an Interactive Genetic Algorithm
(IGA) to computer graphics modelling while compensating the effects of user
fatigue, a common issue with Interactive Evolutionary Computation. An
intelligent agent is used in conjunction with an IGA that offers the potential
to reduce the effects of user fatigue by learning from the choices made by the
human designer and directing the search accordingly. This workflow accelerates
the layout and distribution of basic elements to form complex models. It
captures the designer's intent through interaction, and encourages playful
discovery
The pictures we like are our image: continuous mapping of favorite pictures into self-assessed and attributed personality traits
Flickr allows its users to tag the pictures they like as âfavoriteâ. As a result, many users of the popular photo-sharing platform produce galleries of favorite pictures. This article proposes new approaches, based on Computational Aesthetics, capable to infer the personality traits of Flickr users from the galleries above. In particular, the approaches map low-level features extracted from the pictures into numerical scores corresponding to the Big-Five Traits, both self-assessed and attributed. The experiments were performed over 60,000 pictures tagged as favorite by 300 users (the PsychoFlickr Corpus). The results show that it is possible to predict beyond chance both self-assessed and attributed traits. In line with the state-of-the art of Personality Computing, these latter are predicted with higher effectiveness (correlation up to 0.68 between actual and predicted traits)
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
Personality in Computational Advertising: A Benchmark
In the last decade, new ways of shopping online have increased the
possibility of buying products and services more easily and faster
than ever. In this new context, personality is a key determinant
in the decision making of the consumer when shopping. A personâs
buying choices are influenced by psychological factors like
impulsiveness; indeed some consumers may be more susceptible
to making impulse purchases than others. Since affective metadata
are more closely related to the userâs experience than generic
parameters, accurate predictions reveal important aspects of userâs
attitudes, social life, including attitude of others and social identity.
This work proposes a highly innovative research that uses a personality
perspective to determine the unique associations among the
consumerâs buying tendency and advert recommendations. In fact,
the lack of a publicly available benchmark for computational advertising
do not allow both the exploration of this intriguing research
direction and the evaluation of recent algorithms. We present the
ADS Dataset, a publicly available benchmark consisting of 300 real
advertisements (i.e., Rich Media Ads, Image Ads, Text Ads) rated
by 120 unacquainted individuals, enriched with Big-Five usersâ
personality factors and 1,200 personal usersâ pictures
The Extended Importance of the Social Creation of Value in Evolutionary Processes
This is a single-authored paper delivered at the biennial European Conference on Artificial Intelligence (ECAI) in Riva del Garda, Italy 28 â 29 August 2006 and published in proceedings.
The paper proposed that computational modelling be employed in order to test two processes that might hypothetically distinguish particular dimensions of human creativity. The first process is identified by the researcher as one in which the pursuit of novelty in artistic invention â especially music â tends to words the production of increasingly perceptually complex artefacts. The second process moves from a perspective orientated towards the individual artist and the individual art workâs reception to a more collective one: namely, whether cultural behaviour that tends towards novelty might find itself being reinforced by clustering of similar activities. This latter process would be one that explains why the process of âmaking specialâ â that may distinguish art in an anthropological sense â is one that forms particularly strong community bonds. These bonds between novelty seekers â which in the case of the researchers paper can be understood as musicians or artists â may reciprocally reinforce to support yet more novelty seeking.
The relation of art and the new is not itself innovative. Boris Groysâ âOn The Newâ provides a scoping of that territory. What is innovative is the proposal to use of computer simulation of individual and collective behaviour as a kind of artificial laboratory to determine the complex tendencies that animate these processes of novelty seeking and, by extension, artistic production.
Computationally simulating behaviour that parallels both novelty-seeking as an individual practice (the artist) and the emergence of clusters of novelty seekers (the artistic styles) may, according to the researcher, provide us with new insights the historical evolution of creativity
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