Computational Creativity Research Articles

SKAITMENINIO KŪRYBIŠKUMO TAIKYMAS GENERUOJANT ŽAIDIMO OBJEKTUS / COMPUTATION CREATIVITY USING PROCEDURAL GENERATION FOR GAME OBJECT GENERATION

One of the hardest task for a machine is creativity. Computational creativity defines creative task completion for a machine. Three main creative content generation methods are: exploratory, combinatorial and transformational. Video game content can be generated using procedural generation. Computational creativity, procedural generation, and application are explained in this paper. Procedural level generator is used as a base and additional features are built on top of it. The main goal of this research and modification is to increase application creative value, variety and expression. Additional functionality consists of tree and texture generation.

Conceptual blending of high-level features and data-driven salience computation in melodic generation

Conceptual Blending (CB) theory describes the cognitive mechanisms underlying the way humans process the emergence of new conceptual spaces by blending two input spaces. CB theory has been primarily used as a method for interpreting creative artefacts, while recently it has been utilised in the context of computational creativity for algorithmic invention of new concepts. Examples in the domain of music include the employment of CB interpretatively as a tool to explain musical semantic structures based on lyrics of songs or on the relations between body gestures and music structures. Recent work on generative applications of CB has shown that proper low-level representation of the input spaces allows the generation of consistent and sometimes surprising blends. However, blending high-level features (as discussed in the interpretative studies) of music explicitly, is hardly feasible with mere low-level representation of objects. Additionally, selecting features that are more salient in the context of two input spaces and relevant background knowledge and should, thus, be preserved and integrated in new interesting blends has not yet been tackled in a cognitively pertinent manner. The paper at hand proposes a novel approach to generating new material that allows blending high-level features by combining low-level structures, based on statistically computed salience values for each high-level feature extracted from data. The proposed framework is applied to a basic but, at the same time, complicated field of music, namely melodic generation. The examples presented herein allow an insightful examination of what the proposed approach does, revealing new possibilities and prospects.

Creativity in the acquisition of computational thinking

ABSTRACTMany worldwide initiatives consider both creativity and computational thinking as crucial skills for future citizens, making them a priority for today's learners. We studied the associations between these two constructs among middle school students (N = 57), considering two types of creativity: a general creative thinking, and a specific computational creativity. We did so using learning analytics, specifically, by operationalizing a log-based assessment of computational creativity. We find some evidence for an association between Computational Creativity and Computational Thinking: Demonstrating originality in an early stage of the game is associated with succeeding in this stage relatively easily, however negatively associated with progressing farther in the game. We also find that Computational Creativity is better explained by a state- rather than a trait-model. No associations were found between Creative Thinking and Computational Thinking. Furthermore, we find some striking associations between the two measures of creativity.

An artificial intelligence based data-driven approach for design ideation

Ideation is a source of innovation and creativity, and is commonly used in early stages of engineering design processes. This paper proposes an integrated approach for enhancing design ideation by applying artificial intelligence and data mining techniques. This approach consists of two models, a semantic ideation network and a visual concepts combination model, which provide inspiration semantically and visually based on computational creativity theory. The semantic ideation network aims to provoke new ideas by mining potential knowledge connections across multiple knowledge domains, and this was achieved by applying “step-forward” and “path-track” algorithms which assist in exploring forward given a concept and in tracking back the paths going from a departure concept through a destination concept. In the visual concepts combination model, a generative adversarial networks model is proposed for generating images which synthesize two distinct concepts. An implementation of these two models was developed and tested in a design case study, which indicated that the proposed approach is able to not only generate a variety of cross-domain concept associations but also advance the ideation process quickly and easily in terms of quantity and novelty.

Can Machines Be Artists? A Deweyan Response in Theory and Practice

To speak comfortably of the machine artist (as outlined in the call for papers for this Special Issue) makes key assumptions about what it is to be an artist. It assumes, for instance, that the experience of living as an artist, which includes the socialisation, hard work, single-mindedness, and focused energy of creative activity, is incidental rather than essential since these aspects are not comfortably applicable to machines. Instead, it supposes that what is essential is the artistic product, and it is the similarity of human and machine products that makes it possible to speak of machine artists. This definition of art in terms of products is supported by modern psychological theories of creativity, defined as the generation of novel ideas which give rise to valuable products. These ideas take place in the mind or brain, regarded as a closed system within whose workings the secret of creativity will eventually be revealed. This is the framework of what is widely referred to as “cognitivism”. This definition in terms of novel ideas and valuable products has been widely assumed by artificial intelligence (AI) and computational creativity (CC), and this has been backed up through a particular version of the Turing Test. In this, a machine can be said to be a creative artist if its products cannot be distinguished from human art. However, there is another psychological view of creativity, that of John Dewey, in which a lived experience of inquiry and focus is essential to being creative. In this theory, creativity is a function of the whole person interacting with the world, rather than originating in the brain. This makes creativity a Process rather than a Cognitivist framework. Of course, the brain is crucial in a Process theory, but as part of an open system which includes both body and environment. Developments in “machine art” have been seen as spectacular and are widely publicised. But there may be a danger that these will distract from what we take to be the most exciting prospect of all. This is the contribution of computer technology to stimulate, challenge, and provoke artistic practice of all forms.

Expanding outcomes: Exploring varied conceptions of teacher learning in an online professional development experience

Online technologies hold promise to support more personalized teacher professional development (PD) experiences, but fulfilling this promise requires heightened attention to what teachers value about the outcomes of their learning. This paper uses the example of the Creative Computing Online Workshop (CCOW) to explore outcomes that teachers described as valuable: exposure to new ideas, rethinking classroom practice, and new relationships with their surrounding world. We discuss how the diversity, specificity, and nonlinearity of these outcomes extend teacher PD research, and suggest implications of this expanded framework for the design and evaluation of PD in both in-person and online contexts.

Computers take art in new directions, challenging the meaning of “creativity”

In an experiment carried out in early 2017, researchers from Rutgers University, Facebook, and the College of Charleston in South Carolina asked 18 volunteers to look at hundreds of images and rate them on characteristics such as “novelty,” “complexity,” and “structure.” Some of the images showed paintings created by human artists. The rest had been generated by new artificial intelligence (AI) algorithms, trained on more than 80,000 paintings from the past few hundred years, that had been developed to generate new visuals in a variety of styles. Digital artist Paul Brown’s prints include Reconfigurable Painting ( Left ), a work that has been reconfigured on occasion by the artist and even visitors; Wrapping Paper ( Center ), an unfinished work that explores different color combinations by using a single tile; and Long Loop ( Right ), which shows precompiled graphic sprites that were played back like a multipage flip-book, under the control of the generative program. Image credit: Paul Brown and Kevin Allen (photographer). The experiment’s participants, recruited from Amazon’s Mechanical Turk crowdsourced worker program, were also asked to decide if each artwork on the screen had been created by a human or a computer. That classification task suggests a sort of artistic Turing test for creativity. Namely, can an algorithm autonomously generate art that is indistinguishable from art made by people? And if so, does that mean the computer is “creative”—actually producing something new rather than merely emulating human artists? The researchers hypothesized that the participants would rank human-made art higher than machine-made in every category. They were wrong. In June 2017, at the Eighth International Conference on Computational Creativity in Atlanta, the researchers reported that, on average, participants rated computer-generated artworks as being more novel, complex, and surprising than the paintings made by people (1). The art generated by the algorithm was attributed more …

Conceptual Representations for Computational Concept Creation

Computational creativity seeks to understand computational mechanisms that can be characterized as creative. The creation of new concepts is a central challenge for any creative system. In this article, we outline different approaches to computational concept creation and then review conceptual representations relevant to concept creation, and therefore to computational creativity. The conceptual representations are organized in accordance with two important perspectives on the distinctions between them. One distinction is between symbolic, spatial and connectionist representations. The other is between descriptive and procedural representations. Additionally, conceptual representations used in particular creative domains, such as language, music, image and emotion, are reviewed separately. For every representation reviewed, we cover the inference it affords, the computational means of building it, and its application in concept creation.

Advanced tone rendition technique for a painting robot

One of the unresolved problems in the field of computer creativity is developing a robot capable of creating full-color images with artistic paints in a human-like manner. While several advanced painting machines have been presented up to date, high-grade color rendition remains one of the main bottlenecks in robotic painting. In this paper, we present a robotic setup for realistic grayscale painting. The key feature of our robot is a special paint mixing device aimed at improving tone rendition in comparison to previously reported approaches. We describe the main algorithmic and hardware solutions implemented in our robot as well as the first experimental results. The robot represents a 3-DoF CNC machine equipped with a brush, the paint mixing device and a syringe pump block for paint supplement. In our study, we focus on monochrome painting with black and white acrylic paints. A mathematical model of primary paint mixing is described. Two realistic artworks have been created during test runs, and their reproductions together with the source images are given. The accuracy of tone rendition was experimentally tested. Further research will be aimed at high-grade color rendition and creating full-color paintings.

Analyzing Creativity in the Light of Social Practice Theory.

In this work, starting from the social practice theory, we identified two kinds of creativity: a situational creativity that takes place when, starting from a defined situation, a social practice is played; and a creativity of habit that concerns the agents' capacity for generating new practices from habit when the situation is not defined or is unexpected. To test this hypothesis, the Torrance Test of Creative Thinking (Verbal Form A) was analyzed in the light of praxeology, and the results are analyzed in a computational creativity perspective.

Computational creativity infrastructure for online software composition: A conceptual blending use case

Computational creativity (CC) is a multidisciplinary research field, studying how to engineer software that exhibits behavior that would reasonably be deemed creative. This paper shows how composition of software solutions in this field can effectively be supported through a CC infrastructure that supports user-friendly development of CC software components and workflows, their sharing, execution, and reuse. The infrastructure allows CC researchers to build workflows that can be executed online and be easily reused by others through the workflow web address. Moreover, it enables the building of procedures composed of software developed by different researchers from different laboratories, leading to novel ways of software composition for computational purposes that were not expected in advance. This capability is illustrated on a workflow that implements a Concept Generator prototype based on the Conceptual Blending framework. The prototype consists of a composition of modules made available as web services, and is explored and tested through experiments involving blending of texts from different domains, blending of images, and poetry generation.

A big data approach to computational creativity: The curious case of Chef Watson

Computational creativity is an emerging branch of artificial intelligence that places computers in the center of the creative process. Broadly, creativity involves a generative step to produce many ideas and a selective step to determine the ones that are the best. Many previous attempts at computational creativity, however, have not been able to achieve a valid selective step. This paper shows how bringing data sources from the creative domain and from hedonic psychophysics together with machine learning and data analytics techniques can overcome this shortcoming to yield a system that can produce novel and high-quality creative artifacts. To demonstrate our data-driven approach, we developed and deployed a computational creativity system for culinary recipes and menus, Chef Watson, which can operate either autonomously or semiautonomously with human interaction. We present the basic system architecture, data engineering, and algorithms that are involved. Experimental results demonstrate the system passes the test for creativity based on the consensual assessment technique, producing a novel and flavorful recipe. Large-scale deployments are also discussed.

Mathematical limit theorems for computational creativity

Creativity is the generation of an idea or artifact judged to be novel and high-quality by a knowledgeable social group, and is often said to be the pinnacle of intelligence. Several computational creativity systems of various designs are now being demonstrated and deployed. These myriad design possibilities raise the natural question: Are there fundamental limits to creativity? Here, we define a mathematical abstraction to capture key aspects of combinatorial creativity and study fundamental tradeoffs between novelty and quality. The functional form of this fundamental limit resembles the capacity-cost relationship in information theory, especially when measuring novelty using Bayesian surprise—the relative entropy between the empirical distribution of an inspiration set and that set updated with the new idea or artifact. As such, we show how information geometry techniques provide insight into the limits of creativity and find that the maturity of the creative domain directly parameterizes the fundamental limit. This result is extended to the case when there is a diverse audience for creativity and when the quality function is not known but must be estimated from samples.

What is deemed computationally creative?

In this new era of computational creativity, where artificial intelligence (AI) systems are attempting to achieve human-level creativity, a set of golden questions needs to be answered, such as “What kind of systems are creative systems?” and “When does a system qualify as truly creative?” The existing generation of AI-driven cognitive systems is based on the goal of achieving human-level intelligence, not human-level creativity. Creativity is considered subjective with respect to both the application domains and the perceiving end-user. In this paper, we postulate the dimensions and factors that distinguish creativity and intelligence, such as novelty, value, surprise, influence, coherence, correctness, and comprehensibility. We group the application domains into time-dependent and time-independent ones and define a framework to describe these dimensions in each application. In addition to defining the factors that determine creativity, we also suggest ideas on how to evaluate these factors. We strongly believe that the proposed framework would act as the basis for building and evaluating creative systems and also provide us with the ultimate goal for achieving human-level creativity.

Eliciting Data Relations of IOT based on Creative Computing

Eliciting Data Relations of IOT based on Creative Computing

The Relationship between Various Consciousness about Computer Programming and Creativity

The Relationship between Various Consciousness about Computer Programming and Creativity

Preface: Advances in Computational Creativity Technology

Preface: Advances in Computational Creativity Technology

Applying Creative Computing in the Analysis of Smart Tourism Research in China

Applying Creative Computing in the Analysis of Smart Tourism Research in China

Pilot Study of Applying Creative Computing for the Activation of Intangible Cultural Heritage

Pilot Study of Applying Creative Computing for the Activation of Intangible Cultural Heritage

Applying Creative Computing in the Analysis of Smart Tourism Research in China

Applying Creative Computing in the Analysis of Smart Tourism Research in China