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Computers can help us to trigger our intuition about how to solve a problem. But how does a computer take into account what a user wants and update these triggers? User preferences are hard to model as they are by nature vague, depend on the user&#39;s background and are not always deterministic, changing depending on the context and process under which they were established. We pose that the process of preference discovery should be the object of interest in computer aided design or ideation. The process should be transparent, informative, interactive and intuitive. We formulate HyperPref, a cyclic co-creative process between human and computer, which triggers the user&#39;s intuition about what is possible and is updated according to what the user wants based on their decisions. We combine quality diversity algorithms, a divergent optimization method that can produce many, diverse solutions, with variational autoencoders to both model that diversity as well as the user&#39;s preferences, discovering the preference hypervolume within large search spaces.

ICCC&#39;20

A Deep Dive Into Exploring the Preference Hypervolume

quality diversity

interactive design

co-creativity

Computational creativity is the art, science, philosophy and engineering of computational systems which, by taking on particular responsibilities, exhibit behaviours that unbiased observers would deem to be creative. As a field of research, this area is thriving, with progress in formalising what it means for software to be creative, along with many exciting and valuable applications of creative software in the sciences, the arts, literature, gaming and elsewhere.

The ICCC conference series organized by the Association for Computational Creativity since 2010 is the only scientific conference that focuses on computational creativity alone and also covers all aspects of it.


ICCC'20

The ICCC conference series organized by the Association for Computational Creativity since 2010 is the only scientific conference that focuses on computational creativity alone and also covers all aspects of it.
Computational creativity is the art, science, philosophy and engineering of computational systems which, by taking on particular responsibilities, exhibit behaviors that unbiased observers would deem to be creative. As a field of research, this area is thriving, with progress in formalizing what it means for software to be creative, along with many exciting and valuable applications of creative software in the sciences, the arts, literature, gaming and elsewhere.



Computers can help us to trigger our intuition about how to solve a problem. But how does a computer take into account what a user wants and update these triggers? User preferences are hard to model as they are by nature vague, depend on the user's background and are not always deterministic, changing depending on the context and process under which they were established. We pose that the process of preference discovery should be the object of interest in computer aided design or ideation. The process should be transparent, informative, interactive and intuitive. We formulate HyperPref, a cyclic co-creative process between human and computer, which triggers the user's intuition about what is possible and is updated according to what the user wants based on their decisions. We combine quality diversity algorithms, a divergent optimization method that can produce many, diverse solutions, with variational autoencoders to both model that diversity as well as the user's preferences, discovering the preference hypervolume within large search spaces.

poster

"Casual creators offer an enjoyable and readily accessible creative experience by enabling a safe and easy exploration of a creative space. This explorative and intrinsically motivating process lends itself to education applications, giving rise to a new category of casual creators, which we call Educational Creators. We illustrate this concept through EarthMood, an educational creator for deepening students’ understanding of Climate Change. We demonstrate EarthMood through historical data, showing the deterioration of the planet, as well as on recent data, illustrating an improvement in climate due to COVID-19. The remotely-accessible nature of educational creators makes them applicable to both traditional and remote
learning settings."

A Climate Change Educational Creator

"There are two classes of generative art approaches: neural, where a deep model is trained to generate samples from a data distribution, and “symbolic” or algorithmic, where an artist designs the primary parameters and an autonomous system generates samples within these constraints. In this work, we propose a new hybrid
genre: neuro-symbolic generative art. As a preliminary study, we train a generative deep neural network on samples from the symbolic approach. We demonstrate through human studies that subjects find the final artifacts and the creation process using our neuro-symbolic approach to be more creative than the symbolic approach 61% and 82% of the time respectively."

Neuro-Symbolic Generative Art: A Preliminary Study

We present how we built the dataset and models, summarize evaluation results and show a few examples of generated music at the end.

Automated Music Generation for Visual Art through Emotion

We present a proposal for meta-level evaluation in creative systems. In this paper, the Mexica creative system is the subject of our analysis. To achieve meta-level evaluation, we have constructed a program that interacts with Mexica's inputs and its outputs. The meta-level system evaluates the results and makes decisions based on them. For decision-making it includes heuristics that are defined in terms of Mexica's results; these are rules particular to the object- level system. We also include other general metrics that apply in any other creative system. For example, Ritchie's criteria for evaluating creative systems are included in this proposal.

Meta-level Evaluation and Transformational Creativity; An analysis of MEXICA

"Music variations consists of modifying an original music piece so that it remains harmonious and provides a sense of novelty while still remain relatable to the original song. The melody should progress in a harmonious way and under a reasonable chord hierarchy, due to this constraints, music 
variations can be considered as a rule based composition system. In this research, we develop a genetic algorithm to compose a variation on a given music piece. To achieve this we design a novel evaluation function and evolutionary operators which favor the modification process. These are designed to include components of melody similarity and tempo diversity in addition to harmony. The experimental results show that the proposed  system can generate variations that preserve musicality. A human evaluation study is also included to validate the proposed evaluation function. Additionally a link to listen to our generated compositions is provided."

Melody Similarity and Tempo Diversity as Evolutionary Factors for Music Variations by Genetic Algorithms

The automated evaluation of creative products promises both good-and-scalable creativity assessments and new forms of visual analysis of whole corpora. Where creative works are not ‘born digital’, such automated evaluation requires fast and frugal ways of transforming them into data representations that can be meaningfully assessed with common creativity metrics like novelty. In this paper, we report the results of training a Spatiotemporal DeepInfomax Variational Autoencoder (STDIM-VAE) on a digital photo pool of 162 LEGO ducks to generate a phenotypical landscape of clusters of similar ducks and dissimilarity scores for individual ducks. Visual inspection suggests that our system produces plausible results from image pixels alone. We conclude that under certain conditions, STDIM-VAEs may provide fast and frugal ways of automatically assessing corpora of creative works.

Automatic Similarity Detection in LEGO Ducks

Natural language generation provides designers with methods for automatically generating text, e.g. for creating summaries, chatbots and game content. In practise, text generators are often either learned and hard to interpret, or created by hand using techniques such as grammars and templates. In this paper, we introduce a novel grammar induction algorithm for learning interpretable grammars for generative purposes, called GITTA. We also introduce the novel notion of template trees to discover latent templates in corpora to derive these generative grammars. By using existing human-created grammars, we found that the algorithm can reasonably approximate these grammars using only a few examples. These results indicate that GITTA could be used to automatically learn interpretable and easily modifiable grammars, and thus provide a stepping stone for human-machine co-creation of generative models.

Discovering Textual Structures: Generative Grammar Induction using Template Trees

We introduce CANIS, our computationally creative system for generating novel constellations. We explain the motivations behind its creation, how it works, and demonstrate several of its artifacts. 

Creative Constellation Generation

"Humor is incorporated into our daily interactions, but conceiving jokes ideas can be inherently difficult to produce spontaneously. The PAUL BOT system, “puns are usually lame but occasionally terrific”, includes features of the JAPE system by incorporating aspects of the SAD generator, templates, and sentence forms to automatically generate a pun (Ritchie 2003). A two-word database is composed of adjective-noun phrases that contain a homophone. The punchline to the pun is replacing a word in the two- word phrase with a homophone. A synonym is derived from the homophone, and a hypernym is chosen from the non- homophone word in the original phrase. The synonym and the hypernym are incorporated into a predetermined sentence structure to construct the question. Surveys were conducted to evaluate our artifacts produced by PAUL BOT in order to identify future improvements of the system. The creativity in the system is attributed to the novelty of the unique artifacts, the level of surprise, the typicality of the artifacts as classic puns, and the intentionality by providing the connection between the setup and punchline. This paper proposes the PAUL BOT system that incorporates the JAPE system to create puns in order to produce a humorous, creative system utilized for entertainment.
Source: github.com/knw7x9/PunGenerator"

Computational Humor: Automated Pun Generation

"We present ERwEM (Events Represented with Emotive Music), a system that generates cross-domain artefacts between text and music by drawing from emotional sentiments. Our system uses keywords extracted from a
news article to filter live-stream tweets containing these keywords and analyze the tweet collection’s emotion data. It then uses the emotion sentiment (a scale from positive to negative) to influence the mood of the music it writes using an adapted bi-axial long short term memory (BALSTM) model. From this, our system determines
which mode the generated song will be transposed to. We use the FACE model to characterize the creative aspects of our system as a collection of generative acts. We provide examples of the artefacts our system generates as well as the response we received from external sources who reviewed the generated songs."

A Deep Dive Into Exploring the Preference Hypervolume

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