Mexico

This paper describes a system for the design or redesign of movie posters. The main reasoning strategy used by the system is case-based reasoning (Leake 1996), which requires two important sub-tasks to be performed: case retrieval and case adaptation. We have used the random forest algorithm to implement the case retrieval subtask, as it allows the system to formulate a generalization of the features of all the top matching cases that are deter-mined to be most relevant to the new (re)design desired. We have used heuristic rules to implement the case ad-aptation task which results in generating the suggestion for poster composition. These heuristic rules are based on the Gestalt theory of composition (Arnheim 1974) and the requirements specified by the user.

ICCC&#39;21

A Case-Based Approach to Creating Movie Poster Compositions

gestalt

posters

visual creativity

movie

heuristics

random forest

The 9th Brazilian Conference on Intelligent Systems (BRACIS), the 16h Encontro Nacional de Inteligência Artificial e Computacional (ENIAC), and the 12th Concurso de Teses e Dissertações em Inteligência Artificial e Computacional (CTDIAC) was held from 20 to 23 October, 2020.

BRACIS originated from the combination of the two most important scientific events in Brazil in Artificial Intelligence (AI) and Computational Intelligence (CI): the Brazilian Symposium on Artificial Intelligence - SBIA (22 editions), and the Brazilian Symposium on Neural Networks - SBRN (13 editions). ENIAC is a national forum for researchers, practitioners, educators and students to present and discuss innovations, trends, experiences, developments and work in progress in the fields of Artificial and Computational Intelligence. CTDIAC is a satellite event whose goals are to disseminate and award the best dissertations and theses (MSc and PhDs concluded in Brazilian universities from June 2018 to May 2020) in Artificial and Computational Intelligence areas.

In this year, the Symposium on Knowledge Discovery, Mining and Learning (KDMiLe) will be held in conjunction with BRACIS 2020. This sysposium aims at integrating researchers, practitioners, developers, students and users to present their research results, to discuss ideas, and to exchange techniques, tools, and practical experiences – related to the Data Mining and Machine Learning areas.

BRACIS 2020

technical paper

### **Welcome to the Twelfth International Conference on Computational Creativity, ICCC’21!**

<iframe width=1280 height=720 style="border:0;" scrolling="no" src="https://screencast-o-matic.com/player/crQbitV66dw?width=1280&height=720&ff=1&title=0" allowfullscreen="true"></iframe>

Computational creativity is an interdisciplinary endeavor that is located at the intersection of the fields of artificial intelligence, cognitive psychology, philosophy, social sciences and the arts. The goal of computational creativity is to model, simulate or replicate creativity using a computer, to achieve one of several ends:

* to construct a program or computer capable of showing some level of creativity
* to better understand human creativity and to formulate an algorithmic perspective on creative behavior in humans
* to design programs that can enhance human creativity without necessarily being creative themselves

<br>The ICCC conference series organized by the [**Association for Computational Creativity**](https://computationalcreativity.net/) since 2010 is the only scientific conference that focuses on computational creativity alone and also covers all aspects of it.



To access the Twelfth International Conference on Computational Creativity, ICCC’21, you need to register for the conference.  Instructions on how to access the event will be provided directly to attendees and included on the Twelfth International Conference on Computational Creativity, ICCC’21 conference website. 

[Register now](https://computationalcreativity.net/iccc21/registration/)

Register now

ICCC'21

Welcome to the Twelfth International Conference on Computational Creativity, ICCC’21!

We present a system that automatically generates music from visual art based on the perceived emotion of the given input. We propose the generated music as a framing device that can enhance the aesthetic experience of people viewing Computational Creativity (CC) outputs. In this paper, we carry out a first study to test this by comparing the aesthetic experience of viewing paintings generated by CC systems accompanied by either textual framing, our proposed musical framing or both. We evaluate our system by means of qualitative user evaluations, which require participants to rank their aesthetic experience from best to worst. The results from the study demonstrated that the musical framing generated by our system provided a better aesthetic experience for users compared to the textual framing. Furthermore, the results suggest that with more work, a combination of textual and musical framing could be used to further improve the aesthetic experience for people viewing visual CC art.

Framing through Music: A Pilot Study

We ask the question: to what extent can recent large-scale language and image generation models blend visual concepts? Given an arbitrary object, we identify a relevant object and generate a single-sentence description of the blend of the two using a language model. We then generate a visual depiction of the blend using a text-based image generation model. Quantitative and qualitative evaluations demonstrate the superiority of language models over classical methods for conceptual blending, and of recent large-scale image generation models over prior models for the visual depiction.

Visual Conceptual Blending with Large-scale Language and Vision Models

Computer vision research into image aesthetic quality assessment seeks to use machine learning to measure the aesthetic quality of images, which bears resemblance to a variety of topics within computational creativity, but has not been discussed in our community. To foster a conversation around this research literature, we trace the development of computer vision algorithms foraesthetic judgment over the past fifteen years and critically consider whether these algorithms actually cross the “aesthetic gap” proposed by the first researchers in this space. We then build towards a more fundamental question regarding machine learning and subjectivity.

What Does it Take to Cross the Aesthetic Gap? The Development of Image Aesthetic Quality Assessment in Computer Vision

MuSyFI is a system that tries to model an inspirational
computational creative process. It uses images as source
of inspiration and begins by implementing a possible
translation between visual and musical features. Results
of this mapping are fed to a Genetic Algorithm (GA)
to try to better model the creative process and produce
more interesting results. Three different musical artifacts
are generated: an automatic version, a co-created
version, and a genetic version. The automatic version
maps features from the image into musical features nondeterministically;
the co-created version adds harmony
lines manually composed by us to the automatic version;
finally, the genetic version applies a genetic algorithm
to a mixed population of automatic and co-created
artifacts.
The three versions were evaluated for six different
images by conducting surveys. They evaluated whether
people considered our musical artifacts music, if they
thought the artifacts had quality, if they considered the
artifacts ’novel’, if they liked the artifacts, and lastly if
they were able to relate the artifacts with the image in
which they were inspired. We gathered a total of 300
answers and overall people answered positively to all
questions, which confirms our approach was successful
and worth further exploring.

MuSyFI - Music Synthesis From Images

Intelligent learning environments have demonstrated effectiveness for providing individualized instruction to students of computer science (CS). However, the great potential of intelligent agents has not yet been explored within expressive environments, which are increasingly common for supporting and motivating K-12 students. This paper presents the prototype design and implementation of a novel Co-creative Artificial Intelligence (CAI) integrated within EarSketch, an online environment for learning introductory computing concepts through code-driven, sample-based music remixing. CAI is intended to scaffold student learning from EarSketch's expressive computing curriculum by co-creating algorithmic music alongside a human learner. This paper presents an initial version of CAI, which engages with EarSketch users by offering menu-based dialogue and suggestions based on the state of a project. We report a pilot study in classrooms, showing promising results in students' satisfaction with the system's capabilities. The findings of this pilot study suggest the ability of a co-creative agent to support users in learning and creative objectives, and should inspire research into combined computational and creative user models.

Supporting Computational Music Remixing with a Co-Creative Learning Companion

We describe a real-time system that receives a live audio stream from a jam session and generates lyric lines that are congruent with the live music being played. Two novel approaches are proposed to align the learned latent spaces of audio and text representations that allow the system to generate novel lyric lines matching live instrumental music. One approach is based on adversarial alignment of latent representations of audio and lyrics, while the other approach learns to transfer the topology from the music latent space to the lyric latent space. A user study with music artists using the system showed that the system was useful not only in lyric composition, but also encouraged the artists to improvise and find new musical expressions. Another user study demonstrated that users preferred the lines generated using the proposed methods to the lines generated by a baseline model.

LyricJam: A system for generating lyrics for live instrumental music

In conceptual blending, the transfer of properties from the input spaces relies on a shared semantic base. At the same time, interesting blends are supposed to resolve deep semantic clashes where many concept combinations correspond to impossible blends, i.e. blends whose input spaces lack any obvious similarities. Instead of a shared structure, the blends are based on bi- directional affordance structures. While humans can easily map this information, computational systems for creative constructions require an understanding of how these features relate to one another. In this paper, we discuss this problem from the perspective of linguistics and computational blending and propose a method combining theory weakening and semantic prioritisation. To demonstrate the problem space, we look at the Sesame Street character ‘The Cookie Monster’ and formalise the blending process using description logic.

Deciphering The Cookie Monster: A case study in impossible combinations

This article presents preliminary work on a creative chess engine that can be used to produce creative chess games or sequences. The contribution in this article is the creation of a creative chess engine that is then pitted against itself to form a creative system that outputs chess games. The chess engine is an extension to an existing chess engine that consists of forcing the existing engine to play more creative moves. It is in no way an improvement when compared to existing chess engines, even though it is based on the world's best: Stockfish. Letting the supposedly creative chess engine play against itself forms a creative system that outputs chess games. Through analyzing these games it might be possible to discover new chess openings or principles.

Producing creative chess through chess engine selfplay

Within the field of Computational Creativity, evaluation is one of the most important and more difficult tasks. Sometimes evaluation is part of the creative systems themselves, becoming an internal evaluation. Being a module of a creative system, it is useful to evaluate how effective this internal evaluation is.
In this paper, we propose a procedure for the (meta-)evaluation of internal evaluation modules, that allows for incremental development of both the evaluation module and the creative system, which are considered fully independent of each other. The procedure works by statistically comparing the evaluation of the average output of the generation system with the best results from the same, to see if the evaluation procedure can statistically distinguish the two. We then show how to apply the procedure giving one example evaluating a module we designed to assess structural coherence in generated folk music.

Meta-Evaluating Quantitative Internal Evaluation: a Practical Approach for Developers

Software design patterns can be helpful in describing the architecture of a system. Our objective is to obtain a broad overview of the current state-of-the-art of software design patterns used in Computational Creative (CC) systems. We conducted a systematic mapping study using manual and snowballing search techniques. Only 7 primary studies are identified in the CC community that explicitly mention the use of design patterns. Within these primary studies, 14 design patterns are mentioned, 12 of which are user-interaction design patterns rather than software design patterns describing the architecture of the system. The small number of primary studies indicates a gap in CC literature regarding the use of software design patterns in CC systems and motivates the need for research to identify software design patterns specific to CC systems.

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Optimizing Random Forest from the pondering of regression tree leaves

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