Mexico

Generative deep learning systems offer powerful tools for artefact generation, given their ability to model distributions of data and generate high-fidelity results. In the context of computational creativity, however, a major shortcoming is that they are unable to explicitly diverge from the training data in creative ways and are limited to fitting the target data distribution. To address these limitations, there have been a growing number of approaches for optimising, hacking and rewriting these models in order to actively diverge from the training data. We present a taxonomy and comprehensive survey of the state of the art of active divergence techniques, highlighting the potential for computational creativity researchers to advance these methods and use deep generative models in truly creative systems.

ICCC&#39;21

Active Divergence with Generative Deep Learning - A Survey and Taxonomy

novelty generation

co-creation

creativity support

active divergence

generative deep learning



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TOUR 2021

This conference provides an interactive, dynamic, and shared international platform for global academics and practitioners to discuss how to enhance research applications and knowledge management through resilience, adaptability, digital intelligence, innovation, and smart knowledge in bringing our industry and research back to normal. This summit particularly offers unique opportunities to unfold and embrace various repercussions of COVID-19 pandemic with various stakeholders, including academic researchers, industry professionals, and government officials, through dialogues on how to better utilize research outputs for the benefits of the industry, in a resilient, intelligent and innovative way.

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/)

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ICCC'21

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

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.

Software Design Patterns of Computational Creativity: A Systematic Mapping Study

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.

A Case-Based Approach to Creating Movie Poster Compositions

In this paper we argue that the computational creativity community would benefit by working together to create a methodology for a systematic assessment and comparison of our creative agents. To achieve this goal, we suggest collaborating with experts in real-world environments to develop what we refer to as Rich Creative Environment Experience (RICEE). We describe a work in progress, where two artists and one CC scientist collaborate, to illustrate a possible way to build a RICEE. Based on this experience, we suggest some initial steps to start the development of this methodology. Thus, the purpose of this paper is to motivate the CC community to collaborate in the development of a methodology for the design and implementation of RICEEs.

The stone of madness meets AI

We conjecture that creativity and the perception of creativity are, at least to some extent, shaped by embodiment. This makes embodiment highly relevant for computational creativity (CC) research, but existing research is scarce and the use of the concept highly ambiguous. We overcome this situation by means of a systematic review and a prescriptive analysis of publications at the International Conference on Computational Creativity. We adopt and extend an established typology of embodiment to resolve ambiguity through identifying and comparing different usages of the concept. We collect, contextualise and highlight opportunities and challenges in embracing embodiment in CC as a reference for research, and put forward important directions to further the embodied CC research programme.

Embodiment and Computational Creativity (Study Paper)

In this presentation we focus on two of the major concepts we present in the main paper, namely: how we can understand the Typicality and Novelty of an object with Models created by an observer, and sophistication which lets us analyse the creator of a creative object. We also talk about highly sophisticated objects which can be regarded as masterpieces of the creator. Finally, we review some related works in computational aesthetics and how they can be improved. 



Active Divergence with Generative Deep Learning - A Survey and Taxonomy

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