Mixed-initiative co-creativity

Creating and designing with a machine: do we merely create together (co-create) or can a machine truly foster our creativity as human creators? When does such co-creation foster the co-creativity of both humans and machines? This paper investigates the simultaneous and/or iterative process of human and computational creators in a mixed-initiative fashion within the context of game design and attempts to draw from both theory and praxis towards answering the above questions. For this purpose, we first discuss the strong links between mixed-initiative co-creation and theories of human and computational creativity. We then introduce an assessment methodology of mixed-initiative co-creativity and, as a proof of concept, evaluate Sentient Sketchbook as a co-creation tool for game design. Core findings suggest that tools such as Sentient Sketchbook are not mere game authoring systems or mere enablers of creation but, instead, foster human creativity and realize mixed-initiative co-creativity.peer-reviewe

Boosting Mixed-Initiative Co-Creativity in Game Design: A Tutorial

In recent years, there has been a growing application of mixed-initiative co-creative approaches in the creation of video games. The rapid advances in the capabilities of artificial intelligence (AI) systems further propel creative collaboration between humans and computational agents. In this tutorial, we present guidelines for researchers and practitioners to develop game design tools with a high degree of mixed-initiative co-creativity (MI-CCy). We begin by reviewing a selection of current works that will serve as case studies and categorize them by the type of game content they address. We introduce the MI-CCy Quantifier, a framework that can be used by researchers and developers to assess co-creative tools on their level of MI-CCy through a visual scheme of quantifiable criteria scales. We demonstrate the usage of the MI-CCy Quantifier by applying it to the selected works. This analysis enabled us to discern prevalent patterns within these tools, as well as features that contribute to a higher level of MI-CCy. We highlight current gaps in MI-CCy approaches within game design, which we propose as pivotal aspects to tackle in the development of forthcoming approaches.Comment: 34 pages, 11 figure

Can computers foster human users' creativity? Theory and praxis of mixed-initiative co-creativity

This article discusses the impact of artificially intelligent computers to the process of design, play and educational activities. A computational process which has the necessary intelligence and creativity to take a proactive role in such activities can not only support human creativity but also foster it and prompt lateral thinking. The argument is made both from the perspective of human creativity, where the computational input is treated as an external stimulus which triggers re-framing of humans’ routines and mental associations, but also from the perspective of computational creativity where human input and initiative constrains the search space of the algorithm, enabling it to focus on specific possible solutions to a problem rather than globally search for the optimal. The article reviews four mixed-initiative tools (for design and educational play) based on how they contribute to human-machine co-creativity. These paradigms serve different purposes, afford different human interaction methods and incorporate different computationally creative processes. Assessing how co-creativity is facilitated on a per-paradigm basis strengthens the theoretical argument and provides an initial seed for future work in the burgeoning domain of mixed-initiative interaction.peer-reviewe

Can computers foster human users' creativity? Theory and praxis of mixed-initiative co-creativity

This article discusses the impact of artificially intelligent computers to the process of design, play and educational activities. A computational process which has the necessary intelligence and creativity to take a proactive role in such activities can not only support human creativity but also foster it and prompt lateral thinking. The argument is made both from the perspective of human creativity, where the computational input is treated as an external stimulus which triggers re-framing of humans’ routines and mental associations, but also from the perspective of computational creativity where human input and initiative constrains the search space of the algorithm, enabling it to focus on specific possible solutions to a problem rather than globally search for the optimal. The article reviews four mixed-initiative tools (for design and educational play) based on how they contribute to human-machine co-creativity. These paradigms serve different purposes, afford different human interaction methods and incorporate different computationally creative processes. Assessing how co-creativity is facilitated on a per-paradigm basis strengthens the theoretical argument and provides an initial seed for future work in the burgeoning domain of mixed-initiative interaction.peer-reviewe

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

AudioInSpace : exploring the creative fusion of generative audio, visuals and gameplay

Computer games are unique creativity domains in that they elegantly fuse several facets of creative work including visuals, narra- tive, music, architecture and design. While the exploration of possibil- ities across facets of creativity o ers a more realistic approach to the game design process, most existing autonomous (or semi-autonomous) game content generators focus on the mere generation of single domains (creativity facets) in games. Motivated by the sparse literature on mul- tifaceted game content generation, this paper introduces a multifaceted procedural content generation (PCG) approach that is based on the in- teractive evolution of multiple arti cial neural networks that orchestrate the generation of visuals, audio and gameplay. The approach is evaluated on a spaceship shooter game. The generated artifacts|a fusion of audio- visual and gameplay elements | showcase the capacity of multifaceted PCG and its evident potential for computational game creativity.This re-search is supported, in part, by the FP7 ICT project C2Learn (project no: 318480) and by the FP7 Marie Curie CIG project AutoGameDesign (project no: 630665).peer-reviewe

The Case for a Mixed-Initiative Collaborative Neuroevolution Approach

It is clear that the current attempts at using algorithms to create artificial neural networks have had mixed success at best when it comes to creating large networks and/or complex behavior. This should not be unexpected, as creating an artificial brain is essentially a design problem. Human design ingenuity still surpasses computational design for most tasks in most domains, including architecture, game design, and authoring literary fiction. This leads us to ask which the best way is to combine human and machine design capacities when it comes to designing artificial brains. Both of them have their strengths and weaknesses; for example, humans are much too slow to manually specify thousands of neurons, let alone the billions of neurons that go into a human brain, but on the other hand they can rely on a vast repository of common-sense understanding and design heuristics that can help them perform a much better guided search in design space than an algorithm. Therefore, in this paper we argue for a mixed-initiative approach for collaborative online brain building and present first results towards this goal.Comment: Presented at WebAL-1: Workshop on Artificial Life and the Web 2014 (arXiv:1406.2507