Human-AI co-creativity involves both humans and AI collaborating on a shared creative product as partners. In a creative collaboration, interaction dynamics, such as turn-taking, contribution type, and communication, are the driving forces of the co-creative process. Therefore the interaction model is a critical and essential component for effective co-creative systems. There is relatively little research about interaction design in the co-creativity field, which is reflected in a lack of focus on interaction design in many existing co-creative systems. The primary focus of co-creativity research has been on the abilities of the AI. This paper focuses on the importance of interaction design in co-creative systems with the development of the Co-Creative Framework for Interaction design (COFI) that describes the broad scope of possibilities for interaction design in co-creative systems. Researchers can use COFI for modeling interaction in co-creative systems by exploring alternatives in this design space of interaction. COFI can also be beneficial while investigating and interpreting the interaction design of existing co-creative systems. We coded a dataset of existing 92 co-creative systems using COFI and analyzed the data to show how COFI provides a basis to categorize the interaction models of existing co-creative systems. We identify opportunities to shift the focus of interaction models in co-creativity to enable more communication between the user and AI leading to human-AI partnerships.

pdf/designing-creative-ai-partners-with-cofi-a-framework-for-24ebkvxl.pdf

Designing Creative AI Partners with COFI: A Framework for Modeling Interaction in Human-AI Co-Creative Systems

To achieve meaningful 21st century teaching and learning within a rapidly transforming world, universities must remain nimble enough to address the diversity of their students with innovative approaches, either through the experiential-learning benefits of coop and internships or the instructional-technology benefits of online learning. Founded in 2002, CET&L commits to providing UC faculty with the cutting-edge professional-development opportunities they need to help our students become successful graduates.

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2008-2009 Annual Report

Recently, the production of big games usually requires lots of money but it is very difficult to gain attention from players. Although gamers expectations are very high, game companies resource is limited to maximize the quality of the games. Mod (game contents editing) can be one of the solutions to this problem. It makes gamers satisfy themselves from creating and sharing their Mod. It increases gamers playing time and sales. But there are only few users who have knowledge and special ability to create Mod. So most gamers just use Mod made by someone else. In this paper, we propose a method to generate Mod for 3D game objects and textures. Our method enables the construction of a 3-dimension object using the grown building footprints by L-system, and it also provides a web-based interactive genetic algorithm interface to users for changing shape. Furthermore, it also provides a function to evolve various texture images from an original texture file. We demonstrated the possibility of our systems using TORCS (The Open Racing Car Simulator). These results reveal that 3D objects and textures can be created from our method by amateur users.

3D game model and texture generation using interactive genetic algorithm

In terms of the teaching process of matte painting, it is essential for students to develop a sound understanding of the relationship between virtual and physical environments. In this study, first-person view (FPV) drones are applied to matte painting courses to evaluate the effectiveness of the teaching, and to propose more effective design suggestions for FPV drones that are more suitable for teaching. This provides students with a better learning environment using a digital education system. The results of the study indicate that the flow experience, learning interest, and continuous learning intention of students who use FPV drones in matte painting are significantly greater than those of students who only utilize traditional teaching methods. Furthermore, the technology incentive model (TIM) was developed in this study after being verified by the structural equation model. The results demonstrate that the second-order construct ‘technology incentive’ comprising perceived interactivity, perceived vividness, and novel experience positively influence students’ learning interest and continuous learning intentions under the mediation of flow experience.

/pdf/examining-the-influence-of-using-first-person-view-drones-as-356be271.pdf

Examining the Influence of Using First-Person View Drones as Auxiliary Devices in Matte Painting Courses on College Students’ Continuous Learning Intention

This paper is concerned with the idea of fitness in art and music systems that are based on evolutionary computation. A taxonomy is presented of the ways in which fitness is used in such systems, with two dimensions: what the fitness function is applied to, and the basis by which the function is constructed. A large collection of papers are classified using this taxonomy. The paper then discusses a number of ideas that have not been used for fitness evaluation in evolutionary art and which might be valuable in future developments: memory, scaffolding, connotation and web search.

/pdf/fitness-in-evolutionary-art-and-music-a-taxonomy-and-future-42mcjryrjo.pdf

Fitness in Evolutionary Art and Music: A Taxonomy and Future Prospects

Directing a group behavior towards interesting and complex motion can and should be intuitive, iterative, and often participatory. Toward this end, we present a choreographic system that enables designers to explore a motion space based on a parametric model of behaviors. Designers may work with the system by moving back and forth through two complementary stages: first, using an evolutionary algorithm to traverse the space of behavior possibilities, allowing designers to emphasize desired kinds of motion while leaving room for an element of the unexpected, and second, using selected behaviors to direct the group motion of simple performing creatures. In the second stage, evolved group motion behaviors from the first stage are used alongside existing high-level parametric rules for local articulated motion.

Creating choreography with interactive evolutionary algorithms

We present an intuitive method for novice users to interactively design custom populations of stylized, heterogeneous motion, from one input motion. The user sets up lattice deformers which are used by a genetic algorithm to manipulate the animation channels of the input motion and create new motion variants. Our interactive evolutionary design environment allows the user to traverse the available space of possibilities, presents the user with populations of motion, and gradually converges to a satisfactory set of solutions. Each generated motion can undergo a filtering process subject to user-specified, high-level metrics to produce a result crafted to fit the designer’s interest. We demonstrate application to both character animation and particle systems.

Interactive Evolution for Designing Motion Variants

Evolutionary algorithms have shown themselves to be useful interactive design tools. However, current algorithms only receive feedback about candidate fitness at the whole-candidate level. In this paper we describe a model-free method, using sensitivity analysis, which allows designers to provide fitness feedback to the system at the component level. Any part of a candidate can be marked by the designer as interesting (i.e. having high fitness). This has the potential to improve the design experience in two ways: (1) The finer-grain guidance provided by partial selections facilitates more precise iteration on design ideas so the designer can maximize her energy and attention. (2) When steering the evolutionary system with more detailed feedback, the designer may discover greater feelings of satisfaction with and ownership over the final designs.

Inverse mapping with sensitivity analysis for partial selection in interactive evolution

Recent research in the area of evolutionary algorithms and interactive design tools for ideation has investigated how sensitivity analysis can be used to enable region-of-interest selection on design candidates. Even though it provides more precise control over the evolutionary search to the designer, the existing methodology for this enhancement to evolutionary algorithms does not make full use of the information provided by sensitivity analysis and may lead to premature convergence. In this paper, we describe the shortcomings of previous research on this topic and introduce an approach that mitigates the problem of early convergence. A discussion of the trade-offs of different approaches to sensitivity analysis is provided as well as a demonstration of this new technique on a parametric model built for character design ideation.

Probabilistic Decision Making for Interactive Evolution with Sensitivity Analysis

This paper presents an intuitive method for novice users to interactively design custom populations of stylized, heterogeneous motion, from one input motion clip, thus allowing the user to amplify an existing database of motions. We allow the user to set up lattice deformers which are used by a genetic algorithm to manipulate the animation channels of the input motion and create new motion variations. Our interactive evolutionary design environment allows the user to traverse the available space of possible motions, presents the user with populations of motion, and gradually converges to a satisfactory set of solutions. Each generated motion sequence can undergo a motion ltering process subject to user-specied, high-level metrics to produce a result crafted to t the designer's interest.

Interactive evolutionary design of motion variants

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