Interpretation-driven mapping: A framework for conducting search and re-representation in parallel for computational analogy in design

This paper presents a framework for the interactions between the processes of mapping and rerepresentation within analogy making. Analogical reasoning systems for use in design tasks require representations that are open to being reinterpreted. The framework, interpretation-driven mapping, casts the process of constructing an analogical relationship as requiring iterative, parallel interactions between mapping and interpreting. This paper argues that this interpretation-driven approach focuses research on a fundamental problem in analogy making: how do the representations that make new mappings possible emerge during the mapping process? The framework is useful for both describing existing analogy-making models and designing future ones. The paper presents a computational model informed by the framework Idiom, which learns ways to reinterpret the representations of objects as it maps between them. The results of an implementation in the domain of visual analogy are presented to demonstrate its feasibility. Analogies constructed by the system are presented as examples. The interpretation-driven mapping framework is then used to compare representational change in Idiom to that in three previously published systems

A Framework for Dialogue-Based Human-AI Creative Collaboration

Human-AI co-creative collaboration has been proposed as a model that integrates the strengths of both humans and creative algorithms. Several frameworks have been developed to classify and guide the design of such systems. However, these models lack communication mechanisms that enable the emergence of a common ground between humans and machines through a mutual adaptation of understanding about goals and meanings, a crucial component in all collaborations. We argue that dialogue is a mechanism that serves this purpose and can be included in human-AI co-creative systems to that end. We propose a breakdown of dialogic creative interaction and use it to analyze co-creative dialogue with GPT-3

A Collaborative, Interactive and Context-Aware Drawing Agent for Co-Creative Design

Recent advances in text-conditioned generative models have provided us with neural networks capable of creating images of astonishing quality, be they realistic, abstract, or even creative. These models have in common that (more or less explicitly) they all aim to produce a high-quality one-off output given certain conditions, and in that they are not well suited for a creative collaboration framework. Drawing on theories from cognitive science that model how professional designers and artists think, we argue how this setting differs from the former and introduce CICADA: a Collaborative, Interactive Context-Aware Drawing Agent. CICADA uses a vector-based synthesis-by-optimisation method to take a partial sketch (such as might be provided by a user) and develop it towards a goal by adding and/or sensibly modifying traces. Given that this topic has been scarcely explored, we also introduce a way to evaluate desired characteristics of a model in this context by means of proposing a diversity measure. CICADA is shown to produce sketches of quality comparable to a human user's, enhanced diversity and most importantly to be able to cope with change by continuing the sketch minding the user's contributions in a flexible manner

Evaluation of Musical Creativity and Musical Metacreation Systems

The field of computational creativity, including musical metacreation, strives to develop artificial systems that are capable of demonstrating creative behavior or producing creative artefacts. But the claim of creativity is often assessed, subjectively only on the part of the researcher and not objectively at all. This article provides theoretical motivation for more systematic evaluation of musical metacreation and computationally creative systems and presents an overview of current methods used to assess human and machine creativity that may be adapted for this purpose. In order to highlight the need for a varied set of evaluation tools, a distinction is drawn among three types of creative systems: those that are purely generative, those that contain internal or external feedback, and those that are capable of reflection and self-reflection. To address the evaluation of each of these aspects, concrete examples of methods and techniques are suggested to help researchers (1) evaluate their systems' creative process and generated artefacts, and test their impact on the perceptual, cognitive, and affective states of the audience, and (2) build mechanisms for reflection into the creative system, including models of human perception and cognition, to endow creative systems with internal evaluative mechanisms to drive self-reflective processes. The first type of evaluation can be considered external to the creative system and may be employed by the researcher to both better understand the efficacy of their system and its impact and to incorporate feedback into the system. Here we take the stance that understanding human creativity can lend insight to computational approaches, and knowledge of how humans perceive creative systems and their output can be incorporated into artificial agents as feedback to provide a sense of how a creation will impact the audience. The second type centers around internal evaluation, in which the system is able to reason about its own behavior and generated output. We argue that creative behavior cannot occur without feedback and reflection by the creative/metacreative system itself. More rigorous empirical testing will allow computational and metacreative systems to become more creative by definition and can be used to demonstrate the impact and novelty of particular approaches

Designing to Leverage Presence in VR Rhythm Games

Rhythm games are known for their engaging gameplay and have gained renewed popularity with the adoption of virtual reality (VR) technology. While VR rhythm games have achieved commercial success, there is a lack of research on how and why they are engaging, and the connection between that engagement and immersion or presence. This study aims to understand how the design of two popular VR rhythm games, Beat Saber and Ragnarock, leverages presence to immerse players. Through a mixed-methods approach, utilising the Multimodal Presence Scale and a thematic analysis of open-ended questions, we discovered four mentalities which characterise user experiences: action, game, story and musical. We discuss how these mentalities can mediate presence and immersion, suggesting considerations for how designers can leverage this mapping for similar or related games

Designing to Leverage Presence in VR Rhythm Games

Rhythm games are known for their engaging gameplay and have gained renewed popularity with the adoption of virtual reality (VR) technology. While VR rhythm games have achieved commercial success, there is a lack of research on how and why they are engaging, and the connection between that engagement and immersion or presence. This study aims to understand how the design of two popular VR rhythm games, Beat Saber and Ragnarock, leverages presence to immerse players. Through a mixed-methods approach, utilising the Multimodal Presence Scale and a thematic analysis of open-ended questions, we discovered four mentalities which characterise user experiences: action, game, story and musical. We discuss how these mentalities can mediate presence and immersion, suggesting considerations for how designers can leverage this mapping for similar or related games

Surprise-Triggered Reformulation of Design Goals

This paper presents a cognitive model of goal formulation in designing that is triggered by surprise. Cognitive system approaches to design synthesis focus on generating alternative designs in response to design goals or requirements. Few existing systems provide models for how goals change during designing, a hallmark of creative design in humans. In this paper we present models of surprise and reformulation as metacognitive processes that transform design goals in order to explore surprising regions of a design search space. The model provides a system with specific goals for exploratory behaviour, whereas previous systems have modelled exploration and novelty-seeking abstractly. We use observed designs to construct a probabilistic model that represents expectations about the design domain, and then reason about the unexpectedness of new designs with that model. We implement our model in the domain of culinary creativity, and demonstrate how the cognitive behaviors of surprise and problem reformulation can be incorporated into design reasoning