Browsing by Author "Guckelsberger, Christian"
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- Action Selection in the Creative Systems Framework
A4 Artikkeli konferenssijulkaisussa(2020-09-15) Simo, Linkola; Guckelsberger, Christian; Kantosalo, AnnaThe Creative Systems Framework (CSF) formalises creativity as search through a space of concepts. As a formal account of Margaret Boden’s descriptive hierarchy of creativity, it is at the basis of multiple studies dealing with diverse aspects of Computational Creativity (CC) systems. However, the CSF at present neither formalises action nor action selection during search, limiting its use in analysing creative processes. We extend the CSF by explicitly modelling these missing components in the search space traversal function. We furthermore integrate the distinction between a concept and an artefact, and provide stopping criteria for creative search. Our extension, the Creative Action Selection Framework (CASF), is informed by previous studies in CC and draws on concepts from Markov Decision Processes (MDPs). It allows us to describe a creative system as an agent selecting actions based on the value, validity and novelty of concepts and artefacts. The CASF brings more analytical depth for creative systems that can be modelled as utilising an action selection procedure. - "An Adapt-or-Die Type of Situation”: Perception, Adoption, and Use of Text-To-Image-Generation AI by Game Industry Professionals
A4 Artikkeli konferenssijulkaisussa(2023-10-04) Vimpari, Veera; Kultima, Annakaisa; Hämäläinen, Perttu; Guckelsberger, ChristianText-to-image generation (TTIG) models, a recent addition to creative AI, can generate images based on a text description. These models have begun to rival the work of professional creatives, and sparked discussions on the future of creative work, loss of jobs, and copyright issues, amongst other important implications. To support the sustainable adoption of TTIG, we must provide rich, reliable and transparent insights into how professionals perceive, adopt and use TTIG. Crucially though, the public debate is shallow, narrow and lacking transparency, while academic work has focused on studying the use of TTIG in a general artist population, but not on the perceptions and attitudes of professionals in a specific industry. In this paper, we contribute a qualitative, exploratory interview study on TTIG in the Finnish videogame industry. Through a Template Analysis on semi-structured interviews with 14 game professionals, we reveal 12 overarching themes, structured into 39 sub-themes on professionals' perception, adoption and use of TTIG in games industry practice. Experiencing (yet another) change of roles and creative processes, our participants' reflections can inform discussions within the industry, be used by policymakers to inform urgently needed legislation, and support researchers in games, HCI and AI to support the sustainable, professional use of TTIG, and foster games as cultural artefacts. - Automating Generative Deep Learning for Artistic Purposes: Challenges and Opportunities
A4 Artikkeli konferenssijulkaisussa(2021-09-01) Berns, Sebastian; Broad, Terence; Guckelsberger, Christian; Colton, SimonWe present a framework for automating generative deep learning with a specific focus on artistic applications. The framework provides opportunities to hand over creative responsibilities to a generative system as targets for automation. For the definition of targets, we adopt core concepts from automated machine learning and an analysis of generative deep learning pipelines, both in standard and artistic settings. To motivate the framework, we argue that automation aligns well with the goal of increasing the creative responsibility of a generative system, a central theme in computational creativity research. We understand automation as the challenge of granting a generative system more creative autonomy, by framing the interaction between the user and the system as a co-creative process. The development of the framework is informed by our analysis of the relationship between automation and creative autonomy. An illustrative example shows how the framework can give inspiration and guidance in the process of handing over creative responsibility. - Cine-AI: Generating Video Game Cutscenes in the Style of Human Directors
A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä(2022-10-29) Evin, Inan; Hämäläinen, Perttu; Guckelsberger, ChristianCutscenes form an integral part of many video games, but their creation is costly, time-consuming, and requires skills that many game developers lack. While AI has been leveraged to semi-automate cutscene production, the results typically lack the internal consistency and uniformity in style that is characteristic of professional human directors. We overcome this shortcoming with Cine-AI, an open-source procedural cinematography toolset capable of generating in-game cutscenes in the style of eminent human directors. Implemented in the popular game engine Unity, Cine-AI features a novel timeline and storyboard interface for design-time manipulation, combined with runtime cinematography automation. Via two user studies, each employing quantitative and qualitative measures, we demonstrate that Cine-AI generates cutscenes that people correctly associate with a target director, while providing above-average usability. Our director imitation dataset is publicly available, and can be extended by users and film enthusiasts. - Complexity Science in Game Design
Perustieteiden korkeakoulu | Bachelor's thesis(2020-12-04) Salo, Niilo - Creative collaboration with interactive evolutionary algorithms: a reflective exploratory design study
A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä(2024-06) Uusitalo, Severi; Kantosalo, Anna; Salovaara, Antti; Takala, Tapio; Guckelsberger, ChristianProgress in AI has brought new approaches for designing products via co-creative human–computer interaction. In architecture, interior design, and industrial design, computational methods such as evolutionary algorithms support the designer’s creative process by revealing populations of computer-generated design solutions in a parametric design space. Because the benefits and shortcomings of such algorithms’ use in design processes are not yet fully understood, the authors studied the intricate interactions of an industrial designer employing an interactive evolutionary algorithm for a non-trivial creative product design task. In an in-depth report on the in-situ longitudinal experiences arising between the algorithm, human designer, and environment, from ideation to fabrication, they reflect on the algorithm’s role in inspiring design, its relationship to fixation, and the stages of the creative process in which it yielded perceived value. The paper concludes with proposals for future research into co-creative AI in design exploration and creative practice. - Creativity and Markov Decision Processes
A4 Artikkeli konferenssijulkaisussa(2024-11) Lahikainen, Joonas; Ady, Nadia M.; Guckelsberger, ChristianCreativity is already regularly attributed to AI systems outside specialised computational creativity (CC) communities. However, the evaluation of creativity in AI at large typically lacks grounding in creativity theory, which can promote inappropriate attributions and limit the analysis of creative behaviour. While CC researchers have translated psychological theory into formal models, the value of these models is limited by a gap to common AI frameworks. To mitigate this limitation, we identify formal mappings between Boden's process theory of creativity and Markov Decision Processes (MDPs), using the Creative Systems Framework as a stepping stone. We study three out of eleven mappings in detail to understand which types of creative processes, opportunities for (aberrations), and threats to creativity (uninspiration) could be observed in an MDP. We conclude by discussing quality criteria for the selection of such mappings for future work and applications. - Designing and Evaluating AI Personas for Customer and User Understanding - Exploring Use Cases and Attitudes in an Industrial, B2B Context
School of Science | Master's thesis(2024-12-31) Müller, PhilipRecent advancements in artificial intelligence, particularly the development of large language models, have created new opportunities for customer and user understanding. One notable area of exploration is the use of AI for creating and augmenting personas. In particular, LLMs enable the transformation of personas from static constructs into dynamic, interactive role-playing entities. While research has begun to investigate these capabilities, many aspects remain underexplored. Some researchers have investigated aspects of persona role-playing in the context of other objectives, but no dedicated study has yet been undertaken. Moreover, little is known about how AI personas might function in real work environments, what challenges they might face, and how designers perceive them. To address some of these gaps, this thesis offers the following contributions: (i) a literature review of current AI technologies for customer and user understanding, (ii) an in-depth analysis of workflows and challenges related to customer and user understanding in an industrial B2B environment, (iii) an exploration of AI personas as a potential solution to these challenges through several prototypes, and (iv) an evaluation of AI personas through three studies, exploring how designers perceive them, as well as their implications for workflows and use cases over an extended period within an industrial B2B context. This research finds that designers are generally positive about the potential of AI personas to augment research practices, particularly for initial domain knowledge acquisition and context-aware ideation. However, challenges such as trust and explainability still represent an obstacle to daily adoption. Additionally, while designers are found to naturally oppose the idea of substituting real research participants with AI personas, the convenience of AI personas poses a risk of obstructing real user research advocacy in organizational contexts. - Designing for Playfulness in Human-AI Authoring Tools
A4 Artikkeli konferenssijulkaisussa(2023-04-12) Liapis, Antonios; Guckelsberger, Christian; Zhu, Jichen; Harteveld, Casper; Kriglstein, Simone; Denisova, Alena; Gow, Jeremy; Preuss, MikeMany human-AI authoring tools are used in a playful way, while being primarily designed for task-achievement - not playfulness. We argue that playfulness is an important yet overlooked factor of user behaviour and experience when interacting with such tools. Motivating and rewarding playfulness as an exploratory, task-agnostic, open, and subversive attitude can support the satisfaction of more diverse user goals, and have a strong, positive effect on the user experience, the emerging human-AI interaction, and the resulting artefact. In this paper, we motivate the importance of playfulness as user experience in human-AI authoring tools, and propose concrete strategies to design for playfulness in the human user through UI design, in the AI through algorithms, or through interventions to their dialog. We conclude with an outlook of the research agenda. - Embodiment and Computational Creativity
A4 Artikkeli konferenssijulkaisussa(2021-09-01) Guckelsberger, Christian; Kantosalo, Anna; Negrete-Yankelevich, Santiago; Takala, TapioWe 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. - Game Industry Creative Practitioner Perception, Adoption, and Use of Text-to-Image Generation Systems
Perustieteiden korkeakoulu | Master's thesis(2023-05-16) Vimpari, VeeraAI based text-to-image generation (TTIG) systems, such as DALL-E 2 and Midjourney, produce high-quality images based on text inputs. While these systems have been predicted to revolutionise creative industries, many users and creative professionals are concerned over copyright issues and the impact on artists jobs. So far, existing research only covers the use of TTIG systems across creative domains, but there is no work on the perception, attitudes, and adoption of TTIG systems by creative professionals in a specific industry. To address this research gap, we conducted an interview study with 14 professional creatives in the Finnish game industry with the aim to gain valuable insights on how creative practitioners perceive, adopt, use, and predict the future use and development of TTIG systems. We performed a template analysis on our interview data and discovered that while game industry creatives are ready to adapt to change, they expect the system developers to improve certain system shortcomings, as well as companies and policymakers to address ethical considerations, before fully accepting the systems into their work. While we contribute extensively to a topic that has not been sufficiently addressed through exploratory, and at the same time, deep user studies, we also advocate further research to be conducted on a lengthier timeframe and with more diverse experience levels and demographies. - Generating Role-Playing Game Quest Descriptions With the GPT-2 Language Model
Perustieteiden korkeakoulu | Master's thesis(2022-01-24) Värtinen, SusannaRecent advances in artificial intelligence research have brought forth text-generating language models with promising computational storytelling capabilities. This thesis leveraged one of the most successful Transformer models, GPT-2, to automatically generate video game quest descriptions. We aimed to replace traditional procedural content generation methods for quests in games, as these often produce uninteresting, mechanical descriptions. We have gathered and processed a novel quest data set from a selection of popular 3D role-playing games. We have fine-tuned GPT-2 on the data set using various learning optimizations. Most notably, we replaced proper nouns within raw quest data with generic placeholder tokens to reduce unnecessary variance. We validated the resulting \questgpt{} model via an online user study performed by role-playing game players. Our results indicate that one in five quest descriptions would be deemed acceptable by a human critic, yet the variation in quality across individual quests is large. Further work utilizing next-generation language models and our quest data set is expected to result in improved quest description quality. - Generating Role-Playing Game Quests With GPT Language Models
A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä(2024-03) Värtinen, Susanna; Hämäläinen, Perttu; Guckelsberger, ChristianQuests represent an integral part of role-playing games (RPGs). While evocative, narrative-rich quests are still mostly hand-authored, player demands toward more and richer game content, as well as business requirements for continuous player engagement necessitate alternative, procedural quest generation methods. While existing methods produce mostly uninteresting, mechanical quest descriptions, recent advances in AI have brought forth generative language models with promising computational storytelling capabilities. We leverage two of the most successful transformer models, 1) GPT-2 and 2) GPT-3, to procedurally generate RPG video game quest descriptions. We gathered, processed, and openly published a dataset of 978 quests and their descriptions from six RPGs. We fine-tuned GPT-2 on this dataset with a range of optimizations informed by several ministudies. We validated the resulting Quest-GPT-2 model via an online user study involving 349 RPG players. Our results indicate that one in five quest descriptions would be deemed acceptable by a human critic, yet the variation in quality across individual quests is large. We provide recommendations on current applications of Quest-GPT-2. This is complemented by case-studies on GPT-3 to highlight the future potential of state-of-the-art natural language models for quest generation. - How Does Embodiment Affect the Human Perception of Computational Creativity? An Experimental Study Framework
A4 Artikkeli konferenssijulkaisussa(2022-10-30) Linkola, Simo; Guckelsberger, Christian; Männistö, Tomi; Kantosalo, AnnaWhich factors influence the human assessment of creativity exhibited by a computational system is a core question of computational creativity (CC) research. Recently, the system’s embodiment has been put forward as such a factor, but empirical studies of its effect are lacking. To this end, we propose an experimental framework which isolates the effect of embodiment on the perception of creativity from its effect on creativity per se. We manipulate not only the system’s embodiment but also the human perception of creativity, which we factorise into the assessment of creativity, and the perceptual evidence that feeds into that assessment. We motivate the core framework with embodiment and perceptual evidence as independent and the creative process as a controlled variable, and we provide recommendations on measuring the assessment of creativity as a dependent variable. We propose three types of perceptual evidence with respect to the creative system, the creative process and the creative artefact, borrowing from the popular four perspectives on creativity. We hope the framework will inspire and guide others to study the human perception of embodied CC in a principled manner. - Human-Computer co-creation in procedural level design
Perustieteiden korkeakoulu | Bachelor's thesis(2021-04-25) Lehtinen, Joose - Image-based and machine learning-guided multiplexed serology test for SARS-CoV-2
A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä(2023-08-28) Pietiäinen, Vilja; Polso, Minttu; Migh, Ede; Guckelsberger, Christian; Harmati, Maria; Diosdi, Akos; Turunen, Laura; Hassinen, Antti; Potdar, Swapnil; Koponen, Annika; Sebestyen, Edina Gyukity; Kovacs, Ferenc; Kriston, Andras; Hollandi, Reka; Burian, Katalin; Terhes, Gabriella; Visnyovszki, Adam; Fodor, Eszter; Lacza, Zsombor; Kantele, Anu; Kolehmainen, Pekka; Kakkola, Laura; Strandin, Tomas; Levanov, Lev; Kallioniemi, Olli; Kemeny, Lajos; Julkunen, Ilkka; Vapalahti, Olli; Buzas, Krisztina; Paavolainen, Lassi; Horvath, Peter; Hepojoki, JussiWe present a miniaturized immunofluorescence assay (mini-IFA) for measuring antibody response in patient blood samples. The method utilizes machine learning-guided image analysis and enables simultaneous measurement of immunoglobulin M (IgM), IgA, and IgG responses against different viral antigens in an automated and high-throughput manner. The assay relies on antigens expressed through transfection, enabling use at a low biosafety level and fast adaptation to emerging pathogens. Using severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) as the model pathogen, we demonstrate that this method allows differentiation between vaccine-induced and infection-induced antibody responses. Additionally, we established a dedicated web page for quantitative visualization of sample-specific results and their distribution, comparing them with controls and other samples. Our results provide a proof of concept for the approach, demonstrating fast and accurate measurement of antibody responses in a research setup with prospects for clinical diagnostics. - Impressions of the GDMC AI Settlement Generation Challenge in Minecraft
A4 Artikkeli konferenssijulkaisussa(2022-09-05) Salge, Christoph; Aranha, Claus; Brightmoore, Adrian; Butler, Sean; De Moura Canaan, Rodrigo; Cook, Michael; Green, Michael; Fischer, Hagen; Guckelsberger, Christian; Hadley, Jupiter; Herve, Jean Baptiste; Johnson, Mark; Kybartas, Quinn; Mason, David; Preuss, Mike; Smith, Tristan; Thawonmas, Ruck; Togelius, JulianThe GDMC AI settlement generation challenge is a procedural content generation (PCG) competition about producing an algorithm that can create a settlement in the game Minecraft. In contrast to the majority of AI competitions, the GDMC entries are evaluated by human experts on several criteria such as adaptability, functionality, evocative narrative, and visual aesthetics - all of which represent challenges to state-of-the-art PCG systems. This paper contains a collection of written experiences with this competition, by participants, judges, organizers and advisors. We asked people to reflect both on the artifacts themselves, and on the competition in general. The aim of this paper is to offer a shareable and edited collection of experiences and qualitative feedback which have the potential to push forward PCG and computational creativity, but would be lost once the individual assessments are compressed to scalar ratings. We reflect upon organizational issues for AI competitions, and discuss the future of the GDMC competition. - Level QA testing with curiosity-driven AI agents
School of Arts, Design and Architecture | Master's thesis(2023) Oshiro, KenAdvancements in reinforcement learning have allowed agents to play games of increased complexity. One potential use-case of this technology for the game industry would be for QA testing. This thesis investigates the possibility of training an autonomous agent inside a prevalent game engine, Unreal Engine 5.0, to explore a level and detect missing colliders. The focus of this thesis is the study of the performance of a curiosity-driven AI agent. Curiosity allows agents to discover novel game states through intrinsic motivation, without explicitly defined goals. Coupled with a count-based exploration of the game’s level and a visualization of the agent’s path, the solution’s coverage and speed is evaluated. It is compared to a random policy and a human tester. Results indicate that the agent’s coverage is sufficient to explore the whole level. Its performance in finding collision bugs is higher than both a random policy and a human tester. - Neurodiverse Rhetoric with Conversational AI
School of Arts, Design and Architecture | Master's thesis(2024-12-05) Luostarinen, VerttiThis thesis studies whether neurodivergent rhetorical traits can be repre-sented with a conversational AI system. As the data demands of fine-tuning custom large language model (LLM) solutions have lowered, it is feasible to cater to smaller demographics. In the Tuumailubotti (Pondering bot) project, the FinGPT-3 LLM was trained with a dataset authored by an anonymous group consisting mainly of members of neurominorities. The model was then used to create a demo for a chatbot for discussing performance review-adjacent topics. By applying neuroqueer theory into the design process of a chatbot, the project critiques the neuronormative assumptions that underlie the design of mainstream generative AI assistants and tries to imagine an alternative space for private reflection for neurominority employees. To assess whether the rhetoric of the LLM was neuroqueered via fine-tuning, the thesis investigates which rhetorical traits were present in the text outputs of the chatbot, and how they affected the preferences of users. In a user study, 31 participants had an hour to interact with the chatbot and then fill in a survey. The participants classified quotes from their conversations into five categories: good, bad, weird, toxic and neurodiverse. These quotations were then coded by the thesis author using a list of common neurodivergent rhetorical traits, such as fast switching of topics, repetition and exactness. The results show that many of the coded traits were prevalent in the quotes. However, without a direct point of comparison, such as a chatbot trained with data authored by neurotypicals, it is unclear to what extent rheto-ric factored in the preferences of the testers. From a design perspective, better results might have been achieved with a more participatory design process. While overall impressions of the chatbot were not negative, the coded traits were mostly perceived negatively by the participants. The participants did not identify the traits as neurodivergent, and mostly viewed them as bad design decisions. The responses indicate that the project succeeded in its goal of challenging implicit assumptions the participants had about the rhetoric of chatbots, therefore neuroqueering rhetoric. - On the Machine Condition and its Creative Expression
A4 Artikkeli konferenssijulkaisussa(2020-09-15) Colton, Simon; Pease, Alison; Guckelsberger, Christian; McCormack, Jon; Llano, Maria TeresaThe human condition can be characterised as the most essential characteristics, events and situations which describe human existence. We propose that a parallel discussion of the machine condition could improve public understanding of computational systems in general, and advance perception of creativity in computational creativity systems in particular. We present a framework for machines to creatively express their existence, sketch some aspects of the machine condition, and describe potential benefits of this approach.