Zobrazeno 1 - 10
of 51
pro vyhledávání: '"Guckelsberger, Christian"'
Artificial Life (ALife) as an interdisciplinary field draws inspiration and influence from a variety of perspectives. Scientific progress crucially depends, then, on concerted efforts to invite cross-disciplinary dialogue. The goal of this paper is t
Externí odkaz:
http://arxiv.org/abs/2405.18016
Creativity 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 inappropria
Externí odkaz:
http://arxiv.org/abs/2405.14966
Autor:
Berns, Sebastian, Volz, Vanessa, Tokarchuk, Laurissa, Snodgrass, Sam, Guckelsberger, Christian
Similarity estimation is essential for many game AI applications, from the procedural generation of distinct assets to automated exploration with game-playing agents. While similarity metrics often substitute human evaluation, their alignment with ou
Externí odkaz:
http://arxiv.org/abs/2402.18728
Large data-driven image models are extensively used to support creative and artistic work. Under the currently predominant distribution-fitting paradigm, a dataset is treated as ground truth to be approximated as closely as possible. Yet, many creati
Externí odkaz:
http://arxiv.org/abs/2304.11961
Publikováno v:
Proc. ACM Hum.-Comput. Interact., Vol. 7, No. CHI PLAY, 2023, Article 379
Text-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, lo
Externí odkaz:
http://arxiv.org/abs/2302.12601
Publikováno v:
The 35th Annual ACM Symposium on User Interface Software and Technology (UIST '22), October 29-November 2, 2022, Bend, OR, USA. ACM, New York, NY, USA, 13 pages
The accurate and personalized estimation of task difficulty provides many opportunities for optimizing user experience. However, user diversity makes such difficulty estimation hard, in that empirical measurements from some user sample do not necessa
Externí odkaz:
http://arxiv.org/abs/2209.13495
Cutscenes 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 in
Externí odkaz:
http://arxiv.org/abs/2208.05701
Which 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 studie
Externí odkaz:
http://arxiv.org/abs/2205.01418
Autor:
Salge, Christoph, Aranha, Claus, Brightmoore, Adrian, Butler, Sean, Canaan, Rodrigo, Cook, Michael, Green, Michael Cerny, Fischer, Hagen, Guckelsberger, Christian, Hadley, Jupiter, Hervé, Jean-Baptiste, Johnson, Mark R, Kybartas, Quinn, Mason, David, Preuss, Mike, Smith, Tristan, Thawonmas, Ruck, Togelius, Julian
The GDMC AI settlement generation challenge is a PCG competition about producing an algorithm that can create an "interesting" Minecraft settlement for a given map. This paper contains a collection of written experiences with this competition, by par
Externí odkaz:
http://arxiv.org/abs/2108.02955
Autor:
Roohi, Shaghayegh, Guckelsberger, Christian, Relas, Asko, Heiskanen, Henri, Takatalo, Jari, Hämäläinen, Perttu
This paper presents a novel approach to automated playtesting for the prediction of human player behavior and experience. It has previously been demonstrated that Deep Reinforcement Learning (DRL) game-playing agents can predict both game difficulty
Externí odkaz:
http://arxiv.org/abs/2107.12061