/library/oar/handle/123456789/19739 2025-12-21T13:09:52Z 2025-12-21T13:09:52Z Sun, Yuqian Gualeni, Stefano /library/oar/handle/123456789/139132 2025-09-22T08:52:57Z 2025-01-01T00:00:00Z

Title: Between puppet and actor : reframing authorship in this age of AI agents Authors: Sun, Yuqian; Gualeni, Stefano Abstract: This chapter examines the conceptual tensions in understanding artificial intelligence (AI) agents’ role in creative processes, particularly focusing on Large Language Models (LLMs). Building upon Schmidt’s 1954 categorization of human-technology relationships and the classical definition of “author,” this chapter proposes to understand AI agency as existing somewhere between that of an inanimate puppet and a performing actor. While AI agents demonstrate a degree of creative autonomy, including the ability to improvise and construct complex narrative content in interactive storytelling, they cannot be considered authors in the classical sense of the term. This chapter thus suggests that AI agents exist in a dynamic state between human-controlled puppets and semi-autonomous actors. This conceptual positioning reflects how AI agents, while they can certainly contribute to creative work, remain bound to human direction. We also argue that existing conceptual frames concerning authorship should evolve and adapt to capture these new relationships.

2025-01-01T00:00:00Z Gualeni, Stefano /library/oar/handle/123456789/139131 2025-09-22T08:34:42Z 2025-01-01T00:00:00Z

Title: What we owe the dead : designing fiction as philosophical output Authors: Gualeni, Stefano Abstract: This article reflects on the cultural and heuristic implications of formulating and communicating philosophical thinking through alternative forms of writing that depart from the academic writing canon. He does so by presenting his two theory-fiction books, The Clouds (Routledge, 2023) and What We Owe the Dead (Set Margins’, 2025), which creatively engage with hybrid forms of textuality. These two experimental pieces play with different forms of philosophical texts, inviting readers to share in and co-participate in the critical speculation designed by the author.

2025-01-01T00:00:00Z Song, Yongduan Wu, Dongrui Coello Coello, Carlos A. Yannakakis, Georgios N. Tang, Huajin Cheung, Yiu-Ming /library/oar/handle/123456789/139109 2025-09-19T07:49:58Z 2024-01-01T00:00:00Z

Title: CIS publication spotlight [publication spotlight] Authors: Song, Yongduan; Wu, Dongrui; Coello Coello, Carlos A.; Yannakakis, Georgios N.; Tang, Huajin; Cheung, Yiu-Ming Abstract: Presents a brief summary of new publications in the area of computational intelligence.

2024-01-01T00:00:00Z Song, Yongduan Wu, Dongrui Coello Coello, Carlos A. Yannakakis, Georgios N. Tang, Huajin Cheung, Yiu-ming /library/oar/handle/123456789/138981 2025-09-12T09:34:48Z 2024-01-01T00:00:00Z

Title: CIS publication spotlight [publication spotlight] Authors: Song, Yongduan; Wu, Dongrui; Coello Coello, Carlos A.; Yannakakis, Georgios N.; Tang, Huajin; Cheung, Yiu-ming Abstract: “Large-scale multiobjective optimization problems (LSMOPs) are characterized as optimization problems involving hundreds or even thousands of decision variables and multiple conflicting objectives. To solve LSMOPs, some algorithms designed a variety of strategies to track Pareto-optimal solutions (POSs) by assuming that the distribution of POSs follows a low-dimensional manifold. However, traditional genetic operators for solving LSMOPs have some deficiencies in dealing with the manifold, which often results in poor diversity, local optima, and inefficient searches. In this work, a generative adversarial network (GAN)-based manifold interpolation framework is proposed to learn the manifold and generate high-quality solutions on the manifold, thereby improving the optimization performance of evolutionary algorithms. We compare the proposed approach with several state-of-the-art algorithms on various large-scale multiobjective benchmark functions. The experimental results demonstrate that significant improvements have been achieved by the proposed framework in solving LSMOPs.”

2024-01-01T00:00:00Z