https://www.um.edu.mt/library/oar/handle/123456789/19740 2026-10-17T11:07:30Z 2026-10-17T11:07:30Z https://www.um.edu.mt/library/oar/handle/123456789/150118 2026-10-16T10:15:59Z 2021-01-01T00:00:00Z

Title: Teodicea per universi artificiali : considerazioni morali sulla simulazione digitale Abstract: Come l’ipotesi del cervello in una vasca, le varie versioni dell’ipotesi della simulazione digitale servono a suscitare dubbi riguardo alla genuinità della nostra esperienza. In filosofia sono impiegate per lo più per vagliare la possibilità che quello in cui viviamo sia un universo simulato e per capire se potremmo accorgercene o meno. Quest’uso filosofico però ha da parte altri aspetti e potenziali applicazioni dell’ipotesi della simulazione digitale: ad esempio le riflessioni che riguardano i requisiti tecnologici e computazionali necessari per eseguire la simulazione, i tipi di valori e aspirazioni che possono aver formato e guidato i creatori della simulazione, o le responsabilità etiche che i suoi sviluppatori hanno nei confronti degli esseri artificiali che la abitano.

2021-01-01T00:00:00Z https://www.um.edu.mt/library/oar/handle/123456789/150117 2026-10-16T10:08:58Z 2026-01-01T00:00:00Z

Title: Giocare con l’alterità Abstract: Attraverso tre giochi immaginari del ciclo della Cultura di Iain M. Banks, il saggio indaga come le pratiche ludiche possano condensare ideologie, gerarchie e rapporti con l’alterità. Dal gioco imperiale di Azad alle crudeli competizioni degli Oltraggio, fino agli universi simulati dalle Menti, il gioco diventa uno strumento per pensare forme di vita e intelligenze oltre l’orizzonte umano.

2026-01-01T00:00:00Z https://www.um.edu.mt/library/oar/handle/123456789/147981 2026-07-10T08:31:12Z 2026-01-01T00:00:00Z

Title: A world beyond our understanding : madman’s knowledge Abstract: The item 'Madman’s Knowledge' in FromSoftware's 2016 action-adventure Bloodborne is a game element that contributes to the developer's infamous pursuit of a ludic aesthetics of failure. This particular object leverages two kinds of obfuscation of game-related information: one that places it forever beyond the player’s grasp, and one that can eventually be dispelled through direct experience and recourse to secondary sources. While the latter can be turned into familiar drivel, the former (often referred to as fictional incompleteness) is a mystery that is meant to remain unsolved. In their unique ways, both kinds of informational deficiency evoke fictional worlds whose mystery and greatness extend beyond the limited scope and content of any creative work.

2026-01-01T00:00:00Z Yuan, Yi Zhang, Qingquan Yuan, Bo Barthet, Matthew Khalifa, Ahmed Yannakakis, Georgios N. Chen, Huanhuan Liu, Jialin https://www.um.edu.mt/library/oar/handle/123456789/147356 2026-06-11T12:37:44Z 2026-01-01T00:00:00Z

Title: MOPCGRL : Multi-objective procedural content generation via reinforcement learning Authors: Yuan, Yi; Zhang, Qingquan; Yuan, Bo; Barthet, Matthew; Khalifa, Ahmed; Yannakakis, Georgios N.; Chen, Huanhuan; Liu, Jialin Abstract: Online content generation enables automatic and adaptive creation of diverse and playable game content for maximizing player experience or testing Artificial Intelligence (AI) algorithms. Multiple diversity metrics have been formulated on different content facets in the literature, while some of them conflict with one another. Existing work addresses this multi-dimensional diversity nature by converting those metrics into one term that is further used to direct the training of content generators. However, each generator is trained to meet the preference specified by the weights and fails to fully interpret the relationships among these metrics or provide different trade-offs. This paper proposes a multi-objective procedural content generation via reinforcement learning to train a set of generators that create diverse game content in an online manner while balancing the trade-off between multiple diversity metrics with playability as a constraint. Our framework is compared with state-of-the-art approaches on the commonly used Mario-AI benchmark. Results show that our framework is capable of increasing the diversity of the generator distribution while accelerating the convergence during the early stages of model training. Our approach enables researchers, designers, and practitioners to gain a better understanding of the relationship among conflicting diversity metrics, allowing them to generate content more efficiently and accurately tailored to specific needs.

2026-01-01T00:00:00Z