Class Autonomous Game Adaptation

Modern game development tools allow artists to "mold" and construct a wide variety of virtual artifacts, from worlds, buildings and characters.  One of the advantages of virtual content is that it is highly modular and adaptable, meaning that developers can construct processes that allow this content to be customized, such as dynamic environments (day to night effects, or sun to rain), adding different lighting and material conditions through the use of shaders, or altering the audio to follow the on-screen action. Considering the popularization of Procedural Content Generation (PCG), much of this content is built through the use of algorithmic processes, which can be further customized during play. Thus, it is unsurprising that more and more solutions have investigated the use of combining player modeling with PCG systems, given that such solutions open the possibility of customizing virtual content towards the needs of each individual or group of players.  

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CP1. Procedural generation of content guided by player experience: an overview CP2. Dynamic experience managers 2.1. Dynamic audio 2.2. Visual and 3D content orchestration 2.3. Dynamic Difficulty Adjustment CP3. Dynamic Content Adaptation (DCA): The State of the Art: 3.1. Applied in the video game industry 3.2. Applied in academia and research in the field of video games CP4. Personalization of Dynamic Content. 4.1. The Affective Loop 4.2 Player Modeling and Procedural Content Generation  4.3.Player Modeling and Content Orchestration  4.4. Player Modeling and Dynamic Systems (Audio, Visuals and Level Design) 4.5. Mixed-Initiative Content Creation  CP5. Ethical Implications and Data Privacy 5.1. Ethical Data Collection 5.2 Informed Consent and Consent Forms 5.3.The Balance between addiction and entertainment CP6. Applications of DCA beyond "just" entertainment 6.1. Neuroscience 6.2  Psychology 6.3. Rehabilitation 6.4. Medicine  

Classes will be given within a theoretical and practical framework, and will consist of lectures (TM1), practical project activities (TM2) and feedback sessions by the teacher (TM3). The assessment component will consist of developing a dynamic adaptation system based on common themes within the literature and the video-game industry, which concludes with a playable artifact and a detailed report of the methodological process.  Continuous assessment is conducted as follows: Exploratory project throughout the semester covering the entire program. Assessment test. Final grade = 80% exploratory project + 20% test. Exam assessment: Theoretical test on the contents taught during the semester (20%) + practical exploratory project equivalent to the exploratory project carried out in continuous assessment (80%). In both assessment components, theoretical and practical, a minimum mark may be set.

Togelius, J. (2019). Playing smart: On games, intelligence, and artificial intelligence. MIT Press. Yannakakis, G. N., & Togelius, J. (2018). Artificial Intelligence and Games. Springer. Karpouzis, K., & Yannakakis, G. N. (2016). Emotion in Games. Cham: Springer.  Yannakakis, G. N., & Togelius, J. (2011). Experience-driven procedural content generation. IEEE Transactions on Affective Computing, 2(3), 147-161.