| CODE | IDG5159 | ||||||||||||
| TITLE | Player Modeling: From Game Analytics to Affective Computing | ||||||||||||
| UM LEVEL | 05 - Postgraduate Modular Diploma or Degree Course | ||||||||||||
| MQF LEVEL | 7 | ||||||||||||
| ECTS CREDITS | 10 | ||||||||||||
| DEPARTMENT | Institute of Digital Games | ||||||||||||
| DESCRIPTION | The primary goal of the unit is to revisit the field of game artificial intelligence (AI) and introduce non-traditional uses of AI in games. A short introduction will be given on AI areas that are currently reshaping the game AI research and development roadmap including procedural content generation, player experience modeling, and AI-based game design. The primary focus of the unit, however, will be on player modeling (spanning from game analytics and game data mining to affective computing methods). Within game data mining, emphasis will be given on state-of-the-art data analytics/mining algorithms and methods for improving the gameplay experience and game development procedures. Within affective computing, emphasis will be given in the phases of emotion elicitation, emotion recognition (feature extraction, feature selection, annotation, classification, regression, preference learning), emotion expression (e.g., facial expression, agent behavioural responses, etc.) and affect-driven adaptation (interaction elements adapt to the user needs/affect). The unit will cover the following topics: - Revisiting game artificial intelligence. The role of Player Modeling. Part I: Game Analytics and Game Data Mining - Basic data analysis, data preprocessing and descriptive statistics - Classification and prediction - Clustering - Sequence Mining - Advanced Data Mining Techniques - Data Visualization - Industrial game analytics - problems and needs Part II: Affective Computing - Theories of emotion (affect and cognition) - The Affective Loop: key components - Eliciting Emotion (protocols and approaches) - Recognizing and Modelling Emotion - The model's input - Speech, eye gaze, physiology, images, movement/posture - Feature Extraction / Selection - The model's output (affect annotation / ranks, ratings, ground truth) - A taxonomy of modelling approaches - Model-based (introduction to popular models of emotion and behaviour) - Mode-free - A panorama of data-driven approaches to affective modelling - Pattern recognition, Classification, Regression, Preference Learning - Expressing Emotion (via agents and virtual environments) - Closing the affective loop: Adaptation via agents and virtual environments - Player Experience Modeling - Popular application domains: computer games, HCI, health etc. Study-unit Aims: The aims of the unit are as follows: - Introduction to the theory and implementation of game analytics and game data mining algorithms; - Uses of artificial and computational intelligence for mining data in games; - Introduction to theories of emotion; - Introduction to methods, algorithms and tools for affect annotation, affect recognition and affect-based adaptation; - Introduction to Player Experience Modeling. Learning Outcomes: 1. Knowledge & Understanding By the end of the study-unit the student will be able to: - describe and theorize on the algorithms and domains covered in class; - identify tasks that can be tackled through game data mining and affective computing methods and select the appropriate method for the problem under investigation; - compare the performance of different methods and reflect on their suitability for a domain. 2. Skills By the end of the study-unit the student will be able to: - design and implement efficient data mining, affect recognition, modeling and adaptation algorithms; - evaluate the algorithms in a commercial-standard application (e.g. game production); - work efficiently in groups and evaluate the algorithms in data derived from commercial-standard game productions. Main Text/s and any supplementary readings: - Yannakakis and Togelius, "Artificial Intelligence and Games", Springer, 2017. - Seif El-Nasr et al. (Eds.) "Game Analytics — Maximizing the Value of Player Data", Springer London, 2013. - Picard, Rosalind. Affective Computing. MIT Press, 1997. Various online articles and textbook chapters. |
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| ADDITIONAL NOTES | Pre-Requisite qualifications: Bachelor's in engineering/CS or related fields; object-oriented programming | ||||||||||||
| STUDY-UNIT TYPE | Lecture, Tutorial and Project | ||||||||||||
| METHOD OF ASSESSMENT |
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| LECTURER/S | Daniele Gravina Konstantinos Makantasis David Melhart Georgios N. Yannakakis |
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The University makes every effort to ensure that the published Courses Plans, Programmes of Study and Study-Unit information are complete and up-to-date at the time of publication. The University reserves the right to make changes in case errors are detected after publication.
The availability of optional units may be subject to timetabling constraints. Units not attracting a sufficient number of registrations may be withdrawn without notice. It should be noted that all the information in the description above applies to study-units available during the academic year 2023/4. It may be subject to change in subsequent years. |
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