Player Modeling - IDG5159

Course Description

The primary goal of the course 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 course, 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).

Please, check regularly the course plan for detailed information on lectures, tutorials, and project plan. Note that the course plan is subject to changes.

Course Projects

Propose a research project either on game data mining or affective computing.

In data mining you will have to preprocess raw data, extract and select features for modelling, and apply both an unsupervised and a supervised learning technique to predict an attribute (or set of attributes) of the data.

In affective computing you can either follow the same methodology as above to model an affective outcome of a game OR you can collect your own data using multiple input modalities including physiology.

You are expected to complete one of the following projects:

1. Collect empirical data using your selected system (e.g. a game) and carry out a simple analysis;

2. Build models for the selected psychological state of the users (e.g. the players) that rely on the chosen input modalities, using machine learning.

Your project proposal of max 1 page needs to be submitted to Georgios N. Yannakakis (georgios.yannakakis@um.edu.mt), David Melhart (david.melhart@um.edu.mt) Konstantinos Makantasis (konstantinos.makantasis@um.edu.mt), and Daniele Gravina (daniele.gravina@um.edu.mt).

Students are required to hand in a written report including the motivation of their work, the methodology used and the results of their empirical work, including any supplementary materials (e.g. code and dataset). The written report must follow the given template (Word and LaTeX) and not exceed a maximum of 5 pages. This assignment (hard and soft copy) must be handed in at the Institute of Digital Games.