Artificial Intelligence for Entertainment

Recommended Prerequisites

Artificial Intelligence fundamentals; Python programming; Good English reading, writing and speaking skills.

Teaching Methods

During the lectures (T) the concepts, the theories, the algorithms will be presented and discussed. In the lab classes (PL) students will consolidate what was learned in T. The practical assignments will be done under the supervision of the teacher. Grading will be based on two components: (1) projects involving the techniques; (2) a research work.

Learning Outcomes

The UC aims to study, develop, and apply Artificial Intelligence techniques and tools in the context of product development for entertainment purposes. Particular emphasis will be placed on co-creation scenarios without neglecting procedural content generation, narrative, game mechanics, and social simulation. The main challenges and opportunities in this area will be analyzed. Emphasis will be placed on learning through practice, following a Project Based Learning approach.

At the end of the unit, the student will have a comprehensive understanding of the AI for entertainment field and will also be capable of developing and/or adapting AI systems to meet real needs, applying these approaches to various entertainment domains.

The main competencies to be developed are:

Instrumental – analysis and synthesis, problem-solving

Personal – critical thinking

Systemic - practical application of the theoretical knowledge; research.

Work Placement(s)

No

Syllabus

1. AI in Entertainment

2. Human-Computer Co-Creativity

2.1 Key concepts and categorisations

2.2 Examples of co-creativity in entertainment

2.3 Evaluation

2.4 Theoretical frameworks

3. Intelligent Game Mechanics

3.1 AI-Driven Difficulty Adjustment

3.2 Dynamic Event Systems

3.3 AI in Game Rule Systems

4. Procedural Generation

4.1 Asset Generation

4.2 Terrain and Environment Creation

4.4 Scenario Generation

4.5 Adaptive Level Design

5. Character and Narrative AI

5.1 Dynamic Storytelling

5.2 Smart NPCs (Non-Player Characters)

5.3 Emotionally Responsive Characters

5.4 Text Interaction and AI Dialogue Systems

5.5 Behavioral AI for Character Development

6. AI and Social Simulation

6.1 Social Interaction Models

6.2 Crowd Simulation

6.3 Emotional and Psychological Modeling

6.4 Ecosystem and Economic Simulations.

Head Lecturer(s)

Eliezer de Souza da Silva

Assessment Methods

Assessment
Research work: 40.0%
Project: 60.0%

Bibliography

G. N. Yannakakis and J. Togelius, Artificial Intelligence and Games. Cham, Switzerland: Springer, 2018.

T. Veale and R. Pérez y Pérez, Eds., Computational Creativity: The Philosophy and Engineering of Autonomously Creative Systems. Springer, 2019.

N. Shaker, J. Togelius, and M. J. Nelson, Procedural Content Generation in Games: A Textbook and an Overview of Current Research. Cham, Switzerland: Springer, 2016.

S. Rabin, Ed., Game AI Pro: Collected Wisdom of Game AI Professionals. Boca Raton, FL: CRC Press, 2013.

P. Machado and J. Romero, Eds., The Art of Artificial Intelligence: Themes, Case Studies and Connections. Cham, Switzerland: Springer, 2020.

A. Cardoso, Ed., Computational Creativity: The Philosophy and Future of AI. Cham, Switzerland: Springer, 2021.