AI Agents in Gaming: From NPCs to Strategic AI Training Course
AI agents have transformed the gaming industry by enabling intelligent and responsive behaviours, ranging from non-playable characters (NPCs) to advanced strategic decision-making systems. This course explores the development of AI agents in gaming, covering essential topics such as decision trees, pathfinding algorithms, and reinforcement learning techniques.
This instructor-led, live training (available online or on-site) is designed for intermediate-level game developers and AI enthusiasts who aim to effectively integrate AI agents into gaming applications.
By the end of this training, participants will be able to:
- Understand the role of AI agents in modern gaming.
- Develop decision-making systems using decision trees and finite state machines.
- Implement pathfinding algorithms such as A* for in-game navigation.
- Apply reinforcement learning techniques to create adaptive AI behaviours.
- Optimise AI performance for real-time gaming environments.
Course Format
- Interactive lectures and discussions.
- Abundant exercises and practical sessions.
- Hands-on implementation in a live-lab environment.
Course Customisation Options
- To request a customised training session for this course, please contact us to arrange.
Course Outline
Introduction to AI in Gaming
- Overview of AI applications in games
- Types of AI agents: NPCs, strategic AI, and more
- Key concepts in game AI development
Decision-Making Systems
- Implementing decision trees for simple AI logic
- Finite state machines for complex behaviours
- Behaviour trees and modular AI design
Pathfinding and Navigation
- Understanding pathfinding algorithms
- Implementing the A* algorithm for in-game navigation
- Optimising pathfinding for large maps
Reinforcement Learning in Games
- Introduction to reinforcement learning concepts
- Training AI agents using Q-learning and deep Q-networks
- Designing reward structures for adaptive behaviours
Optimising AI Performance
- Techniques for real-time AI performance optimisation
- Managing resources and prioritising AI tasks
- Debugging and troubleshooting AI systems
Advanced AI Techniques
- Procedural content generation with AI
- Simulating player-like behaviours
- Integrating AI with multiplayer gaming
Future Trends in Game AI
- AI and machine learning in next-generation gaming
- Ethical considerations in game AI
- Exploring AI-driven storytelling and narrative design
Summary and Next Steps
Requirements
- Basic understanding of programming concepts
- Familiarity with game development tools or frameworks
- Foundational knowledge of AI principles
Audience
- Game developers
- AI enthusiasts
Open Training Courses require 5+ participants.
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Testimonials (1)
I like how the course is built to the needs of what we are looking to create for work.
Alexius Burris - Weatherford
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