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Computer Science
Game Development
Artificial Intelligence for Games
1. Introduction to Artificial Intelligence in Games
2. Core Concepts and Movement
3. Decision-Making Architectures
4. Sensing and Knowledge Representation
5. Tactical and Strategic AI
6. Learning and Adaptation in Games
7. Advanced and Specialized Topics
8. Implementation and Production
Learning and Adaptation in Games
Overview of Machine Learning for Games
Supervised Learning
Classification Tasks
Regression Tasks
Training Data Requirements
Feature Engineering
Unsupervised Learning
Clustering Algorithms
Pattern Discovery
Dimensionality Reduction
Anomaly Detection
Reinforcement Learning
Trial-and-Error Learning
Reward Structures
Policy Learning
Value Functions
Semi-Supervised Learning
Limited Labeled Data
Active Learning
Offline vs. Online Learning
Batch Training
Dataset Preparation
Training Pipelines
Model Validation
Real-Time Adaptation
Incremental Learning
Streaming Data
Concept Drift
Safety and Stability Concerns
Catastrophic Forgetting
Performance Guarantees
Fallback Mechanisms
Reinforcement Learning in Games
Core Concepts
Agents and Environments
Actions and States
Rewards and Penalties
Markov Decision Processes
Q-Learning
Q-Table Representation
Bellman Equation
Exploration vs. Exploitation
Epsilon-Greedy Strategies
Deep Reinforcement Learning
Neural Network Function Approximation
Deep Q-Networks (DQN)
Policy Gradients
Actor-Critic Methods
Multi-Agent Reinforcement Learning
Competitive Learning
Cooperative Learning
Mixed Environments
Imitation Learning
Learning from Demonstration
Data Collection Methods
Expert Demonstrations
Feature Extraction
Trajectory Learning
Behavioral Cloning
Supervised Policy Learning
Dataset Aggregation
Limitations and Challenges
Distribution Mismatch
Inverse Reinforcement Learning
Reward Function Learning
Maximum Entropy Methods
Player Modeling
Player Profiling
Behavioral Patterns
Skill Assessment
Preference Learning
Predicting Player Actions
Action Prediction Models
Sequence Modeling
Anticipating Player Strategies
Dynamic Difficulty Adjustment
Real-Time Difficulty Scaling
Performance Metrics
Player Retention Strategies
Adaptive Content
Personalization Systems
Content Recommendation
Adaptive Interfaces
Customized Experiences
Procedural Content Generation with AI
Level and Map Generation
Layout Generation Algorithms
Constraint-Based Generation
Balancing and Playability
Aesthetic Considerations
Weapon and Item Generation
Attribute Generation
Rarity and Balance Systems
Procedural Stats
Quest and Narrative Generation
Story Structure Generation
Branching Narratives
Character Generation
Dialogue Systems
Texture and Art Generation
Neural Style Transfer
Generative Adversarial Networks
Procedural Textures
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5. Tactical and Strategic AI
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7. Advanced and Specialized Topics