AI for Games
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Overview
This project is a collection of classic AI for Games algorithms, implemented in C++ and visualized with Qt. It was developed during the AI for Games 2 lecture and demonstrates several techniques used in navigation, decision-making and learning for AI game agents.
Features:
- Flowfield Pathfinding: Efficient movement of large groups of agents
- Influence Maps: Spatial reasoning for tactical decision making
- Hillclimbing: Estimating local maxima in search problems
- R-Q Learning: Reinforcement learning for action optimization
- Ballistic Prediction: Analysis of projectile trajectories