AI for Real-Time Strategy (RTS) Games

Build challenging, fair RTS opponents with AI that handles macro, micro...

RTS AI spans strategic planning, economy management, unit production, micro-control, pathfinding, and fog-of-war reasoning. Systems must avoid unfair information, respect difficulty settings, and run within tight CPU budgets.

Create believable, varied opponents that challenge players without cheating, with tunable difficulty and strong performance.

Maps, nav meshes, build orders, unit stats, combat logs, replays, play...

Behavior trees/GOAP, planners, utility systems, influence maps, pathfinding (A*/JPS), micro-control policies, build-order search.

AI controllers, debug visualizers, scriptable behaviors, difficulty tuning tools.

live demo · related simulation● LIVE

Worker allocation, expansions, and resource timing.

Fair handicaps/bonuses, behavior variation, and pacing.

Implementation Blueprint

Frequently asked questions

What is the role of governance, safety, and explainability in RTS AI?

Governance, Safety, and Explainability are crucial considerations when developing RTS AI, ensuring fairness, preventing exploits, and allowing for human oversight.

How can we prevent an AI from gaining an unfair advantage through information?

To limit information advantages, AI controllers should declare handicaps or restrictions, respecting the difficulty settings and avoiding exploiting player strategies.

What tools are useful for debugging and understanding the AI's decision-making process?

Debug overlays, logs, and decision traces provide valuable insights into the AI’s actions, allowing developers to identify issues and refine its behavior.

How do we ensure the AI is robust and doesn’t introduce new exploits or bugs during gameplay?

Exploit checks, pathing QA, and regression suites are essential for testing the AI's performance and identifying potential vulnerabilities before they impact players.

▶ Try it live

Everything above runs in your browser — open Hash Function Avalanche Visualizer and change the parameters while it is running. Nothing is installed, nothing is uploaded, the whole model lives in one tab.

▶ Open Hash Function Avalanche Visualizer simulation