Adversarial Search

Learn how to search in multi-agent environments (including decision making in competitive environments) using the minimax theorem from game theory. Then build an agent that can play games better than any human.

• Introduction to Adversarial Search
• Extend classical search to adversarial domains, to build agents that make good decisions without any human intervention—such as the DeepMind AlphaGo agent.
• Search in Multiagent Domains
• Search in multi-agent domains, using the Minimax theorem to solve adversarial problems and build agents that make better decisions than humans.
• Optimizing Minimax Search
• Some of the limitations of minimax search and introduces optimizations & changes that make it practical in more complex domains.
• Build an Adversarial Game Playing Agent
• Build agents that make good decisions without any human intervention—such as the DeepMind AlphaGo agent.
• Extending Minimax Search
• Extensions to minimax search to support more than two players and non-deterministic domains.
• Additional Adversarial Search Topics
• Introduce Monte Carlo Tree Search, a highly-successful search technique in game domains, along with a reading list for other advanced adversarial search topics.