Adversarial Search

Course

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.

Advanced
1 week
Last Updated March 13, 2024

Skills you'll learn:

Minimax searchState space search

Prerequisites:

Object-oriented PythonConstraint satisfaction problemsLinear algebraSearch algorithmsBasic descriptive statistics

Advanced

1 week

Last Updated March 13, 2024

Skills you'll learn:

Minimax search • State space search • Multi-agent training

Prerequisites:

Object-oriented Python • Constraint satisfaction problems • Linear algebra

Course Lessons

Lesson 1

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.

Lesson 2

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.

Lesson 3

Optimizing Minimax Search

Some of the limitations of minimax search and introduces optimizations & changes that make it practical in more complex domains.

Lesson 4 • Project

Build an Adversarial Game Playing Agent

Build agents that make good decisions without any human intervention—such as the DeepMind AlphaGo agent.

Lesson 5

Extending Minimax Search

Extensions to minimax search to support more than two players and non-deterministic domains.

Lesson 6

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.

Taught By The Best

Thad Starner

Professor of Computer Science, Georgia Tech

Thad Starner is the director of the Contextual Computing Group (CCG) at Georgia Tech and is also the longest-serving Technical Lead/Manager on Google's Glass project.

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