AIND Term 1: Week 3

Classroom

• Watch the rest of Introduction to Game Playing, stopping partway through Advanced Game Playing (stop at the "Searching Complex Games" lesson)
• Start Project 2 (This one is significantly harder than Project 1 -- get started early!)
  • Refer to the Project Setup Walkthrough Video
  • Clone the project repository using Git -- refer back to the References & Resources lesson on Git (from week 1) as needed
  • Follow the instructions in the README to run the example in sample_players.py
  • Port your Minimax code from the classroom project (week 2) into the MinimaxAgent class (you'll need to modify the code to apply depth-limited search)

Reading

• Read AIMA Chapter 5.3; skim AIMA 5.4-5.8; select ONE (1) research paper for Project 2 -- you should read the paper in detail during Week 4 (see recommendations below)
  • Game Tree Searching by Min / Max Approximation by Ron Rivest, MIT
  • Deep Blue by IBM
  • AlphaGo by the DeepMind Team
  • Another paper of your choosing related to AI in adversarial games (for example, a paper on DeepStack, Libratus, TDGammon, AlphaGo Zero, etc.)