• Online, Self-Paced
Course Description

Many problems occur in environments with more than one agent, such as games. In this course, you will learn some techniques used to solve adversarial problems to make agents play games, like chess.

Learning Objectives

Adversarial Games

  • start the course
  • describe adversarial problems and the challenges they impose on AI
  • specify how to represent an adversarial problem
  • describe how to use the minimax algorithm to play an adversarial game and some of its shortcomings
  • describe how to use alpha-beta pruning to improve the performance of the minimax algorithm


Imperfect Decisions

  • describe evaluation functions
  • describe how to use cutoffs to be able to perform adversarial searches under a time constraint
  • describe how lookup tables can be used to improve an agent's performance
  • describe chess and how agents can be made to play the game of chess


Stochastic Games

  • describe expect minimax values in stochastic games and how they make solution searching harder
  • describe different evaluation functions that can be used to search in a stochastic game
  • describe how to use Monte Carlo simulations to make decisions when searching


Practice: Using the Minimax Algorithm

  • build a full high-level representation and solution for an adversarial game using the minimax algorithm and alpha-beta pruning


Framework Connections

The materials within this course focus on the NICE Framework Task, Knowledge, and Skill statements identified within the indicated NICE Framework component(s):

Specialty Areas

  • Software Development
  • Technology R&D