AI class overview

This is an overview in point form of the content in the AI class.

Welcome to AI

Introduction

• Lots of work to do
• Will be ranked

Course Overview

Purpose of the class:

• To teach the basics of AI artificial intelligence
• To excite you

Structure:

• Videos -> Quizzes -> Answer Videos
• Assignments (like quizzes but without the answers) and exams

Intelligent Agents

• Agent has sensors to determine its environment
• Agent has actuators to affect its environment
• Agent interacts in a Perception-Action cycle
• Agent's Control Policy maps sensors to actuators

Applications of AI

• Finance
• Robotics
• Games
• Medicine
• Web
• Other fields

Terminology

• Fully Observable vs Partially Observable
  • Fully Observable environment: if what your agent can sense at any point in time is completely sufficient to make the optimal decision, i.e. if the sensors can see the entire state
  • Partially Observable environment: need memory in the agent to make the best possible decision, because the agents' sensors can't see the entire state
  • When we look at Hidden Markov Models we will learn more about structuring memory when state is partially observable
• Deterministic vs Stochastic
  • Deterministic environment: agent's actions uniquely determine the outcome, there is no randomness
  • Stochastic environment: outcome of actions involve a certain level of randomness
• Discrete vs Continuous
  • Discrete environment: finitely many states and actions
  • Continuous environment: infinitely many states and actions
• Benign vs Adversarial
  • Benign environments: not really out there to get you
  • Adversarial environments: harder to find good actions when your opponent is counteracting what you're trying to achieve