Intro to AI - Terminology

I started the Intro to Artificial Intelligence course. I have done the first two units and found them to be very easy to follow, bordering on basic. In the past I've had similar experience with both online and normal in class courses - often they start slow and then somewhere during the course there is a jump in complexity. Lets see if this course is the same or if it will gradually ramp up.

The first unit is very introductory and mainly explains some terminology. My summary of the unit would be:

Unit 1: Welcome to Ai

Some terminology:

Intelligent Agent = AI program

Interface with Environment by sensors and actuators

Perception Action Cycle: Perceive the environment by processing Sensor information and decide how to Act using its Actuators

Attributes of Environments:

Fully or Partially Observable:

Fully Observable - where entire environment state is seen all the time. For example chess or checkers

Partially Observable - where part of the environment state is not visible at any instant. For example Poker or Dominos

Deterministic or Stochastic:

          Deterministic - no randomness. For example chess.

          Stochastic - randomness. Coin toss, dice based games.

Discrete or Continuous:

          Discrete - finite amount of states. Example board games, card games, coin flips.

          Continuous - infinite amount of states. Example Darts, self driving car, speech recognition

Benign or Adversarial:

          Benign - the environment does not react to your actions with an objective that contradicts your objective. Example the weather, roulette, solitaire.

          Adverserial - the environment has an objective that is contradictory to your objective and actively observes your actions and reacts to impede your objective. Ex. Chess

AI can also be thought of as Uncertainty Management