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cs271 Lesson 1 notes


Intelligent Agents

An AI program is called an intelligent agent.
Properties of an intelligent agent:

  • interacts with an environment in a state
  • uses sensors to perceive its state
  • uses actuators to affect its state
  • has a function called its control policy that maps sensors to actuators

This class will deal with how an agent makes decisions that it can carry out with its actuators based on past sensor data. The loop of environment feedback to sensors, agent decision, actuator interaction with the environment and so on is called perception action cycle.

Applications Of Ai

Domain: environment, sensors, actuators.
Finance: market, prices or world events, trades.
Robotics: world, cameras/tactile sensors, motors. 


Environments can have different characteristics. 

fully versus partially observable. An environment is called fully observable if what your agent can sense at any point in time is completely sufficient to make the optimal decision i.e. its sensors can see the entire state of the environment. That is in contrast to some other environments where agents need memory to make the best decision.

deterministic or stochastic. Deterministic environment is one where your agent's actions uniquely determine the outcome. There is no randomness.

discrete versus continuous. A discrete environment is one where you have finitely many action choices, and finitely many things you can sense. For example, in chess there's finitely many board positions, and finitely many things you can do.

benign versus adversarial environments. In benign environments, the environment might be random. It might be stochastic, but it has no objective on its own that would contradict your own objective. For example, weather is benign. Contrast this with adversarial environments, such as many games, like chess, where your opponent is really out there to get you.

Examples of environments

  • checkers: fully observable, deterministic, discrete, adversarial
  • poker: partially observable, stochastic, discrete, adversarial
  • robot car: partially observable, stochastic, continuous, benign

AI And Uncertainty

AI is the technique of uncertainty management in computer software. AI is the discipline that you apply when you want to know what to do when you don't know what to do. 

Reasons for uncertainty

  • sensor limit
  • stochastic environment
  • laziness
  • ignorance - we don't know


  • attributes of intelligent agents: (partial observability, stochasticity, continuous spaces, and adversarial natures)