An AI program is called an intelligent agent.
Properties of an intelligent agent:
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.
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 versus stochastic. Deterministic environment is one where your agent's actions uniquely determine the outcome. In stochastic environment, there is certain amount of 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
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