10 Facts About Intelligent agents

Goal-directed Intelligent agents are described using a term borrowed from economics, "rational agent".

Intelligent agents are often described schematically as an abstract functional system similar to a computer program.

Abstract descriptions of intelligent agents are called abstract intelligent agents to distinguish them from their real world implementations.

Reinforcement learning can generate intelligent agents that appear to act in ways intended to maximize a "reward function".

Evolutionary computing can evolve intelligent agents that appear to act in ways intended to maximize a "fitness function" that influences how many descendants each agent is allowed to leave.

Simple reflex Intelligent agents act only on the basis of the current percept, ignoring the rest of the percept history.

Some reflex Intelligent agents can contain information on their current state which allows them to disregard conditions whose actuators are already triggered.

Goal-based Intelligent agents further expand on the capabilities of the model-based Intelligent agents, by using "goal" information.

Goal-based Intelligent agents only distinguish between goal states and non-goal states.

Intelligent agents are applied as automated online assistants, where they function to perceive the needs of customers in order to perform individualized customer service.