An agent, in artificial intelligence, refers to anything that perceives its environment and takes actions to achieve a particular goal. It makes decisions based on a given set of rules, learning process, or other implementable strategy.
Imagine you’re playing a video game, where your character needs to find a treasure. The character you’re controlling is like an agent. It looks around, understands where it is and makes decisions to reach the treasure, just like the agent uses its sensors to understand its environment and decides on the best actions to reach its goal.
An agent is a term used in artificial intelligence to describe a system that’s situated in an environment, which it perceives through sensors and acts upon through actuators in order to achieve a goal. The agent’s actions are guided by what’s known as a policy, which is a strategy that the agent follows to determine the most appropriate actions based on its perception of environment.
Types of agents can range from simple reflex agents, which directly map states to actions, to goal-based agents, which act in order to achieve a specific goal. Learning agents improve their policy based on feedback from the environment; this process of improving a policy is often referred to as reinforcement learning.
Decision-making for agents can be deterministic or stochastic. A deterministic agent will always output the exact same action given the same input, while a stochastic agent may behave differently even when given the exact same input.
The intelligence of an agent is judged by its ability to make good decisions that maximize its overall goal, often referred to as utility. Evaluating this utility can be immediate, evaluating the decision immediately after it’s made, or delayed, evaluating multiple decisions over time.
The agent’s policy is formulated through a model of the environment. Many-a-times, this model is learned through interaction with the environment, stored as a world model, and used for decision making.
Agents in a multi-agent system work together or compete with each other to achieve their goals. Sometimes, they can collectively achieve a goal that would be difficult for an individual agent. In such systems, agents interact with their peers and the environment and may adapt their behavior based on these interactions.
Reinforcement learning, Policy, Perception, Environment, Multi-agent system, Actuator, Sensor, Utility, World model.