Types of Intelligent Agents

Intelligent agents can be classified according to how they use information and choose actions. The following categories are commonly used to explain different agent designs.

1. Simple Reflex Agents

Simple reflex agents select actions based on the current percept, usually through condition-action rules.

For example:

• If the room temperature is too low, turn on the heater.
• Otherwise, keep the heater off.

These agents are relatively easy to build, but they may struggle when the current observation does not provide enough information to make a good decision.

2. Model-Based Reflex Agents

Model-based agents maintain an internal representation of aspects of their environment. This helps them make decisions when the current percept alone is insufficient.

For example, a robot may remember that it recently passed through a particular corridor, helping it navigate an environment where not every location is visible at once.

3. Goal-Based Agents

Goal-based agents consider a desired state and select actions that help them reach it.

A navigation agent, for example, evaluates possible routes to reach a destination.

Unlike a simple reflex agent, a goal-based agent may need to evaluate future consequences before choosing an action.

4. Utility-Based Agents

Utility-based agents evaluate outcomes using a utility function, which assigns a value to different possible results.

A route-planning system might balance:

• Travel time.
• Distance.
• Toll costs.
• Traffic conditions.
• User preferences.

The agent can then choose a route that offers the best overall trade-off according to its utility function.

5. Learning Agents

Learning agents improve their behavior based on experience or feedback.

A learning agent may use feedback to adjust its recommendations, refine its predictions, or learn a better strategy.

A common conceptual model of a learning agent includes:

• Learning element: Improves the agent's behavior.
• Performance element: Selects actions.
• Critic: Evaluates how well the agent performs.
• Problem generator: Encourages exploration of potentially useful actions.

These components are a conceptual framework; not every learning system implements them as separate software modules.

Comparison of Agent Types

These categories can overlap. For example, a learning agent can also be goal-based or utility-based.