Reactive and Goal-Driven AI Systems

AI systems can also be classified by how they respond to their environment and how they use goals to guide their actions.

Reactive AI Systems

A reactive system responds to the current input or situation. It may follow predefined responses or use a model to choose an immediate action without maintaining a detailed plan for future steps.

For example, a basic thermostat turns heating on when the temperature falls below a specified threshold and turns it off when the temperature reaches the target.

Reactive behavior is useful when decisions must be made quickly and the situation is relatively predictable.

Goal-Driven AI Systems

A goal-driven system selects actions based on a desired outcome. It may evaluate possible actions, consider constraints, and plan a sequence of steps to reach its goal.

For example, a navigation application aims to find a route from one location to another while considering road connections, travel time, and restrictions.

A goal-driven system typically needs:

• A clearly defined goal.
• Information about the current state.
• A set of possible actions.
• A way to evaluate possible outcomes.
• A strategy for selecting actions.

Reactive vs. Goal-Driven Systems

These categories are not mutually exclusive. A system can combine reactive behavior with goal-based planning.

Practical Example

Imagine a warehouse robot.

• A reactive behavior helps it stop when an obstacle appears directly in front of it.
• A goal-driven planner helps it choose a route to the next delivery location.

Together, these behaviors can help the robot navigate efficiently while responding to immediate hazards.