Simple reflex agents versus other types of AI agents
Posted: Thu Jan 23, 2025 3:33 am
AI agents are divided into many types and classes based on their capabilities, their mode of action (reactive or proactive), and their environment (static or dynamic).
The other three AI agents include:
Utility-based agents
Model-based reflex agents
Goal-based reflex agents
1. Model-based reflex agents
Model-based reflex agents can make decisions and take actions even if they do not have a complete view of what is happening around them.
Working mechanism:
These mid-level agents have a “mental map” (aka internal state) that is continually updated with new information from sensors. So even if they can only see part of what’s going on, or if the world changes without them knowing, they can keep track of things and make educated guesses about what might happen next.
**Unlike a simple reflex agent, which only reacts to what it sees at the moment, a model-based audit directors auditors email list reflex agent thinks ahead and adapts its actions based on past experiences.
Example: Imagine a model-based agent in a maze game. It doesn't just blindly follow predefined navigation rules, but secretly consults the internal model to correlate the maze layout and the treasure location.
As the game progresses and new clues emerge, the agent updates his mental map, ready to avoid wrong turns and dead ends and claim the treasure.
Model-based reflex agents via Science Buddies: simple reflex agent
via Friends of Science
2. Goal-based agents
A goal-based agent does more than just react to its environment, it also works towards achieving specific goals. These agents evaluate the possible outcomes of their actions and choose the one that brings them closest to their goal.
Working mechanism: When you share your goal, these intelligent agents explore multiple possible alternatives using intelligent search and planning algorithms. They analyze what could happen with each choice and choose the most desirable situations to get you closer to your goal.
The other three AI agents include:
Utility-based agents
Model-based reflex agents
Goal-based reflex agents
1. Model-based reflex agents
Model-based reflex agents can make decisions and take actions even if they do not have a complete view of what is happening around them.
Working mechanism:
These mid-level agents have a “mental map” (aka internal state) that is continually updated with new information from sensors. So even if they can only see part of what’s going on, or if the world changes without them knowing, they can keep track of things and make educated guesses about what might happen next.
**Unlike a simple reflex agent, which only reacts to what it sees at the moment, a model-based audit directors auditors email list reflex agent thinks ahead and adapts its actions based on past experiences.
Example: Imagine a model-based agent in a maze game. It doesn't just blindly follow predefined navigation rules, but secretly consults the internal model to correlate the maze layout and the treasure location.
As the game progresses and new clues emerge, the agent updates his mental map, ready to avoid wrong turns and dead ends and claim the treasure.
Model-based reflex agents via Science Buddies: simple reflex agent
via Friends of Science
2. Goal-based agents
A goal-based agent does more than just react to its environment, it also works towards achieving specific goals. These agents evaluate the possible outcomes of their actions and choose the one that brings them closest to their goal.
Working mechanism: When you share your goal, these intelligent agents explore multiple possible alternatives using intelligent search and planning algorithms. They analyze what could happen with each choice and choose the most desirable situations to get you closer to your goal.