In AI, an agent is an entity that perceives its environment through sensors and acts upon it using actuators. AI agents are classified based on their complexity and decision-making capabilities.
Thank you for reading this post, don't forget to subscribe!Types of Agents in AI includes:
- Simple Reflexive Agents
- Model-Based Reflexive Agents
- Goal-Based Agents
- Utility-Based Agents
- Learning Agents
1.) Simple Reflexive Agents:
A simple reflex agent is a type of AI agent that makes decisions based on the current state of the environment and follows a set of predefined rules.
- It does not maintain any memory of past actions or states.
- It works well in fully observable environments but fails in dynamic or partially observable settings.
Example: A thermostat that turns on heating when the temperature drops below a set level.
2.) Model-Based Reflexive Agents:
A model-based reflex agent is a type of AI agent that maintains an internal model of the environment, allowing it to handle partially observable environments.
- It remembers past states to make better decisions.
- It updates its internal model based on new observations.
Example: A self-driving car keeps track of road conditions, lane positions, and nearby cars to make driving decisions.
3.) Goal-Based Agents:
A goal-based agent is a type of AI agent that considers future actions and chooses the best path to achieve a specific goal.
- It does not act randomly but instead evaluates different possibilities before making a decision.
- It requires planning and searching techniques.
Example: Google Maps determines the best route to a destination based on real-time traffic data.
4.) Utility-Based Agents:
A utility-based agent is a type of AI agent that takes actions based on a utility function, which quantifies how desirable a particular state is.
- Unlike goal-based agents, which only focus on achieving the goal, utility-based agents aim for the best possible outcome.
- It helps in decision-making under uncertainty by choosing actions that maximize overall benefit.
Example: A stock trading AI predicts future trends and selects investments that maximize profit while minimizing risk.
5.) Learning Agents:
A learning agent is a type of AI agent that improves its performance over time by learning from past experiences.
- It uses machine learning techniques to adapt and make better decisions.
It consists of four components:
- Learning Element – Improves agent behavior.
- Performance Element – Makes decisions.
- Critic – Provides feedback on performance.
- Problem Generator – Suggests new actions to explore.
Example: ChatGPT, which learns from user interactions to generate better responses.