Agents and Environment

Description

In AI, an agent is any entity that perceives its environment and acts upon it. Depending on complexity and intelligence, agents are categorized into different types. These types represent increasing levels of autonomy and decision-making capability.

  • Simple Reflex Agent Acts only on the current percept, ignoring history. Uses condition-action rules.
  • Model-Based Reflex AgentMaintains an internal state (memory) to track unobservable parts of the environment.
  • Goal-Based Agent Takes decisions based on a goal. It considers future consequences of actions (planning involved).
  • Utility-Based AgentChooses actions based on utility (happiness or success score), to maximize satisfaction.
  • Learning AgentImproves performance over time by learning from experience and modifying its behavior.

    • Examples (Code)

    Below is a A basic code in Python:

    # Simple Reflex Agent (Example: Vacuum Cleaner)
def simple_reflex_agent(percept):
    if percept == "dirty":
        return "clean"
    else:
        return "move"

print(simple_reflex_agent("dirty"))  # Output: clean

    Real-World Applications

    Simple Reflex Agent

    Used in basic robotic vacuums like early Roombas.

    Model-Based Agent

    Used in adaptive cruise control in cars.

    Goal-Based Agent

    Used in automated spacecraft navigation systems.

    Utility-Based Agent

    Stock trading bots optimizing risk-reward.

    Learning Agent

    AI tutors adjusting difficulty as students learn.

    Where AI Is Applied

    Agent Type Application Domain Example
    Simple Reflex Agent Home Automation Thermostat, Light Controller
    Model-Based Agent Autonomous Driving Adaptive Cruise Control
    Goal-Based Agent Robotics Warehouse Robot Path Planning
    Utility-Based Agent Finance Trading Bots
    Learning Agent Education Intelligent Tutoring Systems

    Resources

    Additional resources

    Interview Questions with Answers

    What is a Simple Reflex Agent?

    An agent that chooses actions based only on the current percept, without using history or memory.

    What makes a Model-Based Agent different?

    It stores past percepts in an internal state to make more informed decisions.

    What’s the advantage of a Goal-Based Agent?

    It allows the agent to plan and decide based on desired outcomes.

    What does a Utility-Based Agent do?

    It selects actions that maximize a utility function, providing more flexibility than goal-based agents.

    How does a Learning Agent improve over time?

    It updates its knowledge and decision-making policies based on experiences and feedback.