Artificial intelligence notes of unit 5

1. Types of Intelligent
Agents in Artificial
Intelligence
Simple Reflex, Model-Based, Goal-Based, Utility-
Based, and Learning Agents

2. Simple Reflex Agents
• Ignore the percept history and act only on the basis of
the current percept.
• Percept history: The record of everything an agent has
perceived so far.
• Based on condition-action rule: If condition is true,
action is taken.
• Works well only in fully observable environments.
• In partially observable environments, infinite loops may
occur.
• Can escape loops by randomizing actions.

3. Problems:
• Very limited intelligence.
• No knowledge of non-perceptual parts of the
state.
• Usually too large to generate and store.
• Rules must be updated if the environment
changes.

4. Model-Based Reflex Agents
• Handle partially observable environments using a
model of the world.
• Find rules whose conditions match the current
situation.
• Maintain an internal state updated by percepts.
• Store the current state describing unseen parts of the
world.

5. Goal-Based Agents
• Make decisions based on how far they are from their goals.
• Each action aims to reduce the distance to the goal.
• Can choose between multiple possibilities to reach the goal.
• Knowledge is explicit and modifiable, providing flexibility.
• Require search and planning.
• Behavior can easily be changed.

6. Utility-Based Agents
• Used when multiple alternatives exist to decide the best one.
• Choose actions based on preference (utility) for each state.
• Focus not only on achieving goals but also on efficiency, safety,
or cost.
• Utility represents agent 'happiness'.
• Selects action that maximizes expected utility.
• Utility function maps state to a real number representing
happiness.

7. Learning Agents
• Learn from past experiences and adapt automatically.
• Start with basic knowledge and improve through learning.
Main components:
1. Learning Element – Improves by learning from the environment.
2. Critic – Provides feedback about performance quality.
3. Performance Element – Chooses external actions.
4. Problem Generator – Suggests new and informative actions.