AM24P14 ARTIFICIAL INTELLIGENCE (3 credits)
UNIT I
INTELLIGENT AGENTS AND SEARCH TECHNIQUES:
Agents and Environments – Good Behaviour: The concepts of Rationality –
The Nature of Environments – The Structure of Agents, Problem solving -
Solving problems by searching - Search in Complex Environments -
Adversarial Search and games - Constraint Satisfaction Problem
UNIT I
INTELLIGENT AGENTS AND SEARCH TECHNIQUES:
AGENTS AND ENVIRONMENTS:
1. What is an Agent?
An agent is anything that:
Perceives its environment using sensors
Acts upon the environment using actuators
Examples:
Agent Sensors Actuators
Human Eyes, ears Hands, legs
Robot Camera, sensors Motors
Software agent Keyboard input Display/output
2. Agent Function
The agent function maps:
Percept history → Action
Meaning:
An agent decides what to do based on what it has perceived so far.
3. Agents and Environments
Environment
,Everything external to the agent that it interacts with.
4. Types of Environments
1. Fully Observable vs Partially Observable
Fully Observable: Complete information available
Example: Chess
Partially Observable: Limited information
Example: Driving in traffic
2. Deterministic vs Stochastic
Deterministic: Same action → same result
Stochastic: Outcomes involve randomness
3. Episodic vs Sequential
Episodic: Each action independent
Example: Image classification
Sequential: Current action affects future
Example: Chess
4. Static vs Dynamic
Static: Environment doesn’t change
Dynamic: Environment changes over time
5. Discrete vs Continuous
Discrete: Finite states/actions
Continuous: Infinite possibilities
6. Single Agent vs Multi-Agent
Single Agent: Only one agent
Multi-Agent: Multiple agents (competitive/cooperative)
5. Performance Measure
Defines how well an agent performs.
Example:
, Self-driving car → Safety, speed, comfort
Vacuum cleaner → Cleanliness
6. Rational Agent
A rational agent:
Chooses the best action to maximize performance
Based on available information
Factors Affecting Rationality:
1. Performance measure
2. Percept sequence
3. Knowledge of environment
4. Available actions
7. PEAS Description
Used to define an AI task environment.
PEAS stands for:
P – Performance measure
E – Environment
A – Actuators
S – Sensors
Example: Self-Driving Car
Component Description
Performance Safety, speed
Environment Roads, traffic
Actuators Steering, brakes
Sensors Camera, GPS
8. Structure of Agents
UNIT I
INTELLIGENT AGENTS AND SEARCH TECHNIQUES:
Agents and Environments – Good Behaviour: The concepts of Rationality –
The Nature of Environments – The Structure of Agents, Problem solving -
Solving problems by searching - Search in Complex Environments -
Adversarial Search and games - Constraint Satisfaction Problem
UNIT I
INTELLIGENT AGENTS AND SEARCH TECHNIQUES:
AGENTS AND ENVIRONMENTS:
1. What is an Agent?
An agent is anything that:
Perceives its environment using sensors
Acts upon the environment using actuators
Examples:
Agent Sensors Actuators
Human Eyes, ears Hands, legs
Robot Camera, sensors Motors
Software agent Keyboard input Display/output
2. Agent Function
The agent function maps:
Percept history → Action
Meaning:
An agent decides what to do based on what it has perceived so far.
3. Agents and Environments
Environment
,Everything external to the agent that it interacts with.
4. Types of Environments
1. Fully Observable vs Partially Observable
Fully Observable: Complete information available
Example: Chess
Partially Observable: Limited information
Example: Driving in traffic
2. Deterministic vs Stochastic
Deterministic: Same action → same result
Stochastic: Outcomes involve randomness
3. Episodic vs Sequential
Episodic: Each action independent
Example: Image classification
Sequential: Current action affects future
Example: Chess
4. Static vs Dynamic
Static: Environment doesn’t change
Dynamic: Environment changes over time
5. Discrete vs Continuous
Discrete: Finite states/actions
Continuous: Infinite possibilities
6. Single Agent vs Multi-Agent
Single Agent: Only one agent
Multi-Agent: Multiple agents (competitive/cooperative)
5. Performance Measure
Defines how well an agent performs.
Example:
, Self-driving car → Safety, speed, comfort
Vacuum cleaner → Cleanliness
6. Rational Agent
A rational agent:
Chooses the best action to maximize performance
Based on available information
Factors Affecting Rationality:
1. Performance measure
2. Percept sequence
3. Knowledge of environment
4. Available actions
7. PEAS Description
Used to define an AI task environment.
PEAS stands for:
P – Performance measure
E – Environment
A – Actuators
S – Sensors
Example: Self-Driving Car
Component Description
Performance Safety, speed
Environment Roads, traffic
Actuators Steering, brakes
Sensors Camera, GPS
8. Structure of Agents
