Ai types
1. Narrow AI (Weak AI)
Designed to perform a specific task.Most AI applications today fall into this category.
Example: Virtual assistants (Siri, Alexa), spam filters, recommendation systems.
2. General AI (Strong AI)
Hypothetical AI that can perform any intellectual task like a human.Can understand, learn, and
apply knowledge across multiple domains.Still in research stage.
3. Super AI
A theoretical stage where AI surpasses human intelligence. It could perform tasks better than
humans, including reasoning, decision-making, and creativity.
Raises ethical concerns about control and safety.
2. Based on Functionality
1. Reactive Machines
The most basic type of AI.Do not store past experiences, only react to current situations.
Example: IBM’s Deep Blue (chess-playing computer).
2. Limited Memory
Can use past data for a short period to make better decisions.Widely used in modern AI
systems like self-driving cars.
3. Theory of Mind
Future AI with the ability to understand emotions, beliefs, and intentions.Could improve
human-AI interaction.Still under development.
4. Self-Aware AI
The most advanced stage (currently theoretical).AI will have consciousness, self-awareness,
and emotions.Raises significant ethical and philosophical questions.
Breadth-First Search (BFS) is a graph traversal algorithm.
It explores all the nodes of a graph level by level (or breadth-wise), starting from a source node.
Working
1. Start from a source node.
2. Visit all adjacent (neighboring) nodes.
3. Then move to the next level and visit their neighbors.
4. Continue until all nodes are visited.
5. A queue (FIFO) is used to keep track of the nodes to be explored.
Algorithm (Steps)
1. Initialize a queue and mark the starting node as visited.
2. Enqueue the starting node.
3. While the queue is not empty:
Q7. Write a note on PEAS representation with an example.
PEAS stands for Performance measure, Environment, Actuators, Sensors.It is a framework
used to define the characteristics of an intelligent agent.
○ Performance Measure – criteria to judge agent’s success.
○ Environment – external surroundings in which agent operates.
○ Actuators – actions agent can perform to affect environment.
○ Sensors – inputs agent receives from environment.
● Example: Self-Driving Car
○ Performance measure: Safe driving, speed limit, passenger comfort.
○ Environment: Roads, traffic, pedestrians, weather.
○ Actuators: Steering, accelerator, brakes, horn.
○ Sensors: Cameras, GPS, radar, speedometer.
Q8. Explain the structure of intelligent agent. :- An intelligent agent is an entity that
perceives environment through sensors and acts on it using actuators.
Structure of an Intelligent Agent includes:
○ Sensors (input from environment).
○ Perception (converts input into useful data).
○ Knowledge Base (stores information about world).
○ Decision Making / Inference Engine (decides what to do).
○ Actuators (take actions).
● Agents can be classified as:
○ Simple Reflex Agent,
○ Model-Based Reflex Agent,
○ Goal-Based Agent,
○ Utility-Based Agent,
○ Learning Agent.
Q9. Define state space in AI search problems. :- State Space: The set of all possible states
reachable in a problem, starting from the initial state to the goal state. It is represented as a
graph/tree where:
○ Nodes = States
○ Edges = Actions that change one state to another
● Example: In a 8-puzzle game, each board arrangement is a state, and moving a tile
defines transitions.
Q10. Explain the term agent in AI & utility-based agent. :- Agent in AI: An agent is
anything that perceives its environment using sensors and acts upon that environment using
actuators.
● Utility-Based Agent:
○ These agents make decisions by considering not only goal achievement but also
how good the outcome is.
○ They use a utility function to evaluate different possible states and choose the
one that maximizes utility (happiness/success).
○ Example: Self-driving car choosing a route with less traffic (not only reaching the
destination but also minimizing time & fuel).
Q11. Explain the steps performed by problem-solving agent.
● A Problem-Solving Agent works by searching through state space to find a solution.
Steps:
1. Formulate the Goal (define what the agent wants).
2. Formulate the Problem (initial state, actions, transition model, goal test).
3. Search (use search algorithm to explore state space).
4. Execute Solution (perform sequence of actions found).
5. Evaluate (check if goal achieved).
Q12. Explain the properties of search algorithms.
Properties used to evaluate search algorithms:
1.. Completeness – Will the algorithm always find a solution if it exists? 2. Optimality – Does it
guarantee the best solution? 3. Time Complexity – How long does it take to find a solution?
4.Space Complexity – How much memory is required during the search?
