Short Questions:

1. What is a AI?

It is a branch of computer science by which we can create intelligent machines which

can behave like a human, think like humans, and able to make decisions. Artificial
Intelligence is composed of two words Artificial and Intelligence, where Artificial
defines "man-made," and intelligence defines "thinking power", hence AI means "a
man-made thinking power.
2. What are goals of AI?

• Replicate human intelligence

• Solve Knowledge-intensive tasks
• An intelligent connection of perception and action
• Building a machine which can perform tasks that requires human intelligence such
as Proving a theorem, Playing chess
• Creating some system which can exhibit intelligent behavior, learn new things by
itself, demonstrate, explain, and can advise to its user
3. What are types of AI

AI TYPE-1: BASED ON CAPABILITIES

• Weak/narrow AI
• General AI
• Super AI

AI TYPE-2: BASED ON FUNCTIONALITIES

• Reactive machines
• Limited memory
• Theory of mind
• Self awareness

4. What is Weak and strong AI

Narrow AI is a type of AI which is able to perform a dedicated task with intelligence. Narrow
AI cannot perform beyond its field or limitations, as it is only trained for one specific task.

Examples of Narrow AI are playing chess, image recognition

Super AI is a level of Intelligence of Systems at which machines could surpass human

intelligence, and can perform any task better than human with cognitive properties.

5. What are agents

An agent is anything that can perceive its environment through sensors and acts upon
that environment through effectors.
EX- human agent, robot agent
6. What is rationality

Rationality is the status of being reasonable, sensible, and having good sense
of judgment. It is concerned with expected actions and results depending upon what the
agent has perceived.
7. What is an ideal rational agent?

An ideal rational agent is the one, which is capable of doing expected actions to maximize
its performance measure, on the basis of −
Its percept sequence
Its built-in knowledge base
8. What is an environment

It is the surrounding of the agent. The agent takes input from the environment through
sensors and delivers the output to the environment through actuators.

9. What are properties of Search Algorithm.

Completeness: A search algorithm is said to be complete if it guarantees to return a

solution if at least any solution exists for any random input.HTML Tutorial
Optimality: If a solution found for an algorithm is guaranteed to be the best solution
(lowest path cost) among all other solutions, then such a solution for is said to be an
optimal solution.
Time Complexity: Time complexity is a measure of time for an algorithm to complete
its task.
Space Complexity: It is the maximum storage space required at any point during the
search, as the complexity of the problem.
10. What are the types of uninformed search?

Following are the various types of uninformed search algorithms:

1. Breadth-first Search
2. Depth-first Search
3. Depth-limited Search
4. Iterative deepening depth-first search
5. Uniform cost search
6. Bidirectional Search
11. What is difference between uninformed and informed search.
12. What is uniform cost search.
Uniform-cost search is a searching algorithm used for traversing a weighted tree or
graph. The primary goal of the uniform-cost search is to find a path to the goal node
which has the lowest cumulative cost.
13. What is Heuristics function.

Heuristic is a function which is used in Informed Search, and it finds the most promising
path. It takes the current state of the agent as its input and produces the estimation of how
close agent is from the goal. It is represented by h(n).
14. What are the features of hill climbing algorithm?

o Generate and Test variant: Hill Climbing is the variant of Generate and Test method. The
Generate and Test method produce feedback which helps to decide which direction to move in the
search space.
o Greedy approach: Hill-climbing algorithm search moves in the direction which
optimizes the cost.
o No backtracking: It does not backtrack the search space, as it does not remember the previous
states.
15. What is the local maximum and global maximum?

Local Maximum: Local maximum is a state which is better than its neighbor states, but
there is also another state which is higher than it.
Skip Ad
Global Maximum: Global maximum is the best possible state of state space landscape.
It has the highest value of objective function.
16. Define agent, rational agent and agent program.

An agent is anything that can perceive its environment through sensors and acts upon that
environment through effectors.
EX- human agent, robot agent
A rational agent always performs right action, where the right action means the action that
causes the agent to be most successful in the given percept sequence.
An artificial intelligence (AI) agent is a software program that can interact with its
environment, collect data, and use the data to perform self-determined tasks to meet
predetermined goals.
17. What do you mean by local maxima with respect to search technique?
Local maximum is a state which is better than its neighbor states, but
there is also another state which is higher than it.
18. List out some of the applications of Artificial Intelligence.

AI in Astronomy-Artificial Intelligence can be very useful to solve complex universe problems.

AI technology can be helpful for understanding the universe such as how it works, origin,etc.
AI in Gaming-AI can be used for gaming purpose. The AI machines can play strategic
games like chess, where the machine needs to think of a large number of possible places.
19. Mention the criteria for the evaluation of search strategy.
Completeness: A search algorithm is said to be complete if it guarantees to return a
solution if at least any solution exists for any random input.HTML Tutorial
Optimality: If a solution found for an algorithm is guaranteed to be the best solution
(lowest path cost) among all other solutions, then such a solution for is said to be an
optimal solution.
Time Complexity: Time complexity is a measure of time for an algorithm to complete
its task.
Space Complexity: It is the maximum storage space required at any point during the
search, as the complexity of the problem.
20. Differentiate BFS & DFS.

21. With a suitable example explain multiple-connected graph.

The term multiple connected graphs typically refers to scenarios where multiple connected
subgraphs (components) exist within a larger graph. This concept is especially relevant in
situations where different parts of the graph are connected to each other, but there are also
groups of nodes that are isolated from other groups.
Ex-Social Networks: A social network can be modeled as a graph where each person is a node,
and an edge between two nodes indicates some form of connection (e.g., friendship). If there
are groups of friends who are only connected within their group, the network would consist of
multiple connected components.
22. What are the different types of planning?
State-space planning
Plan-space or partial order planning
23. List out the different types of induction heuristics?
24. Define an inference procedure.
25. What is the basic difference between A* and AO* algorithm?
26. What is futility cutoff in game playing?
The futility cutoff in game-playing AI refers to a pruning technique used in search algorithms,
such as minimax or alpha-beta pruning, to improve performance and reduce the amount of
computation required. The futility cutoff determines when to skip evaluating a node if the
estimated value of that node is unlikely to improve on the best known value at a higher level in
the search tree.
27. Differentiate between Declarative and Procedural representation of knowledge.
Declarative Knowledge
S.NO Procedural Knowledge

It is also known as Interpretive It is also known as Descriptive

1. knowledge.
knowledge.

Procedural Knowledge means While Declarative Knowledge

2. how a particular thing can be means basic knowledge about
accomplished. something.

Procedural Knowledge is Declarative Knowledge is more

3. generally not used means it is popular.
not more popular.

Procedural Knowledge can’t be Declarative Knowledge can be

4. easily communicate.
easily communicate.

Procedural Knowledge is Declarative Knowledge is data

5. generally process oriented in oriented in nature.
nature.

In Procedural Knowledge In Declarative Knowledge

6. debugging and validation is not debugging and validation is
easy. easy.

Procedural Knowledge is less Declarative Knowledge is more

7. effective in competitive effective in competitive
programming. programming.

28. State the significance of using Heuristic functions.

Heuristic is a function which is used in Informed Search. Heuristic function estimates how
close a state is to the goal. it calculates the cost of an optimal path between the pair of
states.
29. What do you mean by local maxima with respect to search technique? repeat
30. What are the differences and similarities between Problem Solving and Planning?
31. ‘Minimax is not good for game playing when the opponent is not playing optimally.’ Justify
using suitable example.
Minimax is effective when both players are playing optimally, as it systematically evaluates every
possible move assuming perfect play. However, when the opponent does not play optimally,
Minimax becomes ineffective because it still assumes the opponent will always make the best
moves, leading to suboptimal decisions. The AI’s move is driven by the wrong expectations, and
it may miss opportunities to exploit the opponent’s mistakes. Minimax might decide that the AI
should take an aggressive move that would usually lead to a win if the opponent were playing
optimally. However, because the opponent made a poor move, this strategy might backfire,
leading to the AI missing a chance to win or get a better position. The AI might even waste a
move that could have been used to set up a winning condition or defence.
32. When and why Nonmonotonic Reasoning is used?
In Non-monotonic reasoning, some conclusions may be invalidated if we add some
more information to our knowledge base.
Logic will be said as non-monotonic if some conclusions can be invalidated by adding
more knowledge into our knowledge base.
Non-monotonic reasoning deals with incomplete and uncertain models.
33. Distinguish between Supervised learning and Unsupervised learning.
Supervised Learning Unsupervised Learning

Supervised learning algorithms are Unsupervised learning algorithms are

trained using labeled data. trained using unlabeled data.

Supervised learning model takes direct

Unsupervised learning model does not
feedback to check if it is predicting correct
take any feedback.
output or not.

Supervised learning model predicts the Unsupervised learning model finds the
output. hidden patterns in data.

In supervised learning, input data is

In unsupervised learning, only input data
provided to the model along with the
is provided to the model.
output.
 The goal of supervised learning is to train The goal of unsupervised learning is to
the model so that it can predict the find the hidden patterns and useful
output when it is given new data. insights from the unknown dataset.

Supervised learning needs supervision to Unsupervised learning does not need

train the model. any supervision to train the model.

Supervised learning can be categorized Unsupervised Learning can be classified

in Classification and Regression problems. in Clustering and Associations problems.

Supervised learning can be used for those Unsupervised learning can be used for
cases where we know the input as well as those cases where we have only input
corresponding outputs. data and no corresponding output data.

Unsupervised learning model may give

Supervised learning model produces an
less accurate result as compared to
accurate result.
supervised learning.

Supervised learning is not close to true Unsupervised learning is more close to

Artificial intelligence as in this, we first the true Artificial Intelligence as it learns
train the model for each data, and then similarly as a child learns daily routine
only it can predict the correct output. things by his experiences.

It includes various algorithms such as

Linear Regression, Logistic Regression,
It includes various algorithms such as
Support Vector Machine, Multi-class
Clustering, KNN, and Apriori algorithm.
Classification, Decision tree, Bayesian
Logic, etc.

34. What is Blocks World Problem?

This is how the problem goes — There is a table on which some blocks are placed. Some blocks
may or may not be stacked on other blocks. We have a robot arm to pick up or put down the
blocks. The robot arm can move only one block at a time, and no other block should be stacked
on top of the block which is to be moved by the robot arm.
Our aim is to change the configuration of the blocks from the Initial State to the Goal State
35. What is STRIPS?
STRIPS is a formal language used for expressing planning problems and was originally designed
to control the actions of a robot in a manipulable environment. It is primarily concerned with the
automatic generation of plans, which are sequences of actions that transition a system from its
initial state to a desired goal state.
36. What is planning in AI?
planning refers to the process of deciding a sequence of actions that a system must take to
achieve a specific goal. AI systems use planning algorithms to break down a problem into
smaller, manageable tasks and determine the best course of action.