AI mod 1

Question 1

Definition of AI

Answer 1

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.”

So, we can define AI as:

“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 exists when a machine can have human based skills such as learning, reasoning, and solving problems

Question 2

What are AI techniques ?

Answer 2

AI technique is a method that achieves knowledge. The main AI techniques are:

Search
Use of knowledge
Abstraction

Question 3

What is search AI technique ?

Answer 3

Search provides a way of solving problems for which no more direct approach is available as well as a framework into which any direct techniques that are available can be embedded. A search program finds a solutions for a problem by trying various sequences of actions or operators until a solution is found.

Question 4

Which AI technique is the best to solve a problem ?

Answer 4

Search AI technique. it is one of the advantage of the search technique.

Question 5

what should be done to implement search AI technique ?

Answer 5

To solve a problem using search, it is only necessary to code the operator that can be used; the search will find the sequence of actions that will provide the desired results.

It is one of the advantage of the search AI technique

Question 6

What is the disadvantage of search AI technique ?

Answer 6

Most problems have search spaces so large that it is impossible to search for the whole space.

Question 7

What is abstraction AI technique ?

Answer 7

Abstraction finds a way of separating important features and notifications from the unimportant ones that would otherwise confuse any process.

Question 8

What is use of knowledge AI technique ?

Answer 8

Provides a way of solving complex problems by exploiting the structure of the objects that are involved.

Question 9

Definition of state space search

Answer 9

A state space represents a problem in terms of states and operators that change states.
A state space consists of:
1- A representation of the states the system can be in. For example, in a
board game, the board represents the current state of the game.
2-A set of operators that can change one state into another state. In a board
game, the operators are the legal moves from any given state. Often the
operators are represented as programs that change a state representation to
represent the new state.
3- An initial state.
4- A set of final states; some of these may be desirable, others undesirable.
This set is often represented implicitly by a program that detects terminal
states.

Question 10

What is state in state space search ?

Answer 10

A representation of the states the system can be in. For example, in a
board game, the board represents the current state of the game.

Question 11

What is an operator in state space search

Answer 11

A set of operators that can change one state into another state. In a board
game, the operators are the legal moves from any given state. Often the
operators are represented as programs that change a state representation to
represent the new state.

Question 12

What is informed search ?

Answer 12

Informed Search: Informed Search algorithms have information on the goal state which helps in more efficient searching. This information is obtained by a function that estimates how close a state is to the goal state.
Example: Greedy Search and Graph Search A* Search

Question 13

What is uninformed search ?

Answer 13

Uninformed Search: Uninformed search algorithms have no additional information on the goal node other than the one provided in the problem definition. The plans to reach the goal state from the start state differ only by the order and length of actions.
Examples: Depth First Search and Breadth-First Search

Question 14

Informed and uninformed search

Which technique uses knowledge for the searching process.

Answer 14

Informed

Question 15

Informed and uninformed search

Which technique uses knowledge for the searching process.

Answer 15

Informed

Question 16

Informed and uninformed search

Which technique finds solution more quickly ?

Answer 16

Informed

Question 17

Informed and uninformed search

Which technique gets always completed ?

Answer 17

uninformed

Informed may or may not be complete.

Question 18

Informed and uninformed search

Which technique has high cost?

Answer 18

uninformed

Question 19

Basic definition of hill climbing algorithm

Answer 19

Hill climbing algorithm is a local search algorithm which continuously moves in the direction of increasing elevation/value to find the peak of the mountain or best solution to the problem. It terminates when it reaches a peak value where no neighbor has a higher value.

Question 20

Give an good example for hill climbing algorithm

Answer 20

Hill climbing algorithm is a technique which is used for optimizing the mathematical problems. One of the widely discussed examples of Hill climbing algorithm is Traveling-salesman Problem in which we need to minimize the distance traveled by the salesman.

Question 21

What is the generate and test variant feature of hill climbing algorithm ?

Answer 21

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.

Question 22

Is hill climbing utilizes the greedy approach ?

Answer 22

Greedy approach: Hill-climbing algorithm search moves in the direction which optimizes the cost.

Question 23

Does backtracking happens in hill climbing algorithm ?

Answer 23

No backtracking: It does not backtrack the search space, as it does not remember the previous states.

Question 24

State-space Diagram for Hill Climbing:

Answer 24

The state-space landscape is a graphical representation of the hill-climbing algorithm which is showing a graph between various states of algorithm and Objective function/Cost.

On Y-axis we have taken the function which can be an objective function or cost function, and state-space on the x-axis. If the function on Y-axis is cost then, the goal of search is to find the global minimum and local minimum. If the function of Y-axis is Objective function, then the goal of the search is to find the global maximum and local maximum.

https://www.javatpoint.com/hill-climbing-algorithm-in-ai

Question 25

Brief definition of best first search

Answer 25

If we consider searching as a form of traversal in a graph, an uninformed search algorithm would blindly traverse to the next node in a given manner without considering the cost associated with that step. An informed search, like Best first search, on the other hand would use an evaluation function to decide which among the various available nodes is the most promising (or ‘BEST’) before traversing to that node.
The Best first search uses the concept of a Priority queue and heuristic search. To search the graph space, the BFS method uses two lists for tracking the traversal. An ‘Open’ list which keeps track of the current ‘immediate’ nodes available for traversal and ‘CLOSED’ list that keeps track of the nodes already traversed.

Question 26

Time complexity for best first search

Answer 26

This algorithm will traverse the shortest path first in the queue. The time complexity of the algorithm is given by O(n*logn) .

Question 27

Advantages of best first search ?

Answer 27

1. Can switch between BFS and DFS, thus gaining the advantages of both.
2. More efficient when compared to DFS.

Question 28

Disadvantages of best first search ?

Answer 28

1. Chances of getting stuck in a loop are higher.

Question 29

Brief definition of means end analysis

Answer 29

We have studied the strategies which can reason either in forward or backward, but a mixture of the two directions is appropriate for solving a complex and large problem. Such a mixed strategy, make it possible that first to solve the major part of a problem and then go back and solve the small problems arise during combining the big parts of the problem. Such a technique is called Means-Ends Analysis.

Question 30

Which technique is a mixture of Backward and forward search technique.

Answer 30

Means end analysis

Question 31

How means-ends analysis Works:

Answer 31

The means-ends analysis process can be applied recursively for a problem. It is a strategy to control search in problem-solving. Following are the main Steps which describes the working of MEA technique for solving a problem.

First, evaluate the difference between Initial State and final State.
Select the various operators which can be applied for each difference.
Apply the operator at each difference, which reduces the difference between the current state and goal state.