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Welcome to the Game Planning MCQs Page

Dive deep into the fascinating world of Game Planning with our comprehensive set of Multiple-Choice Questions (MCQs). This page is dedicated to exploring the fundamental concepts and intricacies of Game Planning, a crucial aspect of Artificial Intelligence. In this section, you will encounter a diverse range of MCQs that cover various aspects of Game Planning, from the basic principles to advanced topics. Each question is thoughtfully crafted to challenge your knowledge and deepen your understanding of this critical subcategory within Artificial Intelligence.

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Check out the MCQs below to embark on an enriching journey through Game Planning. Test your knowledge, expand your horizons, and solidify your grasp on this vital area of Artificial Intelligence.

Note: Each MCQ comes with multiple answer choices. Select the most appropriate option and test your understanding of Game Planning. You can click on an option to test your knowledge before viewing the solution for a MCQ. Happy learning!

Game Planning MCQs | Page 1 of 4

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Q1.
General games involves

a.

Single-agent

b.

Multi-agent

c.

Neither a nor b

d.

Only a and b

Discuss
Answer: (d).Only a and b
Q2.
Adversarial search problems uses

a.

Competitive Environment

b.

Cooperative Environment

c.

Neither a nor b

d.

Only a and b

Discuss
Answer: (a).Competitive Environment
Q3.
The initial state and the legal moves for each side define the __________ for the game.

a.

Search Tree

b.

Game Tree

c.

State Space Search

d.

Forest

Discuss
Answer: (b).Game Tree
Q4.
General algorithm applied on game tree for making decision of win/lose is ____________.

a.

DFS/BFS Search Algorithms

b.

Heuristic Search Algorithms

c.

Greedy Search Algorithms

d.

MIN/MAX Algorithms

Discuss
Answer: (d).MIN/MAX Algorithms
Q5.
Which search is equal to minimax search but eliminates the branches that can’t influence the final decision?

a.

Depth-first search

b.

Breadth-first search

c.

Alpha-beta pruning

d.

None of the mentioned

Discuss
Answer: (c).Alpha-beta pruning
Q6.
Which search is similar to minimax search?

a.

Hill-climbing search

b.

Depth-first search

c.

Breadth-first search

d.

All of the mentioned

Discuss
Answer: (b).Depth-first search
Q7.
How the effectiveness of the alpha-beta pruning gets increased?

a.

Depends on the nodes

b.

Depends on the order in which they are executed

c.

Both a & b

d.

None of the mentioned

Discuss
Answer: (a).Depends on the nodes
Q8.
Which is identical to the closed list in Graph search?

a.

Hill climbing search algorithm

b.

Depth-first search

c.

Transposition table

d.

None of the mentioned

Discuss
Answer: (c).Transposition table
Q9.
Which function is used to calculate the feasibility of whole game tree?

a.

Evaluation function

b.

Transposition

c.

Alpha-beta pruning

d.

All of the mentioned

Discuss
Answer: (a).Evaluation function
Q10.
What is called as transposition table?

a.

Hash table of next seen positions

b.

Hash table of previously seen positions

c.

Next value in the search

d.

None of the mentioned

Discuss
Answer: (b).Hash table of previously seen positions
Page 1 of 4

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