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.

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

Single-agent

Multi-agent

Neither a nor b

Only a and b

Discuss

Answer: (d).Only a and b

Q2.

Adversarial search problems uses

Competitive Environment

Cooperative Environment

Neither a nor b

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.

Search Tree

Game Tree

State Space Search

Forest

Discuss

Answer: (b).Game Tree

Q4.

General algorithm applied on game tree for making decision of win/lose is ____________.

DFS/BFS Search Algorithms

Heuristic Search Algorithms

Greedy Search Algorithms

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?

Depth-first search

Breadth-first search

Alpha-beta pruning

None of the mentioned

Discuss

Answer: (c).Alpha-beta pruning

Q6.

Which search is similar to minimax search?

Hill-climbing search

Depth-first search

Breadth-first search

All of the mentioned

Discuss

Answer: (b).Depth-first search

Q7.

How the effectiveness of the alpha-beta pruning gets increased?

Depends on the nodes

Depends on the order in which they are executed

Both a & b

None of the mentioned

Discuss

Answer: (a).Depends on the nodes

Q8.

Which is identical to the closed list in Graph search?

Hill climbing search algorithm

Depth-first search

Transposition table

None of the mentioned

Discuss

Answer: (c).Transposition table

Q9.

Which function is used to calculate the feasibility of whole game tree?

Evaluation function

Transposition

Alpha-beta pruning

All of the mentioned

Discuss

Answer: (a).Evaluation function

Q10.

What is called as transposition table?

Hash table of next seen positions

Hash table of previously seen positions

Next value in the search

None of the mentioned

Discuss

Answer: (b).Hash table of previously seen positions

Page 1 of 4

Welcome to the Game Planning MCQs Page

Game Planning MCQs | Page 1 of 4

Explore more Topics under Artificial Intelligence

Introduction to AI

Intelligent Agents

Problem Solving

Knowledge and Reasoning

Uncertain Knowledge

Learning

Perceiving and Acting

Natural Language Processing

LISP Programming

Expert Systems

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