Sr No.

Questions Marks CO
What is game programming, and how does it relate to the
1 2
development of video games?
2 Explain game development frameworks and engines. 2
Name one popular game development framework and one game
3 2
engine.
Summarize some common applications of machine learning
4 2
outside of game development?
What ethical considerations arise from the use of AI to create
5 procedurally generated content that may inadvertently include 2
offensive or inappropriate material?
Provide an example scenario where biased AI responses could
6 2
negatively affect player engagement.
7 Outline some strategies for handling large amounts of data. 2
Define machine learning and provide a brief explanation of its
8 5
core principles. CO1
Explain the role of game programming in creating interactive
9 experiences and how it involves designing game mechanics, 5
graphics, and user interfaces.
Compare and contrast traditional rule-based AI in games with
10 machine learning-based AI. Provide examples of scenarios 5
where each approach might excel.
How can machine learning techniques enhance player
11 5
experience in video games?
Describe the features and advantages of Unity as a game
12 5
development engine.
Explain how machine learning algorithms can be utilized to
13 5
create personalized gameplay experiences.
Summarize the concept of bias in AI behaviors and its potential
14 impact on player experiences. Give an example of how biased 5
AI behaviors could lead to unfair gameplay dynamics.
Explain the basic principles of supervised learning and provide
15 2
an example of how it is used in game development.
Apply the concept of feature extraction in the context of game
16 2
data.
Summarize the concept of data normalization in the context of
17 2
game data preprocessing.
18 Examine how reinforcement learning could be used in a game? 2
Describe the purpose of training and testing phases in machine
19 2
learning model development.
20 What is fine-tuning in the context of machine learning models? 2
How can semi-supervised learning techniques be applied to
21 2
improve AI performance with limited labeled training data?
Describe the core concept of reinforcement learning and its
22 5
relevance to training game AI. CO2
Compare and contrast supervised learning and reinforcement
23 learning in terms of the type of training data they require and 5
the nature of their learning objectives.
Give an example of a real-world game scenario where
24 unsupervised learning could be applied to extract meaningful 5
patterns from player behavior data.
Explain the purpose of splitting data into training and testing
25 5
sets when training machine learning models for game AI.
Identify the process of hyperparameter tuning in fine-tuning
26 5
machine learning models for game AI.
Illustrate the trade-off between underfitting and overfitting in
27 5
the context of training machine learning models for game AI.
Discuss why early stopping is used during the training of
28 5
machine learning models?
Describe the relationship between RL and the concept of an
29 2
agent interacting with an environment in a game scenario.
Examine the role of exploration strategies in Multi-Agent
30 2
Reinforcement Learning
Describe how the concept of convergence applies to the training
31 2
process of an RL agent in a game.
Determine the trade-off between exploration and exploitation in
32 2
RL algorithms for game agents.
Analyze the importance of reward shaping in reinforcement
33 2
learning for game agents
Name three fundamental elements of a reinforcement learning
34 2
scenario involving game agents.
Explain the concept of an "agent" and an "environment" in the
35 2
context of reinforcement learning for game agents.
How is RL relevant to game programming, and what role does it
36 5 CO3
play in creating intelligent game agents?
Explain the fundamental concept of the reward system in
37 reinforcement learning and how it influences agent behavior in 5
games.
Analyze the concept of Multi-Agent Reinforcement Learning
38 (MARL) and how it differs from single-agent reinforcement 5
learning.
Describe the policy gradient method and its significance in
39 5
training game agents through trial and error.
Summarize the steps involved in implementing a reinforcement
40 5
learning algorithm for a game agent.
Examine the concept of an episodic task and how it relates to
41 5
the training of game agents using reinforcement learning.
Analyze the challenges of evaluating the performance of game
42 agents trained using reinforcement learning. What metrics can 5
be used to measure their effectiveness?