Unit 1: Introduction to AI
-
What is AI? How can you define AI from the perspective of the thought process?
-
How philosophy, sociology, and economics influence the study of artificial intelligence?
-
Describe how Turing test is used to define AI as acting humanly.
-
What is intelligence? Describe the foundation of AI.
-
What is Turing Test? What properties an agent should have to pass the Turing Test?
Unit 2: Intelligent Agents
-
What is an agent? How does a utility agent work? Give an example of a utility agent.
-
What are the properties of an intelligent agent? How do simple reflex agents work? Give an example.
-
Differentiate between model-based and simple reflex agent with an example.
-
What do you mean by Rational Agent? What are differences between Utility-based and model-based agent?
-
Discuss the types of environment where an agent can work on.
-
Using your own assumptions, design PEAS framework for:
-
Medicine delivery drone
-
Covid medicine prescriber
-
Internet Shopping Assistant
-
English Language Tutor
-
Covid-19 prediction system
-
Vaccine recommender system
-
Unit 3: Problem Solving by Searching
-
Define state space graph. Differentiate between A* search and greedy best first search.
-
How is informed search different from uninformed search?
-
How uniform cost search is used to search goal in the state space? Illustrate with example.
-
What is game search? How is Minimax search used in game playing? Illustrate with example.
-
Why alpha-beta pruning is necessary? How is it done? Illustrate.
-
What is constraint satisfaction problem? Illustrate graph coloring problem.
-
What is state space representation? Illustrate with one example.
-
How depth-limited search and iterative deepening search work? Illustrate with example.
-
Construct state space and apply:
-
A*
-
Greedy Best First Search
-
Hill Climbing (and discuss its incompleteness)
-
Unit 4: Knowledge Representation
-
What is semantic network? Construct semantic network with given facts.
-
What is frame? How is knowledge encoded in a frame? With example.
-
How is knowledge represented using scripts? Create a knowledge base.
-
Convert statements into FOPL and CNF.
-
What is forward chaining? Explain with example.
-
How uncertain knowledge is represented? (Bayesian/Joint distribution)
-
What is fuzzy logic? Construct fuzzy rule base expert system.
-
What do you mean by unification and lifting?
-
Write the rules to convert statements into CNF form.
-
How resolution algorithm is used in FOPL to infer conclusion?
-
Construct belief network from given probability conditions.
-
Discuss predicate logic resolution example:
-
"Roney is naughty"
-
"Sushma likes PHP"
-
"If all drivers horn, then all traffics are frustrated"
-
"Pugu doesn't love Anmol"
-
Unit 5: Machine Learning
-
Differentiate supervised learning from unsupervised. How Naive Bayes model works?
-
What is supervised learning? Discuss Naive Bayes model with example.
-
What is reinforcement learning? Give example.
-
Define genetic algorithm. Explain selection, crossover, mutation with example.
-
Write algorithm for learning by genetic approach.
-
Define artificial neural network. Explain with model.
-
Discuss Perceptron learning.
-
Simulate OR gate using ANN.
-
Explain Hebbian learning with example.
-
What is the role of activation function? How sigmoid works?
-
Write algorithm for backpropagation learning and show 1 iteration.
Unit 6: Applications of AI
-
What is expert system? Define with example. Stages of development.
-
Explain major components of expert system.
-
How machine vision is used in robotics?
-
How NLP works? Explain all steps: Morphological, Syntactic, Semantic, Pragmatic.
-
What is pragmatic analysis? How is it done?
-
What is morphological analysis in NLP?
-
Construct fuzzy rule-based expert system.
-
Describe components of machine vision system.