Artificial Intelligence (IT-504 (A)) - Important Questions

Explain Breadth First Search vs Depth First Search, compare their advantages/disadvantages, and give an example where BFS is better.

Explain A* search, including $f'$, $g'$, $h'$, OPEN and CLOSED lists / stages, with a suitable example.

Explain the problems/limitations of the Hill climbing algorithm and their solutions

Define Artificial Intelligence, explain its goals / scope and expectations, discuss various researchers' definitions and applications in detail.

What is a production system? Explain its components/technique with example and its characteristics/importance.

Write a detailed short note on the Hill climbing algorithm

Test whether the given production systems are commutative with justification.

Compare BFS vs Best First Search and Hill Climbing vs A* algorithm, including advantages, disadvantages and applications.

Find the shortest path using A* algorithm (generic graph search).

What is control strategy? What are the characteristics of a good control strategy?

Define/explain heuristic search and heuristic function, how it helps during search, with examples.

How to define a problem as state space search, with an example.

Prove a propositional entailment from a knowledge base by resolution refutation.

Explain the concept of knowledge representation in detail, including its approaches, characteristics, and issues/challenges in representing real-world knowledge.

Represent / convert given English sentences or facts into first-order / predicate logic.

Explain knowledge representation using predicate (first-order) logic, including constants, variables, predicates, quantifiers, propositional vs predicate logic, and inference rules with examples.

Use first-order resolution to find the instance satisfying a query about what is liked.

Show that the formula $(A(X) \lor B(Y)) \to C(Z) \Rightarrow (\neg A(X) \land \neg B(Y)) \lor C(Z)$ is valid.

Describe the role of logic in Artificial Intelligence. Explain how propositional and predicate logic support knowledge representation and automated reasoning.

Write short notes on following (any two)

Explain the difference between procedural and declarative knowledge.

Explain properties/characteristics of a good knowledge representation system and describe various knowledge representation techniques/schemes in brief.

Construct (partitioned) semantic net representations / diagrams for given sentences or facts.

State and explain Bayes' Theorem with a numerical example and its significance in AI

Explain conceptual dependency with an example and construct conceptual dependency representations for given sentences.

Given two fuzzy sets, compute union, intersection, complement and difference.

Explain probabilistic reasoning in AI, compare it with logical/deterministic reasoning, and discuss why it is needed for reasoning under uncertainty, its advantages, limitations, and situations where it is preferred.

Apply Bayes' Theorem numerically to compute conditional pass percentage for two tests

Define fuzzy logic and its various operations.

Construct a Bayesian network with conditional probability tables for the given statements

Distinguished the forward and backward reasoning with example.

Explain how semantic nets are used in knowledge representation and reasoning.

What is Natural Language Processing? Explain its phases/levels/components such as morphological, syntactic, semantic, and pragmatic analysis.

Show parse trees for the given sentences: i) John wanted to go to the movie with Sue. ii) I heard the story listening to the radio.

Explain different approaches to Natural Language Processing: rule-based, statistical, and hybrid approaches with examples.

Demonstrate the first 3-ply moves in tic-tac-toe using the alpha-beta pruning algorithm.

Explain the Blocks World problem, its representation of blocks, actions and constraints, and how planning solves it.

Solve a given Blocks World start-to-goal instance using STRIPS and using TWEAK planning.

Explain how the minimax procedure must be modified for a three- or four-person game.

Define a plausible-move generator and explain its role.

Explain the minimax (min-max) procedure for choosing a move in a two-player game.

How does learning in problem solving improve performance?

Explain the architecture of artificial neural networks, including single-layer and multi-layer networks with diagrams.

Describe how transformational and derivational analogy apply in case-based reasoning systems.

Development of expert systems including knowledge acquisition and knowledge representation techniques

Stages of the expert system life cycle

Compare promising application domains for expert system approach vs neural network approach

Explain the basic model of an ANN and compare biological neurons vs artificial neural networks.

Write short notes on learning techniques, NLP, knowledge representation, and frames.

What is explanation-based learning? Explain in detail with an example.

Define expert systems and explain architecture with neat diagram and example

Short note on expert system (with Bayes theorem, resolution technique, monotonic and non-monotonic reasoning)

Explain the various types of learning in neural networks.

Write short notes on following (any two):