ARTIFICIAL INTELLIGENCE (Professional Elective - IV) IV Year B.Tech. IT I -Sem JNTUH R-18

1. Differentiate between optimal and human-like reasoning within the context of AI problem solving.

2. Explain the concept of state space representation and its use in formulating search problems.

3. Compare and contrast the time and space complexity of various uninformed search strategies like BFS, DFS, and iterative deepening DFS.

4. Analyze the role of heuristic functions in informed search and discuss the effectiveness of Greedy Best-First and A algorithms.*

5. Describe the limitations of classical search and explain how algorithms like hill-climbing and simulated annealing address them.

Unit - II: Search and Propositional Logic

1. Explain the minimax principle and its application in optimal decision-making for games like chess.

2. Analyze the effectiveness of alpha-beta pruning in reducing the search space during game playing.

3. Define constraint satisfaction problems (CSPs) and discuss different search algorithms used for solving them.

4. Explain the concept of propositional logic and its use in representing knowledge-based agents like the Wumpus world.

5. Compare and contrast forward and backward chaining methods for performing inference in propositional logic.

Unit - III: Logic and Knowledge Representation

1. Describe the syntax and semantics of first-order logic and explain how it provides more expressive power than propositional logic.

2. Demonstrate the use of first-order logic for representing natural language knowledge and relationships between entities.

3. Analyze the process of unification in first-order logic and its importance for performing inference.

4. Explain the key concepts of ontological engineering and how it helps in classifying and organizing knowledge within an AI system.

5. Describe different reasoning systems for categories and objects, including inheritance and reasoning with default information.

Unit - IV: Planning

1. Define the components of a classical planning problem and explain the role of state-space search in finding solutions.

2. Compare and contrast different algorithms for classical planning, such as STRIPS and partial-order planning.

3. Analyze the challenges of planning in the real world, including dealing with time, schedules, and resources.

4. Discuss how hierarchical planning can be used to decompose complex tasks into smaller, more manageable subtasks.

5. Explain the challenges and approaches for planning and acting in nondeterministic domains, where outcomes are uncertain.

Unit - V: Uncertain Knowledge and Learning

1. Explain how Bayes' rule can be used for reasoning under uncertainty in AI systems.

2. Describe the structure and function of Bayesian networks and how they can be used to represent probabilistic relationships between variables.

3. Discuss different approaches for approximate inference in Bayesian networks, such as Gibbs sampling and Markov Chain Monte Carlo methods.

4. Compare and contrast supervised learning and unsupervised learning, and explain the types of problems each is suitable for.

5. Analyze the process of learning decision trees and their usefulness in classification tasks.