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

 Unit 1: Problem Solving by Search - I & II:

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

2. Describe the key components of a problem-solving agent and explain the role of state space representation in search algorithms.

3. Compare and contrast uninformed search strategies like Breadth-first search and Depth-first search, analyzing their time and space complexities.

4. Explain the concept of a heuristic function and discuss its importance in guided search algorithms like Greedy best-first search and A search.*

5. Describe the limitations of classical search algorithms and discuss alternatives like Hill-climbing search and Simulated annealing, highlighting their trade-offs.

Unit 2: Adversarial Search & Propositional Logic:

1. Formulate a game playing scenario as a search problem and explain the use of Alpha-Beta pruning for optimal decision-making.

2. Explain the challenges of making decisions in imperfect real-time situations and discuss potential AI techniques for handling them.

3. Define a Constraint Satisfaction Problem (CSP) and describe constraint propagation, a key technique for solving CSPs.

4. Introduce Propositional Logic as a knowledge representation method and explain the concept of inference using proof by resolution.

5. Design an agent based on Propositional Logic for a specific task (e.g., navigating a maze) and illustrate how it would reason and make decisions.

Unit 3: Logic and Knowledge Representation:

1. Explain the syntax and semantics of First-Order Logic (FOL) and compare it with Propositional Logic for knowledge representation.

2. Translate a real-world problem statement into FOL expressions and demonstrate how it can be used for reasoning and inference.

3. Discuss the process of unification and lifting in FOL and their role in inference techniques like forward chaining and backward chaining.

4. Describe ontological engineering as a knowledge engineering technique and explain the concept of categories and objects in knowledge representation.

5. Discuss reasoning systems for categories and explain how they handle default information within a knowledge base.

Unit 4: Planning:

1. Define the key elements of classical planning and explain how state-space search algorithms can be used for planning tasks.

2. Compare and contrast different planning approaches like planning graphs and partial-order planning, analyzing their suitability for different situations.

3. Discuss the challenges of planning and acting in the real world, considering factors like time, schedules, and resources.

4. Explain hierarchical planning as a technique for handling complex real-world tasks and its advantages over linear planning.

5. Describe the challenges of planning in non-deterministic domains and discuss multi-agent planning as a potential solution.

Unit 5: Uncertain knowledge and Learning:

1. Explain the concept of acting under uncertainty in AI and discuss the role of probability theory in handling such situations.

2. Derive and apply Bayes' rule for probabilistic reasoning and explain its significance in updating beliefs based on new evidence.

3. Compare and contrast Bayesian networks and other approaches to representing knowledge in an uncertain domain.

4. Explain the concept of supervised learning and discuss how decision trees can be used for learning from labeled data.

5. Describe the role of knowledge in learning, including explanation-based learning and learning using relevance information.