Artificial Intelligence (AL-304) - Important Questions

Explain the scope and contribution of AI with real-life applications in human life and global challenges

Discuss the need and motivation for AI in modern computing and technology.

Explain the essential characteristics of production systems and how they differ from other AI problem-solving approaches.

Compare and contrast hill climbing and best-first search algorithms, including their strengths and weaknesses.

Explain the $A^*$ search algorithm in detail, including how it combines other search strategies and why it is widely used.

Explain heuristic search algorithm/strategy with suitable example.

What is production system? Explain it with an example. Discuss the characteristics of a production system.

Give an example of a problem for which breadth-first search works better than depth-first search.

Define search space in AI.

Explain how a problem is formally defined and list its components.

Define intelligence and explain the goals of Artificial Intelligence

Explain why AI is considered an interdisciplinary field and how it incorporates knowledge from multiple domains.

Differentiate between monotonic and non-monotonic reasoning, with examples of scenarios where non-monotonic reasoning is essential in AI.

Explain how predicate logic / first-order logic is used for knowledge representation in AI

Explain what AI inference is and the inference mechanisms in Artificial Intelligence.

Explain the common problems and challenges in representing knowledge in AI systems.

Discuss the various approaches, issues and problems in knowledge representation.

Formalize given natural language statements using predicate logic

Discuss theorem proving and refutation techniques.

How does knowledge representation facilitate the process of deductive reasoning in AI?

Compare and contrast propositional logic and predicate logic in terms of their expressiveness and applicability in different problem domains.

Analyze a complex AI problem such as natural language understanding and propose an effective knowledge representation scheme.

Translate statements to predicate logic, convert to clausal form, and prove by resolution which course Steve would like.

Write short notes on propositional logic and first-order predicate logic

Define Bayes' theorem and explain its significance and applications in probabilistic reasoning.

Differentiate between forward reasoning/chaining and backward reasoning/chaining.

Explain what semantic networks are in knowledge representation with an example.

Design a simple knowledge base using semantic networks for a weather prediction system.

Explain probabilistic reasoning with suitable examples.

Evaluate the strengths and weaknesses of forward chaining vs backward chaining and identify scenarios where each is more appropriate.

Differentiate between scripts, schemas and frames and explain their application in knowledge representation.

Analyze a real-world problem and show how frames can represent the knowledge needed to solve it.

Explain the role of scripts, schemas and frames in AI chatbots and virtual assistants and their potential for improving user interaction.

Evaluate limitations of frames in knowledge representation and propose enhancements or alternatives.

Explain the key advantages of using Bayes' theorem compared to other probability frameworks.

Write a short note on backward reasoning.

Explain the alpha-beta cutoff / pruning technique in game playing and its advantages.

Explain the minimax procedure for game playing, its objective, steps and working

What is the Block World problem in AI planning/robotics? Explain with an example and why it is significant.

Write short notes on Minimax algorithm, Frames and Scripts, Heuristic search, and Components of NLP

Develop/design a simple AI-based chatbot using rule-based NLP.

Propose a variation of the alpha-beta pruning algorithm to further optimize game tree search and assess its advantages.

Apply the minimax procedure to a given game tree to determine the optimal move

Investigate recent advancements, innovations and technologies in robotic systems for solving the Block World problem.

Evaluate the ethical implications of using NLP in surveillance and monitoring systems and how to address them responsibly.

Give an overview of applications of Natural Language Processing.

Define Natural Language Processing (NLP) and explain its significance in today's technological landscape.

Apply alpha-beta pruning to a given game tree and identify all pruned nodes.

Explain the components / architecture and capabilities / working of an expert system

Design / develop a rule-based expert system for a domain such as disease diagnosis, legal advice or financial planning

Explain how forward chaining and backward chaining / forward and backward reasoning are used in expert systems.

Compare forward chaining vs backward chaining in expert systems and state when each approach is preferred, with suitable scenarios.

What is an expert system and what are its key characteristics distinguishing it from other computer programs?

Explain expert system development environment.

Discuss benefits, limitations and applications of expert systems.

Mixed choice: limitations of expert systems, classical vs heuristic search, classical vs modern AI, and NLP definition and importance.

Define expert systems and explain their role in artificial intelligence and problem-solving.

Discuss the usage of backward chaining in the inference engine.

Explain the key advantages and limitations of expert systems in AI.

What are the critical factors to consider when designing and developing an effective expert system?

Write short notes on any two of the following: a) Benefits of Expert Systems b) Components of NLP c) Compare and contrast conceptual dependency analysis and semantic networks d) Importance of knowledge representation

Explain any two of the following: