Artificial Intelligence (AD-304) - Important Questions

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

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

Define intelligence and explain the goals of AI and its contribution to modern technology.

Explain heuristic search algorithm/techniques with suitable examples.

Discuss and compare $A^*$ and $AO^*$ search techniques, including advantages and disadvantages.

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

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

Define Artificial Intelligence and explain its motivation and need.

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

Explain production systems and their characteristics.

Describe search space and the hill climbing search technique.

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

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

Explain resolution (and associated inference / refutation / deduction mechanisms) in predicate logic.

Explain the issues, problems and challenges in knowledge representation in AI systems.

Compare and contrast propositional logic and predicate logic in terms of expressiveness and applicability.

Explain monotonic vs non-monotonic reasoning, their distinction, and examples of scenarios where non-monotonic reasoning is essential in AI.

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

Formalize given natural language statements using predicate logic

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

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.

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

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

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 and Bayes' theorem.

Describe semantic networks, frames and scripts for knowledge representation.

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.

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

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

What is the Block World problem in AI planning and robotics? Explain with an example and its significance.

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.

Explain the minimax algorithm together with alpha-beta pruning

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.

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 what an expert system is and describe its key characteristics, including how they distinguish expert systems from other computer programs.

Discuss components, inference engine and limitations 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: