ANU UG/Degree 4th Sem(Y23) Introduction to AI Unit Wise Important Questions

ANU UG/Degree 4th Sem(Y23) Introduction to AI  Unit Wise Important Questions are now available, these questions are very important for your semester exams. These questions are prepared by top qualified faculty. Read these questions for good marks.

 

UNIT – I:
Introduction to AI: What is AI? AI problems, foundation of AI and history of AI intelligent agents: Agents and Environments, the concept of rationality, the nature of environments, structure of agents, problem solving agents, problem formulation.

Short Answer Questions

1. What is artificial intelligence (AI) and how is it defined?
2. What are some of the key problems that AI aims to solve?
3. Summarize the foundational concepts and historical milestones in the development of AI.
4. Define an intelligent agent and explain the relationship between agents and their environments.
5. What is meant by the concept of rationality in intelligent agents, and why is it important?
6. How does problem formulation influence the design of problem-solving agents in AI?

Long Answer Questions

1. Discuss the evolution of artificial intelligence, covering its foundational principles, historical developments, and the impact of these milestones on modern AI applications.
2. Explain the concept of an intelligent agent, detailing its structure, the nature of its environment, and how rationality guides its behavior.
3. Analyze various AI problems and describe the approaches used to address them, providing examples of specific techniques or algorithms.
4. Elaborate on the process of problem formulation in AI. How does the way a problem is formulated affect the strategies employed by problem-solving agents?
5. Compare and contrast different types of environments in which intelligent agents operate, and discuss how these environments influence agent design and decision-making.
6. Evaluate the role of intelligent agents in solving complex problems, highlighting the interplay between agent structure, environmental factors, and the concept of rationality.

 UNIT-II:
Searching: Searching for solutions, uniformed search strategies – Breadth first search, depth first Search. Search with partial information (Heuristic search) Hill climbing, A*, AO* Algorithms, Problem reduction, Game Playing-Adversial search, Games, mini-max algorithm, optimal decisions in multiplayer games, Problem in Game playing, Alpha-Beta pruning, Evaluation functions.

Short Answer Questions

1. What is the concept of searching for solutions in AI, and how do uniformed search strategies differ from heuristic search methods?
2. How does breadth-first search (BFS) work, and what are its primary advantages and disadvantages?
3. What is depth-first search (DFS), and in what scenarios is it most effectively applied?
4. Define heuristic search and explain how hill climbing, A*, and AO* algorithms utilize heuristics.
5. What is the role of problem reduction in search strategies, and how does it simplify complex problems?
6. How does the mini-max algorithm function in adversarial game playing, and what is its significance in optimal decision-making?

Long Answer Questions

1. Discuss the differences between uniformed search strategies (BFS and DFS) and heuristic search methods (hill climbing, A*, AO*) in terms of their operational principles, advantages, and limitations.
2. Explain in detail the working mechanisms of BFS and DFS, including scenarios where each is preferable, and illustrate with examples.
3. Analyze heuristic search techniques, focusing on hill climbing, A*, and AO* algorithms. Explain how heuristics guide these searches and discuss potential pitfalls.
4. Elaborate on the concept of problem reduction in search algorithms. How does breaking down a complex problem into simpler sub-problems facilitate more efficient solution finding?
5. Describe the mini-max algorithm used in game playing, detailing how it evaluates moves in adversarial settings, and discuss the challenges associated with its implementation.
6. Evaluate the role of alpha-beta pruning in optimizing game-playing search algorithms, and explain how evaluation functions contribute to effective decision-making in multiplayer games.

 UNIT-III:
Knowledge representation issues, predicate logic- logic programming, semantic nets- frames and inheritance, constraint propagation, representing knowledge using rules, rules based deduction systems. Reasoning under uncertainty, review of probability, Baye’s probabilistic interferences and dempstershafer theory.

Short Answer Questions

1. What are the key challenges in knowledge representation, and how does predicate logic help address these issues in logic programming?
2. Define semantic nets and explain how they represent relationships between concepts.
3. What are frames and inheritance, and how do they enhance the representation of structured knowledge compared to semantic nets?
4. How does constraint propagation work in knowledge representation, and why is it important in rule-based systems?
5. What is a rules-based deduction system, and what role does it play in automated reasoning?
6. Briefly compare Bayesian probabilistic inference and Dempster-Shafer theory in handling uncertainty.

Long Answer Questions

1. Discuss the major issues in knowledge representation and explain how predicate logic and logic programming provide solutions. Include examples of their application in AI systems.
2. Analyze the use of semantic nets, frames, and inheritance in representing knowledge. Compare their strengths and limitations, and explain scenarios where one might be preferred over the others.
3. Describe the concept of constraint propagation in the context of knowledge representation. How does it facilitate the integration of rules in a deduction system?
4. Explain the role of rules-based deduction systems in AI. Discuss how these systems work, including the process of rule evaluation and the challenges involved in designing efficient rule sets.
5. Provide an overview of reasoning under uncertainty, focusing on the fundamental principles of probability. How does Bayesian inference contribute to decision-making in uncertain environments?
6. Compare and contrast Bayesian probabilistic inference with Dempster-Shafer theory. Discuss their theoretical foundations, practical applications, and the types of uncertainty they are best suited to handle.

 UNIT-IV
First order logic: Inference in first order logic, propositional vs. first order inference, unification & lifts forward chaining, Backward chaining, Resolution, learning from observation Inductive learning, Decision trees, Explanation based learning, Statistical Learning methods, Reinforcement Learning.

Short Answer Questions

1. What is first order logic and how does it differ from propositional logic in terms of inference?
2. Explain the concept of unification and its importance in first order logic inference.
3. What is the difference between forward chaining and backward chaining in the context of first order logic?
4. Define resolution in first order logic and discuss its role in automated reasoning.
5. What is inductive learning, and how does it differ from deductive reasoning?

6. Briefly describe decision trees, explanation-based learning, and one statistical learning method, highlighting their applications.

Long Answer Questions

1. Discuss the process of inference in first order logic, contrasting it with propositional inference. Include an explanation of unification and its role in facilitating inference.
2. Compare and contrast forward chaining and backward chaining techniques in first order logic. Provide examples to illustrate scenarios where each method is most effective.
3. Explain the resolution method in first order logic in detail, and analyze its advantages and limitations in automated theorem proving.
4. Elaborate on learning from observation through inductive learning. Discuss how decision trees and explanation-based learning contribute to this process.
5. Analyze statistical learning methods in the context of first order logic and symbolic AI, highlighting their differences and potential complementarities.
6. Provide an overview of reinforcement learning, describing its core principles and discussing how it can be integrated with logic-based approaches in machine learning.

 UNIT-V:
Expert systems:- Introduction, basic concepts, structure of expert systems, the human element in expert systems how expert systems works, problem areas addressed by expert systems, expert systems success factors, types of expert systems, knowledge engineering, scope of knowledge, difficulties in knowledge acquisition methods of machine learning, selecting an appropriate knowledge acquisition method, societal impacts reasoning in artificial intelligence, inference with rules, with frames: model based reasoning, case based reasoning, explanation & meta knowledge inference with uncertainty.

Short Answer Questions

1. What are expert systems and what are the basic components that constitute an expert system?
2. How do expert systems incorporate the human element in their design and decision-making processes?
3. Describe the typical structure of an expert system and explain how it operates.
4. What problem areas are commonly addressed by expert systems, and what factors contribute to their success?
5. Define knowledge engineering in the context of expert systems and discuss the challenges associated with knowledge acquisition.
6. Briefly explain the different reasoning methods in expert systems, including inference with rules, model-based reasoning, and handling uncertainty.

Long Answer Questions

1. Discuss the introduction and basic concepts of expert systems, outlining their historical evolution, fundamental structure, and the role of the human element in their design.
2. Explain in detail how expert systems work by describing their architecture, key components, and success factors, along with examples of problem areas they address.
3. Analyze the role of knowledge engineering within expert systems. Discuss the scope of knowledge, the difficulties in knowledge acquisition, and the various machine learning methods used for acquiring knowledge.
4. Evaluate the challenges and considerations involved in selecting an appropriate knowledge acquisition method for expert systems, and how these methods impact system performance.
5. Describe the different reasoning methods used in expert systems, such as inference with rules, model-based reasoning with frames, case-based reasoning, and explain how each approach deals with uncertainty.
6. Discuss the societal impacts of expert systems by evaluating their benefits and potential challenges in real-world applications, and how they influence decision-making processes in various domains.