AKTU · B.Tech · Important questions
AKTU Artificial Intelligence Important Questions
The questions AKTU asks again and again in Artificial Intelligence, found by comparing every question in 16 previous year papers from 2016-17 to 2025-26 (subject codes BCAI501, BCIT054, BCS701, ECS801, KAI501, KCS071, KDS071, KME062, KOT063, NCS702, RCS702).
How this list is made: we compared all 381 questions in these papers and grouped the ones that ask about the same thing. Each item shows how many papers it appeared in, which years, and other ways it was worded. Topics that come back often are a strong hint, not a guarantee, so study your full syllabus too.
Most repeated long questions
From Sections B and C (7 or 10 marks, depending on the year). Unit numbers come from Section C, where each question covers one unit.
- Demonstrate Alpha-Beta pruning algorithm with suitable example.
- Explain Alpha-Beta pruning? Solve the following question-
- Explain the concept of alpha-beta pruning. Write alpha-beta search algorithm.
- What is alpha-beta pruning?How alpha-beta pruning can improve MIN MAX algorithm? Evaluate the given problem using alpha-beta pruning.
- Describe the role of Artificial Intelligence in Natural Language Processing.
- Explain Natural Language Processing and its components. (NLP)
- Discuss the role of NLP in AI. Describe the stages of natural language processing in artificial intelligence.
- Describe Supervised Learning and Unsupervised Learning.
- Explain supervised learning and unsupervised learning with examples.
- Define Learning. Differentiate between Supervised Learning and Unsupervised 10 Learning.
- Discuss the structure and components of a Constraint Satisfaction Problem (CSP).
- Explain in detail Constraint Satisfaction Problem with example.
- Explain forward and backward chaining with suitable example.
- Describe and compare forward chaining and backward chaining.
- Differentiate between Forward and Backward chaining of Inference with the help of an example.
- Explain how PCA is used in pattern recognition. Describe parameter estimation methods in pattern recognition.
- Define pattern recognition. Explain design principles of pattern recognition systems with suitable examples.
- Explain how tokenization impacts natural language processing tasks.
- What are language models, and how do they contribute to natural language 10 processing tasks?
- Compare semantic networks and frames as knowledge representation methods.
- Describe A* Search Technique. Prove that A* is complete and optimal.
- Discuss A* search techniques. Prove that A* is complete and optimal. Justify with example.
- Describe statistical learning model in detail.
- What are Statistical learning models? Show with suitable example.
- Describe the negotiation process in multi-agent systems with examples.
- Describe the role of computer vision in Artificial Intelligence.
- Discuss the problem of Hill Climbing Algorithm.
- Explain Hill Climbing algorithm and its limitations.
- Discuss the process and significance of information retrieval in AI.
- Describe the role of information retrieval systems in modern AI applications.
- Explain Trust and Reputation in Multi-Agent Systems with examples.
- What is trust and reputation in multi-agent systems? Explain in detail.
- Explain how argumentation is used for resolving conflicts in multi-agent systems.
- Explain information extraction in detail.
- Explain the concept of information extraction and its significance in processing unstructured data.
- Explain the architecture of intelligent agent.
- Discuss the architecture of intelligent agents with a diagram.
- Explain the difference between uninformed and informed search strategies with examples.
- Explain the role of AI in enhancing decision-making processes in business applications.
- Explain the role of events, mental events, and mental objects in intelligent agents’ reasoning.
- How do reasoning systems handle mental events, mental objects, and what are the implications for AI applications?
- Illustrate Decision Trees Technique using suitable example.
- Illustrate decision trees technique using a suitable example.
- Prove that following sentence is valid ” If price fall then sell increases. If sell increases then Jhon makes the whole money. But John doesn’t make the whole money. Therefore , price do not fall”.
- Prove that following sentence is valid: “If prices fall then sell increases. If sell increases then John makes the whole money. But john doesn’t make the whole money. Therefore, prices do not fall.”
- Translate the following sentences into formulas in Predicate Logic and Clausal Form: i. John likes all kind of food. ii. Apples are food. iii. Chicken is food. iv. Anything any one eats and is not killed by is food. v. Bill eats peanuts and is still alive. vi. Sue eats everything Bill eats.
- What are Image- Processing Operations? Define how Vision can be used for navigation and manipulation.
- What are the features of an Image Processing technique? Compare the role of 10 vision for navigation and manipulation of an object.
Most repeated short questions (2 marks)
From Section A. Learn a crisp definition plus one example or line of explanation for each.
- Define Artificial Intelligence.
- Explain the term Artificial Intelligence.
- List various applications of Artificial Intelligence.
- Write short note on state of the art of Artificial Intelligence.
- Discuss Supervised & Unsupervised learning.
- Differentiate supervised and unsupervised learning.
- Write the difference between supervised and unsupervised learning.
- Explain problem solving methods..
- Define "problem-solving" in AI.
- Define problem-solving in the context of AI.
- Explain the concept of speech recognition.
- Write some applications of Speech Recognition.
- Differentiate between machine translation & Speech recognition.
- What do you mean by machine translation?
- Explain the concept of machine translation.
- What is Turing test?
- Describe the turing test for intelligence.
- Define Constraint Satisfaction Problems
- Define Practical Natural Language Processing.
- Describe the role of Artificial Intelligence in Natural Language Processing.
- Define an intelligent agent.
- What is an intelligent agent?
- Define rational agent.
- Differentiate between an agent and a rational agent.
- Differentiate between forward and backward chaining.
- Differentiate between forward chaining and backward chaining.
- Discuss the concept of Information retrieval.
- Define the characteristics of Information retrieval.
- How Artificial intelligence is different from human intelligence.
- Define Artificial Intelligence. Is it different from intelligence?
- List various issues in knowledge representation.
- List various schemes of knowledge representation.
- Recall local search.
- Define a local search?
- What are Software Agents? Give examples.
- Define negotiation in the context of software agents.
- What is First Order Predicate Logic (FOPL)?
- What is Pattern Recognition?
- Write down some applications of pattern recognition.
Unit-wise questions from recent papers
Section C questions from the latest papers (2025-26 BCS701, 2025-26 BCAI501, 2024-25 KCS071), sorted by unit. Use these to practise one unit at a time.
Unit 1
- Explain the transition of Artificial Intelligence from rule-based systems to machine learning-based systems. (2025-26, BCAI501)
- Explain the concept of an environment in which an AI agent operates. (2025-26, BCAI501)
- Explain the problem-solving approach to AI with an example and also Explain problem-solving agents and their components. (2025-26, BCS701)
- Discuss the architecture of intelligent agents with a diagram. (2025-26, BCS701)
- Illustrate the differences between reactive and proactive intelligent agents including applications. (2024-25, KCS071)
- Examine the ways the future of Artificial Intelligence can address the current limitations in natural language processing and understanding. (2024-25, KCS071)
Unit 2
- Explain the formulation and solution techniques of Constraint Satisfaction Problems (CSP) with examples. (2025-26, BCAI501)
- Discuss stochastic games and the role of probability in decision-making. (2025-26, BCAI501)
- What do you mean by Constraint Satisfaction Problems (CSP) with examples? (2025-26, BCS701)
- Explain stochastic games and partially observable games. (2025-26, BCS701)
- Discuss the primary problem-solving methods of artificial intelligence, and how do they differ from traditional computational approaches? (2024-25, KCS071)
- Examine the working of backtracking search in solving CSPs, along with key advantages and limitations. (2024-25, KCS071)
Unit 3
- Demonstrate resolution principle by proving a simple theorem in propositional logic. (2025-26, BCAI501)
- Create a forward chaining example in a rule-based system to determine eligibility for a scholarship. (2025-26, BCAI501)
- Describe and compare forward chaining and backward chaining. (2025-26, BCS701)
- Discuss ontological engineering with its importance and also Explain reasoning using ontologies. (2025-26, BCS701)
- Evaluate First Order Predicate Logic (FOPL), and its difference from propositional logic in terms of expressiveness and complexity. (2024-25, KCS071)
- How do reasoning systems handle mental events, mental objects, and what are the implications for AI applications? (2024-25, KCS071)
Unit 4
- Explain the complete architecture of intelligent agents including deliberative, reactive, and hybrid layers. (2025-26, BCAI501)
- Describe how learning agents adapt their behavior over time. (2025-26, BCAI501)
- Explain supervised learning and unsupervised learning with examples. (2025-26, BCS701)
- Derive and explain Bayes’ Rule with an example. (2025-26, BCS701)
- Explain the role of negotiation and bargaining in multi-agent systems, and how do agents reach agreements? (2024-25, KCS071)
- Examine the challenges in establishing trust among agents in a multi- agent system. (2024-25, KCS071)
Unit 5
- Explain planning algorithms in AI, such as STRIPS and hierarchical task networks. (2025-26, BCAI501)
- Analyze the speech recognition techniques including acoustic modeling and language modeling. (2025-26, BCAI501)
- Discuss case studies of XAI in NLP and computer vision. (2025-26, BCS701)
- Explain the role of trust and interpretability in AI. (2025-26, BCS701)
- Discuss the steps involved in speech recognition and its importance in human-computer interaction. (2024-25, KCS071)
- Explain the concept of information extraction and its significance in processing unstructured data. (2024-25, KCS071)
Papers used for this list
- AKTU Artificial Intelligence (BCAI501) question paper 2025-26
- AKTU Artificial Intelligence (BCS701) question paper 2025-26
- AKTU Artificial Intelligence (BCAI501) question paper 2024-25
- AKTU Artificial Intelligence (BCIT054) question paper 2024-25
- AKTU Artificial Intelligence (KCS071) question paper 2024-25
- AKTU Artificial Intelligence (KDS071) question paper 2024-25
- AKTU Artificial Intelligence (KAI501) question paper 2023-24
- AKTU Artificial Intelligence (KCS071) question paper 2023-24
- AKTU Artificial Intelligence (KME062) question paper 2023-24
- AKTU Artificial Intelligence (KAI501) question paper 2022-23
- AKTU Artificial Intelligence (KCS071) question paper 2022-23
- AKTU Artificial Intelligence (KOT063) question paper 2022-23
- AKTU Artificial Intelligence (KME062) question paper 2021-22
- AKTU Artificial Intelligence (RCS702) question paper 2020-21
- AKTU Artificial Intelligence (NCS702) question paper 2017-18
- AKTU Artificial Intelligence (ECS801) question paper 2016-17
How to use these questions
- Start with the questions asked in the most papers, and make short notes for each.
- Write 2–3 long answers by hand with a timer (about 12–15 minutes each), then compare them with your textbook.
- Revise all the 2-mark questions the night before the exam.
- Finish with one full paper from the list above, timed.
Full plan with AI prompts: How to prepare for AKTU exams using previous year papers. After results, check your marks with the AKTU CGPA calculator.
Frequently asked questions
Are these AKTU Artificial Intelligence questions guaranteed to come in the exam?
No. They are the questions that repeated most in past papers, which makes them a strong hint, but AKTU can ask anything from the syllabus. Use this list to prioritise, not to skip units.
How was this important questions list made?
We compared all 381 questions from 16 AKTU Artificial Intelligence papers (2016-17 to 2025-26) and grouped questions that ask about the same topic, then counted how many papers each appeared in.
Where can I download AKTU Artificial Intelligence previous year papers?
Every paper used here is on LearnAIFast with all questions as text and a link to the original PDF. See the list of papers above.
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