AD-304 RGPV B.Tech
Artificial Intelligence (AI)
Previous year papers and the official syllabus are free to read, no account needed.
Previous Year Papers
8 papers, latest Dec 2025
- AD-304 paper, Dec 2025
- AD-304 paper, Jun 2025
- AD-304 paper, Dec 2024
- AD-304 paper, Jun 2024
- AD-304 paper, May 2024
- AD-304 paper, Dec 2023
Syllabus, notes and important questions for AD-304
Syllabus units
Topics as the official RGPV syllabus lists them.
Unit 1 Fundamentals of Artificial Intelligence 3 topics
- History, motivation and need of AI
- Production systems and their characteristics
- Search space and search techniques: hill climbing, best‑first search, heuristic search, A* and AO*
Unit 2 Knowledge Representation 7 topics
- Problems in representing knowledge
- Knowledge representation using propositional logic
- Knowledge representation using predicate logic
- Comparison of propositional and predicate logic
- Resolution, refutation and deduction
- Theorem proving and inferencing
- Monotonic and non‑monotonic reasoning
Unit 3 Probabilistic Reasoning and Semantic Networks 4 topics
- Bayes' theorem and probabilistic reasoning
- Semantic networks, scripts, schemas and frames
- Conceptual dependency
- Forward and backward reasoning
Unit 4 Game Playing, Planning and Natural Language Processing 4 topics
- Game playing techniques: minimax procedure and alpha‑beta cut‑offs
- Planning and the block‑world problem in robotics
- Introduction to Natural Language Processing (NLP)
- Components of NLP and its application to expert systems
Unit 5 Expert Systems 4 topics
- Characteristics, requirements and components of expert systems
- Inference engine: forward and backward chaining
- Limitations of expert systems
- Expert system development environment, technology and benefits
Exam insights
Counted from 7 past AD-304 papers.
Marks asked per paper, by unit
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Unit 1 Fundamental of Artificial Intelligence 31 marks
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Unit 2 Knowledge Representation 19 marks
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Unit 3 Probabilistic Reasoning and Structured Knowledge 19 marks
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Unit 4 Game Playing, Planning, and NLP 20 marks
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Unit 5 Expert Systems 19 marks
Average marks of the questions from each unit in one paper. You answer only some of them, so the total is more than the paper’s maximum.
Asked again and again
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Explain the alpha-beta cutoff / pruning technique in game playing and its advantages.
Asked 4 times: 2025, 2023, 2022
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Define Bayes' theorem and explain its significance and applications in probabilistic reasoning.
Asked 3 times: 2025, 2024, 2023
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Explain the components / architecture and capabilities / working of an expert system
Asked 3 times: 2025, 2023, 2022