AL-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
- AL-304 paper, Dec 2025
- AL-304 paper, Jun 2025
- AL-304 paper, Dec 2024
- AL-304 paper, Jun 2024
- AL-304 paper, May 2024
- AL-304 paper, Dec 2023
Syllabus, notes and important questions for AL-304
Syllabus units
Topics as the official RGPV syllabus lists them.
Unit 1 Fundamentals and Search Techniques 12 topics
- Fundamentals of AI
- History of AI
- Motivation and need of AI
- Production systems
- Characteristics of production systems
- Goals and contributions of AI to modern technology
- Search space
- Hill Climbing
- Best‑first Search
- Heuristic search algorithms
- A* search
- AO* search
Unit 2 Knowledge Representation and Reasoning 12 topics
- Knowledge representation
- Problems in representing knowledge
- Propositional logic
- Predicate logic
- Comparison of propositional and predicate logic
- Resolution
- Refutation
- Deduction
- Theorem proving
- Inferencing
- Monotonic reasoning
- Non‑monotonic reasoning
Unit 3 Probabilistic and Symbolic Reasoning 9 topics
- Probabilistic reasoning
- Bayes' theorem
- Semantic networks
- Scripts
- Schemas
- Frames
- Conceptual dependency
- Forward reasoning
- Backward reasoning
Unit 4 Game Playing, Planning and NLP 7 topics
- Minimax procedure
- Alpha‑beta pruning
- Planning
- Block world problem in robotics
- Natural Language Processing (NLP) introduction
- Components of NLP
- Application of NLP to expert systems
Unit 5 Expert Systems 11 topics
- Characteristics of expert systems
- Requirements of expert systems
- Components of expert systems
- Capabilities of expert systems
- Inference engine
- Forward chaining
- Backward chaining
- Limitations of expert systems
- Expert system development environment
- Technology for expert systems
- Benefits of expert systems
Exam insights
Counted from 7 past AL-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 19 marks
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Unit 4 Game playing techniques 20 marks
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Unit 5 Expert systems (ES) 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 minimax procedure for game playing, its objective, steps and working
Asked 3 times: 2025, 2024, 2023