{{{credits}}}
| L | T | P | C |
| 3 | 0 | 2 | 4 |
- To Study the fundamental concepts of AI agents and environments.
- To Learn the methods of problem solving in AI using various search strategies.
- To Understand the concepts of knowledge representation and inference using logic.
- To Understand the concepts of knowledge representation and inference under uncertainty.
- To Learn the introductory concepts of machine learning in AI.
{{{unit}}}
| UNIT I | FOUNDATIONS | 8 |
Introduction: What is AI; Intelligent Agents: Agents and environments – Good behavior – The nature of environments – Structure of agents; Philosophical Foundations: Weak AI – Strong AI – Ethics and risks of developing AI; AI: The Present and Future: Agent components – Agent architectures.
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| UNIT II | PROBLEM SOLVING & SEARCH TECHNIQUES | 10 |
Solving Problems by Searching: Problem solving agents – Example problems – Searching for solutions – Uninformed search strategies – Informed search strategies – Heuristic functions; Beyond classical search: Local search algorithms and optimization problems; Adversarial search: Games – Optimal decisions in games – Alpha-beta pruning.
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| UNIT III | KNOWLEDGE REPRESENTATION & REASONING | 9 |
Logical Agents: Knowledge-based agents – Propositional logic – Propositional theorem proving; First order logic: Syntax and semantics for first order logic – Using first order logic; Inference in first order logic: Propositional versus first order logic – Unification and lifting – Forward chaining – Backward chaining – Resolution.
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| UNIT IV | UNCERTAIN KNOWLEDGE AND REASONING | 9 |
Quantifying Uncertainty: Acting under uncertainty – Basic probability notation – Inference using full joint distributions – Bayes’ rule & its use; Probabilistic Reasoning: The semantics of Bayesian networks – Exact inference in Bayesian networks – Other approaches to uncertain reasoning.
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| UNIT V | LEARNING | 9 |
Learning from Examples: Forms of learning – Supervised learning – Learning decision trees; Reinforcement learning: Passive reinforcement learning – Active reinforcement learning – Application to robot control.
\hfill Theory Periods: 45
- Uninformed Search Techniques
- Informed Search Techniques
- Hill Climbing algorithms
- Adversarial Search techniques
- Construction of AND-OR graph from knowledge base
- Inference from knowldge base
- Inference using full joint probability distribution
- Inference using Bayesian network
- Decision tree learning algorithm
- Passive reinforcment learning algorithm
\hfill Practical Periods: 30
\hfill Total Periods: 75
After the completion of this course, students will be able to:
- Identify, formulate, understand and solve AI problems using search techniques (K3)
- Elucidate the concept of Knowledge Representation and inference using logics (K2)
- Elucidate the concept of Knowledge Representation and inference under uncertainty (K2)
- Elucidate the concept of learning in AI applications (K3)
- Implement various search, inference and learning algorithms in AI (K4).
- Stuart Russell, Peter Norvig, “Artificial Intelligence – A Modern Approach”, 3rd Edition, Pearson Education / Prentice Hall of India, 2015.
- Deepak Khemani “A First Course in Artificial Intelligence”, McGraw Hill, 2014.
- Dawn W Patterson, “Introduction to Artificial Intelligence and Expert Systems”, 1st Edition, Pearson Education India, 2015.
- Elaine Rich, Kevin Knight, “Artificial Intelligence”, 2nd Edition, Tata McGraw-Hill, 2003.
- Andreas Muller, Sarah Guido, “Introduction to Machine Learning with Python: A Guide for Data Scientists”, Shroff/O’Reilly, 1st edition, 2016.
- David Poole, Alan Mackworth, “Artificial Intelligence : Foundation of Computational Agents”, 2nd Edition, Cambridge University Press, 2017.
- Prateek Joshi, “Artificial Intelligence with Python”, 1st edition, Packt Publishing Limited, 2017.
| PO1 | PO2 | PO3 | PO4 | PO5 | PO6 | PO7 | PO8 | PO9 | PO10 | PO11 | PO12 | PSO1 | PSO2 | PSO3 | |
| CO1 | 3 | 2 | 2 | ||||||||||||
| CO2 | 3 | 2 | 2 | ||||||||||||
| CO3 | 3 | 2 | 2 | ||||||||||||
| CO4 | 3 | 2 | 2 | ||||||||||||
| CO5 | 3 | 2 | 3 | 3 | |||||||||||
| Score | 15 | 10 | 3 | 11 | |||||||||||
| Course Mapping | 3 | 2 | 3 | 3 |