Agenda Item 65/39 - Annexure - 35
BCSE306L Artificial Intelligence (3-0-0-3)
Introduction- Intelligent Agents- Problem Solving based on Searching- Informed Search
Methods- Local Search and Adversarial Search – Logic and Reasoning- Uncertain
Knowledge and Reasoning-Planning-Learning from Data-Applications in Artificial
Intelligence.
Proceedings of the 65th Academic Council (17.03.2022) 968
, Agenda Item 65/39 - Annexure - 35
BCSE306L Artificial Intelligence L T P C
3 0 0 3
Pre-requisite NIL Syllabus version
1.0
Course Objectives
1. To impart artificial intelligence principles, techniques and its history.
2. To assess the applicability, strengths, and weaknesses of the basic knowledge
representation, problem solving, and learning methods in solving engineering
problems
3. To develop intelligent systems by assembling solutions to concrete computational
problems
Course Outcomes
On completion of this course, student should be able to:
1. Evaluate Artificial Intelligence (AI) methods and describe their foundations.
2. Apply basic principles of AI in solutions that require problem-solving, inference,
perception, knowledge representation and learning.
3. Demonstrate knowledge of reasoning, uncertainty, and knowledge representation for
solving real-world problems
4. Analyse and illustrate how search algorithms play a vital role in problem-solving
Module:1 Introduction 6 hours
Introduction- Evolution of AI, State of Art -Different Types of Artificial Intelligence-
Applications of AI-Subfields of AI-Intelligent Agents- Structure of Intelligent Agents-
Environments
Module:2 Problem Solving based on Searching 6 hours
Introduction to Problem Solving by searching Methods-State Space search, Uninformed
Search Methods – Uniform Cost Search, Breadth First Search- Depth First Search-Depth-
limited search, Iterative deepening depth-first, Informed Search Methods- Best First Search,
A* Search
Module 3 Local Search and Adversarial Search 5 hours
Local Search algorithms – Hill-climbing search, Simulated annealing, Genetic Algorithm,
Adversarial Search: Game Trees and Minimax Evaluation, Elementary two-players games:
tic-tac-toe, Minimax with Alpha-Beta Pruning.
Module:4 Logic and Reasoning 8 hours
Introduction to Logic and Reasoning -Propositional Logic-First Order Logic-Inference in First
Order Logic- Unification, Forward Chaining, Backward Chaining, Resolution.
Module:5 Uncertain Knowledge and Reasoning 5 hours
Quantifying Uncertainty- Bayes Rule -Bayesian Belief Network- Approximate Inference in
Bayesian networks
Module:6 Planning 7 hours
Classical planning, Planning as State-space search, Forward search, backward search,
Planning graphs, Hierarchical Planning, Planning and acting in Nondeterministic domains –
Sensor-less Planning, Multiagent planning
Module:7 Communicating, Perceiving and Acting 6 hours
Communication-Fundamentals of Language -Probabilistic Language Processing -Information
Retrieval- Information Extraction-Perception-Image Formation- Object Recognition.
Module:8 Contemporary Issues 2 hours
Total Lecture hours: 45 hours
Text Book
1. Russell, S. and Norvig, P. 2015. Artificial Intelligence - A Modern Approach, 3rd Edition,
Prentice Hall.
Proceedings of the 65th Academic Council (17.03.2022) 994
, Agenda Item 65/39 - Annexure - 35
Reference Books
1. K. R. Chowdhary, Fundamentals of Artificial Intelligence, Springer, 2020.
2 Alpaydin, E. 2010. Introduction to Machine Learning. 2nd Edition, MIT Press.
Mode of Evaluation: CAT, Assignment, Quiz, FAT
Recommended by Board of Studies 04-03-2022
Approved by Academic Council No. 65 Date 17-03-2022
Proceedings of the 65th Academic Council (17.03.2022) 995
, PEAS IN AI: -
BCSE306L Artificial Intelligence (3-0-0-3)
Introduction- Intelligent Agents- Problem Solving based on Searching- Informed Search
Methods- Local Search and Adversarial Search – Logic and Reasoning- Uncertain
Knowledge and Reasoning-Planning-Learning from Data-Applications in Artificial
Intelligence.
Proceedings of the 65th Academic Council (17.03.2022) 968
, Agenda Item 65/39 - Annexure - 35
BCSE306L Artificial Intelligence L T P C
3 0 0 3
Pre-requisite NIL Syllabus version
1.0
Course Objectives
1. To impart artificial intelligence principles, techniques and its history.
2. To assess the applicability, strengths, and weaknesses of the basic knowledge
representation, problem solving, and learning methods in solving engineering
problems
3. To develop intelligent systems by assembling solutions to concrete computational
problems
Course Outcomes
On completion of this course, student should be able to:
1. Evaluate Artificial Intelligence (AI) methods and describe their foundations.
2. Apply basic principles of AI in solutions that require problem-solving, inference,
perception, knowledge representation and learning.
3. Demonstrate knowledge of reasoning, uncertainty, and knowledge representation for
solving real-world problems
4. Analyse and illustrate how search algorithms play a vital role in problem-solving
Module:1 Introduction 6 hours
Introduction- Evolution of AI, State of Art -Different Types of Artificial Intelligence-
Applications of AI-Subfields of AI-Intelligent Agents- Structure of Intelligent Agents-
Environments
Module:2 Problem Solving based on Searching 6 hours
Introduction to Problem Solving by searching Methods-State Space search, Uninformed
Search Methods – Uniform Cost Search, Breadth First Search- Depth First Search-Depth-
limited search, Iterative deepening depth-first, Informed Search Methods- Best First Search,
A* Search
Module 3 Local Search and Adversarial Search 5 hours
Local Search algorithms – Hill-climbing search, Simulated annealing, Genetic Algorithm,
Adversarial Search: Game Trees and Minimax Evaluation, Elementary two-players games:
tic-tac-toe, Minimax with Alpha-Beta Pruning.
Module:4 Logic and Reasoning 8 hours
Introduction to Logic and Reasoning -Propositional Logic-First Order Logic-Inference in First
Order Logic- Unification, Forward Chaining, Backward Chaining, Resolution.
Module:5 Uncertain Knowledge and Reasoning 5 hours
Quantifying Uncertainty- Bayes Rule -Bayesian Belief Network- Approximate Inference in
Bayesian networks
Module:6 Planning 7 hours
Classical planning, Planning as State-space search, Forward search, backward search,
Planning graphs, Hierarchical Planning, Planning and acting in Nondeterministic domains –
Sensor-less Planning, Multiagent planning
Module:7 Communicating, Perceiving and Acting 6 hours
Communication-Fundamentals of Language -Probabilistic Language Processing -Information
Retrieval- Information Extraction-Perception-Image Formation- Object Recognition.
Module:8 Contemporary Issues 2 hours
Total Lecture hours: 45 hours
Text Book
1. Russell, S. and Norvig, P. 2015. Artificial Intelligence - A Modern Approach, 3rd Edition,
Prentice Hall.
Proceedings of the 65th Academic Council (17.03.2022) 994
, Agenda Item 65/39 - Annexure - 35
Reference Books
1. K. R. Chowdhary, Fundamentals of Artificial Intelligence, Springer, 2020.
2 Alpaydin, E. 2010. Introduction to Machine Learning. 2nd Edition, MIT Press.
Mode of Evaluation: CAT, Assignment, Quiz, FAT
Recommended by Board of Studies 04-03-2022
Approved by Academic Council No. 65 Date 17-03-2022
Proceedings of the 65th Academic Council (17.03.2022) 995
, PEAS IN AI: -
