Professional Electives
Other Branches
| Course Name | Principles of Artificial Intelligence |
| Course Code | 23CSE431 |
| Program | B. Tech. in Computer Science and Engineering (CSE) |
| Credits | 3 |
| Campus | Amritapuri ,Coimbatore,Bengaluru, Amaravati, Chennai |
Other Branches
Introduction to AI and systems, Agents and environments-concept of rationality, nature of environment, structure of agents, Search strategies- Uniformed search, Informed search, Local search, Adversarial search, Constrained satisfaction.
Logical Agents-propositional logic, model checking, agent based on propositional logic, First order logic-syntax and semantics of First-Order logic, assertions and queries in First-Order logic, Knowledge engineering in First-Order logic, Inference in First-order logic-forward chaining, Backward chaining, Resolution
Automated planning–algorithm for classical planning, Heuristics planning, planning and acting in nondeterministic domain Classical planning, temporal and resource constrains, uncertain knowledge and reasoning, Ethical and Legal Considerations in AI-Privacy, Bias, AI, Standards in AI systems, Future of AI-emerging development.
Course Objectives
This course provides a comprehensive, graduate-level introduction to artificial intelligence, emphasizing advanced topics such as advanced search, reasoning, decision-making under uncertainty and automated planning.
Course Outcomes
CO1: Develop understanding of the history of artificial intelligence (AI) and its foundations.
CO2: Develop understanding of various applications of AI techniques in intelligent agents and
quantifying uncertainty.
CO3: Apply various search strategies and its applicability in advanced AI systems.
CO4: Develop applications in AI language Prolog and Data Mining tool.
CO5: Apply classical planning in various application domains.
CO-PO Mapping
|
PO/PSO |
PO1 |
PO2 |
PO3 |
PO4 |
PO5 |
PO6 |
PO7 |
PO8 |
PO9 |
PO10 |
PO11 |
PO12 |
PSO1 |
PSO2 |
|
CO |
||||||||||||||
|
CO1 |
3 |
2 |
2 |
1 |
1 |
1 |
1 |
2 |
3 |
2 |
||||
|
CO2 |
3 |
2 |
3 |
2 |
3 |
2 |
2 |
2 |
3 |
2 |
||||
|
CO3 |
3 |
1 |
2 |
2 |
3 |
2 |
2 |
2 |
3 |
2 |
||||
|
CO4 |
3 |
1 |
2 |
2 |
3 |
2 |
2 |
2 |
3 |
2 |
||||
|
CO5 |
3 |
1 |
2 |
2 |
3 |
2 |
2 |
2 |
3 |
2 |
Evaluation Pattern: 70:30
|
Assessment |
Internal |
End Semester |
|
MidTerm Exam |
20 |
|
|
Continuous Assessment – Theory (*CAT) |
10 |
|
|
Continuous Assessment – Lab (*CAL) |
40 |
|
|
**End Semester |
30 (50 Marks; 2 hours exam) |
*CAT – Can be Quizzes, Assignments, and Reports
*CAL – Can be Lab Assessments, Project, and Report
**End Semester can be theory examination/ lab-based examination/ project presentation
Textbook(s)
Stuart Russell & Peter Norvig, “Artificial Intelligence: A Modern Approach”, Prentice-Hall, Third Edition (2009).
Reference(s)
Patrick Henry, “Artificial Intelligence”, Winston Pearson 2002.
National Science and Technology Council, “Preparing for the future of AI,” October 2016.
Jerome, J, “Why AI may be the next big privacy trend”, 2016.
Ginsberg M. “Essentials of artificial intelligence”, Newnes; 2012.
Luger, G. F.,Stubblefield, W. A.”Artificial Intelligence – Structures and Strategies for Complex Problem Solving”, New York, NY: Addison Wesley, Fifth edition;2005.
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