B. Sc (Computer Science), MSU, CCSU & All Universities

Artificial Intelligence

Exam Preparation for Artificial Intelligence: This model paper is designed for graduation students as per the latest National Education ... Show more

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Model Question Paper

Artificial Intelligence

Key Features

Unit-wise Short Notes
Each unit includes a summary in both languages, making revision faster and more effective.
Extensive MCQ Practice
1500+ MCQ Practice Questions: This comprehensive question bank includes 1500+ multiple-choice questions (MCQs). Each unit contains approximately 150 MCQs covering a wide range of cognitive levels such as remembering, understanding, application, and analysi.
Exam Practice Paper with Mock Tests
Includes three full-length mock tests for real exam practice. One mock test is free for students to assess the quality of our question paper.
Latest Syllabus as per NEP
The syllabus aligns with the latest National Education Policy (NEP) and follows the exam patterns of MSU, CCSU, and other universities following the NEP.
Designed by Experts
This question bank has been meticulously prepared by subject matter experts to ensure accuracy and relevance.

Why Choose This Model Paper?

Complete Exam Preparation: Unit-wise summaries, MCQ practice, and mock tests provide a complete study solution.
Latest NEP-Based Pattern: Ensures compliance with the latest university exam structure.

Program/Class: Degree/ B.Sc		Year: Third		Semester: Sixth
Subject: Computer Science
Course Title: Artificial Intelligence
Course Outcome: The main learning objectives of the course are to: Identify problems where artificial intelligence techniques are applicable. Apply selected basic AI techniques; judge applicability of more advanced techniques.
Credits: 4			Core Compulsory
Max. Marks: 25+75			Min. Passing Marks: 33
Unit	Topics
I	Approaches to AI: Introduction and Applications, History of AI from Alan Turing and developments in AI, application areas, Criteria for success, Problem Characteristics, Problem representation- State space representation, problem reduction representation, production system, Introduction to agents, intelligent software systems
II	Search and Control Strategies: Data driven and goal driven search, Uninformed search- Breadth- first search and Depth- first Search methods, Heuristic Search Techniques- Hill Climbing, best first Search, A, AO, Constraint satisfaction and means- end analysis techniques.
III	Knowledge Representation: Information and Knowledge, Knowledge Acquisition and Manipulation, Issues in Knowledge Representation, Knowledge Representation methods, Propositional logic and First Order predicate Logic, Horn’s Clauses, Semantic Networks, Frames, scripts and Conceptual Dependencies.
IV	Game Playing: Min-max Search Procedure, Adding Alpha -Beta Cut-offs
V	Expert Systems: Definition and Applications, Characteristics of Expert Systems, Architecture of a typical Expert System, Expert System shells, Building an Expert System, Expert Systems like MYCIN, Specific Application of AI, Definition of Neurons, Communication and Learning in Neural Networks.