INT404 Artificial Intelligence 2018 End Term Question Paper AI End term 2018 LPU Question paper |INT 404 End Term LPU
INT404 Artificial Intelligence End Term Question Paper AI End term LPU Question paper |INT 404 End Term LPU
INT404: ARTIFICIAL INTELLIGENCE
Course Outcomes:
• Describe basic knowledge representation, problem-solving, and learning methods of
artificial intelligence.
• Analyze the role of problem-solving, vision, and language in understanding human
intelligence from a computational perspective.
• Apply analytical concepts for solving logical problems using different optimized and
heuristics approach.
INT404: ARTIFICIAL INTELLIGENCE
Course Outcomes:
• Describe basic knowledge representation, problem-solving, and learning methods of
artificial intelligence.
• Analyze the role of problem-solving, vision, and language in understanding human
intelligence from a computational perspective.
• Apply analytical concepts for solving logical problems using different optimized and
heuristics approach.
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INT404 Artificial Intelligence End Term Question Paper AI End term LPU Question pape
Unit I
Introduction: What is intelligence?, Foundations of artificial intelligence(AI), History of AI, Basics of
AI, Artificial Intelligence Problems, Artificial Intelligence Techniques
Problem Spaces and Search: Defining the problem as a state space search, Production systems,
Problem characteristics, Production system characteristics, Issues in designing search problems,
Breadth first search (BFS), Depth-first search(DFS), Bi-directional Search, Iterative Deepening
Unit II
Informed Search Strategies : Best first search, A* algorithm, Heuristic functions, Generate and
Test, Hill Climbing, Simulated Annealing, Constraint satisfaction
Unit III
Knowledge Representation : Representations & mappings, Approaches in knowledge
representation, Issues in knowledge representation, Predicate logic, Propositional logic, Procedural
versus declarative knowledge, Logic programming, Forward versus backward reasoning
Unit IV
Statistical reasoning : Probability & Bayes' theorem, Bayesian networks, Dempster-Shafer-Theory,
Certainty factors & rule-based systems
Weak slot and filler structures : Semantic nets, Frames
Strong slot and filler structures : Conceptual dependency, Scripts
Unit V
Game playing : Alpha-beta pruning, Minmax Problem, The min-max search procedure
Natural Language Processing : Introduction to NLP and information retrieval, Spell checking,
Soundex algorithm, NLP phases, Applications of NLP
Unit VI
Advanced topics in Artificial Intelligence : Definition of Machine Learning, Types of Machine
Learning, Supervised Learning, Unsupervised Learning, Reinforcement Learning, Overview of Neural
Networks, Overview of Genetic Algorithms, Overview of Fuzzy Logics
Text Books:
1. ARTIFICIAL INTELLIGENCE by KEVIN KNIGHT, ELAINE RICH, B. SHIVASHANKAR NAIR, MC
GRAW HILL
References:
1. ARTIFICIAL INTELLIGENCE AND INTELLIGENT SYSTEM by N. P. PADHY, OXFORD UNIVERSITY
PRESS
2. ARTIFICIAL INTELLIGENCE: A MODERN APPROACH by STUART RUSSEL, PETER NORVIG,
PEARSON
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