Download CS332 - Vignan University

VFSTR UNIVERSITY
III Year B.Tech. CSE II - Semester
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CS332 ARTIFICIAL INTELLIGENCE
(ELECTIVE II)
Course Description & Objectives:
Provide knowledge of ideas and techniques underlying the design of intelligent
computer systems. Develop problem solving skills in students. Provide
knowledge of the tools and applications of AI. Lay the foundation for research
areas like Natural language Processing(NLP) and Machine learning(ML).
Course Outcomes:
·· Basic knowledge of AI principles, techniques, Expert Systems
·· Applications of basic AI techniques for problem solving.
·· Knowledge representation and new knowledge deduction in
intelligent systems.
·· A brief idea of NLP, and Machine learning techniques.
UNIT I - Intelligent Systems
Introduction- What is AI? Examples of AI systems, Brief history of AI. Intelligent
Agent- Agents and environments, The concept of rationality, The nature of
environments, Structure of agents, stimulus-response agents (simple reflex
agents), Model based agents, Goal based agents, Utility based agents,
Learning agents.
UNIT II - Problem Solving
Searching, Solving problems by searching, A* algorithm, AO* algorithm,
Heuristic functions, Hill climbing. Searching game trees (Adversarial search):
Games, Optimal decisions in games, Minimax procedure, Alpha-beta pruning.
UNIT III - Knowledge Representation
Propositional logic, Logical agents, reasoning patterns in propositional logic,
Inference in propositional logic i.e. Resolution, Forward chaining, Backward
chaining. First order logic, Reasoning patterns in First order logic, Inference
in First order logic i.e. Resolution, Forward chaining, Backward chaining.
Computer Science & Engineering
106
VFSTR UNIVERSITY
UNIT IV - Planning
The planning problem, planning with state space search, partial order
planning, planning graphs, planning with propositional logic, analysis with
planning approaches.
UNIT V - Learning
Forms of learning, Inductive learning, Learning Decision Trees, Ensemble
Learning, W hy learning works. Natural Language Processing(NLP):
Introduction, Understanding, Perception, Machine learning.
Text Book:
1. Stuart Russell, Peter Norvig, “Artificial Intelligence”, Second Edition, Pearson
Education, 2003.
Reference Books:
1. G.Luger, W.A. Stubblefield, “Artificial Intelligence”, Third Edition, AddisonWesley Longman, 1998.
2. N.J. Nilsson, “Principles of Artificial Intelligence”, Narosa Publishing House,
1980.
Computer Science & Engineering
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