Page 62 - B.E CSE Curriculum and Syllabus R2017 - REC

Department of CSE, REC

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2. John Horton, “Learning Java by Building Android Games”, Packt Publishing Limited, 1 edition,
2015.
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3. Jorge Palacios, “Unity 5.x Game AI Programming Cookbook”, Packt Publishing Limited, 1 edition,
2016.

CS17602 ARTIFICIAL INTELLIGENCE L T P C
3 0 0 3
OBJECTIVES:
● To understand the various characteristics and search strategies of Intelligent agents
● To learn about the different strategies involved in problem solving
● To learn to represent knowledge in solving AI problems
● To understand the different models of learning
● To apply A.I to various applications

UNIT I INTRODUCTION 9
Introduction–Definition - Foundations of Artificial Intelligence – Introduction to Learning - Intelligent Agents
-The Nature of Environments - Characteristics and Structure of Agents– Problem-Solving Agents -
Uninformed Search Strategies - Informed (Heuristic) Search Strategies.

UNIT II METHODS OF PROBLEM SOLVING 9
Local Search Algorithms and Optimization Problems - Searching with Partial Observations - Constraint
Satisfaction Problems – Constraint Propagation - Backtracking Search - Game Playing - Optimal Decisions in
Games – Alpha - Beta Pruning - Stochastic Games.

UNIT III REPRESENTING KNOWLEDGE 9
Knowledge Based Agents- First Order Predicate Logic – Prolog Programming – Unification – Forward
Chaining-Backward Chaining – Resolution – Ontological Engineering-Categories and Objects – Events -
Mental Events and Mental Objects - Reasoning Systems for Categories - Reasoning with Default Information.

UNIT IV LEARNING 9
Forms of Learning - Supervised Learning - Learning Decision Trees - Evaluating and Choosing the Best
Hypothesis - Regression and Classification with Linear Models - Artificial Neural Networks - Support Vector
Machines - Ensemble Learning.

UNIT V APPLICATIONS 9
AI applications – Information Mining – Natural Language processing -– Robot – Perception – Planning –
Moving.
TOTAL: 45 PERIODS

OUTCOMES:
At the end of the course, student will be able to:
● Use appropriate search algorithms for any AI problem.
● Provide the apt agent strategy to solve a given problem.
● Represent a problem using first order and predicate logic.
● Suggest supervised, unsupervised or semi-supervised learning algorithms for any given problem.
● Design applications like NLP that uses Artificial Intelligence.

TEXT BOOKS:
1. S. Russell and P. Norvig, Artificial Intelligence: A Modern Approach, Prentice Hall, Third Edition,
2009.

Curriculum and Syllabus | B.E. Computer Science and Engineering | R2017 Page 62