cse 402

Lovely Professional University,Punjab

Course No Cours Title Course Planner Lectures Tutorial Practical CreditsCSE402 ARTIFICIAL INTELLIGENCE 13737 :: Manoj Kumar 4 0 0 4

Sr No Jouranls atricles as compulsary readings (specific articles, Complete reference)10 http://www.journals.wspc.com.sg/ijait/ijait.shtml11 http://ceser.res.in/ijai.html

Rich ,Knight, Artificial Intelligence, Tata McGraw Hill, 2009(Third edition)1Text Book:

Other Specific Book:P. H.Winston:, Artificial Intelligence2

D.W.Patterson, Introduction to AI & Expert Systems, Prentice Hall.3

N.J.Nilsson, Principles of Artificial Intelligence, Kaufmann, 19804

Charnmiak & M. Dermalt, Introduction to AI , Addison Wesley, 1985.5

A.J. Gongalez & D.D. Dankel, The Engineering of Knowledge based systems theory & practice, Prentice Hall, 19936

G.F.Lager & W.A. Stubblefield, Artificial Intelligence and the design of Expert System , Benjamin Kummings, 1989.7

W.F. Clocksin & C.S. Mellish, Programming in Prolog, (Third, Revised and extended Edition)8

M. Tim Jones , Artificial Intelligence and application Programming dreamteh press9

Other Reading

Format For Instruction Plan [for Courses with Lectures and Labs

1 Approved for Autumn Session 2011-12

Sr. No. (Web adress) (only if relevant to the courses) Salient Features18 http://www-formal.stanford.edu/jmc/whatisai/ AI know how19 http://inst.cs.berkeley.edu/~cs188/archives.html AI basic concepts20 http://www.csc.liv.ac.uk/_konev/COPM210/ AI basic concepts21 http://ai-depot.com/Intro.html AI depot22 http://en.wikipedia.org/wiki/Applications_of_artificial_intelligen

ceApplications of Artificial Intelligence

23 http://ww3.algorithmdesign.net/handouts/DFS.pdf Depth first search24 http://en.wikipedia.org/wiki/Breadth-first_search Breadth first search25 http://en.wikipedia.org/wiki/Knowledge_representation_and_re

asoningKnowledge Representation

26 http://en.wikipedia.org/wiki/Predicate_logic predicate logic27 http://en.wikipedia.org/wiki/Fuzzy_logic Fuzzy logic28 http://www.doc.ic.ac.uk/~nd/surprise_96/journal/vol4/sbaa/rep

ort.htmlFuzzy Logic

29 http://en.wikipedia.org/wiki/Expert_system Expert System30 http://www.cs.cf.ac.uk/htbin/Dave/AI/ai.pl?AI1/weak_slot.html Weak slot and filler31 http://www.cs.cf.ac.uk/htbin/Dave/AI/ai.pl?AI1/strong_slot.htm Strong slot and filler32 http://nptel.iitm.ac.in/video.php?subjectId=106105079 very good videos on Ai by Intelligent professors

12 http://www-lsi.upc.es/~miquel/aijournals.html13 http://www.jair.org/14 http://www.worldscinet.com/ijait/15 http://www.revue-i3.org/16 http://as.wiley.com/WileyCDA/Section/index.html17 http://www.elsevierdirect.com/brochures/academicpress/

Relevant Websites

Detailed Plan For Lectures Week Number Lecture Number Lecture Topic Chapters/Sections of

Textbook/other reference

Pedagogical tool Demonstration/case study/images/anmation ctc. planned


Part 1Week 1 Lecture 1 Introduction and Overview of Artificial Intelligence ->Reference :1,11.1 of

Ch 1->Reference :4,1.3 of ch-1

Artificial IntelligenceImages

Lecture 2 Intelligent Computer ->Reference :3,ch-5 Intelligent_computer.pptLecture 3 Meaning of AI , Historical foundations ->Reference :1,1.3 &

1.5of ch1AI_meaning.ppt

Lecture 4 Development of logic, Turing test ->Reference :1,1.5of ch1

Turing Test image

Week 2 Lecture 5 Applications of AI & related fields ->Reference :4,1.4 of ch-1

AI_applications.ppt

Lecture 6 Problems, Problem Spaces & Search:Problems & state Space SearchChess Problem, Water Jug Problem

->Reference :1,2.1 of Ch2

Lecture 7 Problem characteristicsProduction system characteristics

->Reference :1,2.3 & 2.4 of Ch2

Lecture 8 Depth first search

Week 3 Lecture 9 Breadth first search ->Reference :1,2.2 of Ch2

Lecture 10 Depth first search with iterative deepening ->Reference :1,7.5 of Ch7

Lecture 11 Design on Search Programs ->Reference :1,2.5 of Ch2

AI_search_programs.ppt

Lecture 12 Heuristic search: - Generate & Test ->Reference :1,3.1of Ch3

Case Study on Heuristicsearch

Week 4 Lecture 13 Hill Climbing (Simple, Steepest-Ascent Hill Climbing, SimulatedAnealing)

->Reference :1,3.2of Ch3

Case Study on Heuristicsearch

Part 2Week 4 Lecture 14 Best First Search ->Reference :1,3.3.1 of

Ch3bfs.ppt

Lecture 15 Problem Reduction (AND OR Graph) ->Reference :1,3.4.1 of Ch3

Lecture 16 Constraint Satisfaction, Means End Analysis ->Reference :1,3.5 & 3.6 of Ch3


Week 5 Lecture 17 Knowledge Representation:General concepts of knowledge

->Reference :4,2.1of Ch2

knowledge_rep.ppt

Lecture 18 Approaches & issues in knowledge representation ->Reference :1,4.2 &4.3 of Ch4

Lecture 19 Propositional logic to represent knowledge ->Reference :1,5.1 of Ch5

Lecture 20 Predicate logic to represent knowledge ->Reference :1,5.1 & 5.2 & 5.3 of Ch5

Week 6 Lecture 21 Resolution ->Reference :1,5.4 of Ch5

1,Algorithm 5.4.1Resolution_ppt

Lecture 22 Natural deduction ->Reference :1,5.5 of Ch5

Lecture 23 Procedural Vs Declarative Knowledge ->Reference :1,6.1 & 6.2 of Ch6

Lecture 24 Logic Programming ->Reference :1,6.2 of Ch6

Week 7 Lecture 25 Forward Vs Backward Reasoning ->Reference :1,6.3 of Ch 6

Lecture 26 Matching & Control Knowledge ->Reference :1,6.4& 6.5 of Ch 6

Lecture 27 Symbolic Reasoning Under uncertainty ->Reference :1,7.1 & 7.2 of Ch7

Lecture 28 Nonmonotonic reasoning ->Reference :1,7.2 of Ch7

nonmonotonic_reason.ppt

MID-TERMPart 3

Week 8 Lecture 29 Statistical Reasoning- Probability & Bayes Theorem ->Reference :1,8.1of Ch 8

Lecture 30 Rule Based Systems Bayesian N/W ->Reference :1,8.2 of Ch8

Lecture 31 Bayesian N/W ->Reference :1,8.3 of Ch8

Lecture 32 Dampster Shafer Theory ->Reference :1,8.4 of Ch 8

Week 9 Lecture 33 Fuzzy Logic ->Reference :1,8.5 of Ch 8

Fuzzy_logic.ppt,

Lecture 34 Overview of weak slot and filler structures ->Reference :1,9.1 of Ch 9


Week 9 Lecture 35 Weak slot and filler structures ->Reference :1,9.2 of Ch 9

Lecture 36 Weak slot and filler structures ->Reference :30 http://www.cs.cf.ac.uk/Dave/AI2/node57.html

Week 10 Lecture 37 Strong slot and filler structures ->Reference :1,10.1 & 10.2 of Ch10

Part 4Week 10 Lecture 38 Strong slot and filler structures ->Reference :31 http://www.cs.cf.ac.uk/D

ave/AI2/node68.html

Lecture 39 Natural Language Processing-Meaning, Syntactic processing

->Reference :1,15.1 & 15.2 of Ch 15

nlp.ppt

Lecture 40 Semantic analysis ->Reference :1,15.3 of Ch15

Week 11 Lecture 41 Discourse & Pragmatic processing ->Reference :1,15.4 of Ch15

Lecture 42 Discourse & Pragmatic processing

Lecture 43 Learning:Meaning, Rote Learning

->Reference :1,17.1 & 17.2 of Ch17->Reference :4,16.1 of Ch 16

Lecture 44 Various Type of Learning ->Reference :1,17.3 &17.4 of Ch17->Reference :4,16.1 & 16.2 & 16.3 of Ch 16

Week 12 Lecture 45 Discovery, Formal Learning Theory ->Reference :1,17.7 17.9 of Ch17

discovery.pptformal_learning.ppt

Lecture 46 Genetic Learning ->Reference :1,17.10 of Ch17

Lecture 47 Expert Systems-IntroductionNeural Net

->Reference :1,20.1to 20.4 of Ch20->Reference :4,15.1 to 15.4 of Ch 15

Lecture 48 Other advanced Topics in AI:Alpha - Beta cutoff search , MinMax Search

->Reference :1,12.1 & 12.2 of Ch12


Spill OverWeek 13 Lecture 49 Neural Net ->Reference :1,17.10

of Ch17Lecture 50 Net and Optimal Search ->Reference :3,ch-6

Details of homework and case studies Homework No. Objective Topic of the Homework Nature of homework

(group/individuals/field work

Evaluation Mode Allottment / submission

WeekTest 1 To evaluate the

basic understanding in AI

Introduction and Overview: Meaning of AI, Historical foundations, Intelligent Computer development of logic, Turing test, Applications of AI, AI & related fields. Problems, Problem Spaces & Search: Problems & state Space Search, Production Systems BFS, DFS & DFS with iterative deepening, Problem & Production System characteristics, Design on Search Programs. Heuristic search Generate & Test, Hill Climbing, Best First Search, Problem reduction, Constraint Satisfaction, Means-End Analysis. Knowledge Representation: General concepts of knowledge, Approaches & issues in knowledge representation, Propositional and predicate logic to represent knowledge, resolution, Natural Deduction.

Individual Answer must be precise as well as not same content of two student

3 / 6

Test 2 To evaluate the understanding of processing knowledge and its way of storage in AI

Knowledge Representation: Procedural Vs Declarative Knowledge, Logic Programming, Forward Vs backward Reasoning, matching & Control Knowledge, symbolic reasoning under uncertainty. Symbolic Reasoning Under uncertainty - Nonmonotonic reasoning, Augmenting a Problem solver. MTE Statistical reasoning - Probability & Bayes Theorem, Rule based Systems, Bayesian N/W, Dampster Shafer theory, Fuzzy logic. Nets and optimal search , Overview of Weak slot and filler structures & Strong slot and filler structures

Individual Answer must be precise as well as no two student should have same answer word by word

7 / 10

Term Paper,Test 1

to evalaute the presentation skill of student on paper on specific topic

All the topics are mentioned below Individual No student should have written same content as well as directly written from somewhere

3 / 9

Scheme for CA:out of 100*


Component Frequency Out Of Each Marks Total MarksTerm Paper,Test 2 3 10 20

Total :- 10 20

* In ENG courses wherever the total exceeds 100, consider x best out of y components of CA, as explained in teacher's guide available on the UMS

List of suggested topics for term paper[at least 15] (Student to spend about 15 hrs on any one specified term paper)

Sr. No. Topic1 Robotics2 AI Research- creating a new form of life3 Representations of artificial intelligence in cinema4 Intelligent machines5 Bioinformatics Vs AI 6 How Big-Bang is related to A7 Artificial Neural Network 8 Fuzzy Expert System9 Real Time Expert System

10 Face Recognition 11 Finger Print Recognition 12 Character Recognition 13 Cybernetics14 Brain Simulation15 Probabilistic methods for uncertain reasoning16 Timeline of artificial Intelligence17 General Intelligence18 Social Intelligence19 AI programming techniques20 Epistemology21 ontology


22 genetic programming23 logical AI24 Science fiction25 Machine Learning26 List of Term papers are mentioned below27 Different specific topics are mentioned below.........28 All the topics are mentioned below


cse 402

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