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Acknowledgement
 
The instructor would like to thank the following students who, between them, reviewed all the video lectures - Abhik Mondal, Arjun Bharadwaj, Ashika Naidu, Ditty Mathew, Jyothis V, Parth Joshi, Savitha Sam Abraham, Shailend Chand, Shashank Shekhar, Shubhranshu Shekhar, Sowmya S Sundaram, Viswajith V. The instructor would also like to thank Mr. Kannan Krishnamurthy and his team for seamlessly recording and editing the live lectures during the autumn semester of 2013.

Untitled Document
 

Introduction: Overview and Historical Perspective, Turing test, Physical Symbol Systems and the scope of Symbolic AI, Agents.


State Space Search: Depth First Search, Breadth First Search, DFID.


Heuristic Search: Best First Search, Hill Climbing, Beam Search, Tabu Search.


Randomized Search:  Simulated Annealing, Genetic Algorithms, Ant Colony Optimization.


Finding Optimal Paths: Branch and Bound, A*, IDA*, Divide and Conquer approaches, Beam Stack Search.


Problem Decomposition: Goal Trees, AO*, Rule Based Systems, Rete Net.


Game Playing: Minimax Algorithm, AlphaBeta Algorithm, SSS*.


Planning and Constraint Satisfaction: Domains, Forward and Backward Search, Goal Stack Planning, Plan Space Planning, Graphplan, Constraint Propagation.


Logic and Inferences: Propositional Logic, First Order Logic, Soundness and Completeness, Forward and Backward chaining.

Lecture

Topic

Chapter in text

1

Introduction

1

2

Introduction

1

3

Introduction

1

4

Introduction

1

5

Introduction

1

6

State space search

2

7

State space search

2

8

DFS - DFID

2

9

Heuristic Search

3

10

Hill Climbing

3

11

Solution space search , Beam search

3

12

TSP greedy methods

4

13

Tabu search

3

14

Randomized: Simulated Annealing

4

15

Genetic Algorithms (GA)

4

16

GAs continued

4

17

GAs for TSP, Ant colony optimization

4

18

Branch & Bound

5

19

A*

5

20

A* admissibility

5

21

A* - Monotone condition, IDA*

5

22

RBFS, Sequence alignment

5

23

Pruning - DCFS, SMGS, BSS, DCBSS

5

24

Problem Decomposition

6

25

AO*

6

26

Game playing

8

27

Game playing - Minimax

8

28

Game playing - AlphaBeta

8

29

Game Playing - SSS*

8

30

Rule Based Systems

6

31

Inference Engines

6

32

Rete Algorithm

6

33

Planning

7

34

Planning FSSP, BSSP

7

35

Goal Stack Planning. Sussman's Anomaly

7

36

Non-linear planning

7

37

Plan Space Planning

7

38

GraphPlan

10

39

Constraint Satisfaction Problems (CSP)

9

40

CSP, AC-1

9

41

Knowledge Based Systems

11

42

Knowledge Based Systems

12

43

Propositional Logic

12

44

Resolution refutation for PL

12

45

First Order Logic (FOL)

12

46

Reasoning in FOL

12

47

Backward Chaining

12

48

Resolution method for FOL

12


None.

Some exposure to algorithms and programming.


Text Book

  1. Deepak Khemani. A First Course in Artificial Intelligence, McGraw Hill Education (India), 2013.

Reference Books

  1. Stefan Edelkamp and Stefan Schroedl. Heuristic Search: Theory and Applications, Morgan Kaufmann, 2011.
  2. John Haugeland, Artificial Intelligence: The Very Idea, A Bradford Book, The MIT Press, 1985.
  3. Pamela McCorduck, Machines Who Think: A Personal Inquiry into the History and Prospects of Artificial Intelligence, A K Peters/CRC Press; 2 edition, 2004.
  4. Zbigniew Michalewicz and David B. Fogel. How to Solve It: Modern Heuristics. Springer; 2nd edition, 2004.
  5. Judea Pearl. Heuristics: Intelligent Search Strategies for Computer Problem Solving, Addison-Wesley, 1984.
  6. Elaine Rich and Kevin Knight. Artificial Intelligence, Tata McGraw Hill, 1991.
  7. Stuart Russell and Peter Norvig. Artificial Intelligence: A Modern Approach, 3rd Edition, Prentice Hall, 2009.
  8. Eugene Charniak, Drew McDermott. Introduction to Artificial Intelligence, Addison-Wesley, 1985.
  9. Patrick Henry Winston. Artificial Intelligence, Addison-Wesley, 1992.


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