Teaching Plan AI July 2011

8/6/2019 Teaching Plan AI July 2011

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TEACHING PLAN

CS 502: Artificial IntelligenceOdd Semester, 2011-12; Instructor: Dr. Suraiya Jabin, DCS-FNS-JMI

A. Course Objective: To introduce the theoretical and computational techniques inArtificial Intelligence. This course covers the issues and techniques involved inthe creation of computer programs that engage in intelligent behavior. Thefollowing are among the topics that we will cover: State-Space Search, GamePlaying, Knowledge Representation, Reasoning and Machine Learning (GA andANN).

B. Course Scope: Artificial Intelligence (AI) is one of the oldest disciplines incomputer science. A primary goal of AI is to build intelligent entities. This courseis structured to give an overview of the area, as well as provide necessary depthto those fundamental techniques. We will keep investigating as what it means tobe intelligent throughout the course. We will also try to attain an understanding

on the contributions AI has made to the field of computer science. By the end ofthe course, you should have a general knowledge of the field of AI. You shouldbe able to recognize when AI techniques are necessary, apply standard AItechniques to solve problems. You should also be able to evaluate newtechniques you encounter. This course presents artificial intelligence as acoherent body of ideas and methods to acquaint the student with the classicprograms in the field and their underlying theory. Students will explore thisthrough problem-solving paradigms, search, logic and theorem proving, languageand neural network processing, and learning.

C. Delivery Schedule: This course is to be delivered with 3 lectures, 1 tutorial and 4lab periods weekly, in the present set-up in the department.

D. Lab Assignments: Learning AI is never a theoretical exercise, it is augmented

by programs that actually exhibit it e.g. PROLOG. PROLOG is especially wellsuited for problems that involve structured objects, and relations between them.PROLOG will be used to solve problems and theorem proving using searchtechniques and many more. Experiments based on ANN will be done onMATLAB.

E. Assignment Submissions: All the work in this course should be done

independently. Collaborations on home works are not permitted. Cheating and anyother anti intellectual behavior, including giving your work to someone else, will bedealt with severely. There will be weekly homework assignments or at least at theend of each unit. The assignments are due at the beginning of the class on the dayspecified on the assignment. In general, no extensions will be granted.

F.Evaluation: Copying is strictly discouraged but not the cooperation. Evaluationshall be attempted to objective, and may appropriate with test marks.

G. Resources: Apart from the above mentioned texts, huge online resources are

available on AI.

H. Syllabus: Syllabus for all the evaluation purposes comprises of 'Topics Taught +

Prescribed Reading + Suggested Activities, the details of topics are prescribedoverleaf.

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Unit Activities1 Reading: Ch-1 (T) AI History and Applications:

Exercises: T{1.1, 1.2, 1.3, 1.7, 1.9, 1.10, 1.11, 1.12, 1.13 }

2Reading: Ch-3, 4, 6 (T); Ch-2, 3, 12, 23 (R1) Problem Solving by SearchExercises: T{ch 3 (3.1, 3.2, 3.4, 3.5, 3.7, 3.8, 3.9),

ch 4 (4.1, 4.2, 4.3, 4.5, 4.8, 4.9, 4.10, 4.11)},

R1 { ch 3(1-11), ch 23 (1-3)}

3 Reading: Ch-7, 8, 9 (T)Knowledge Representation using LOGIC

Exercises: T {9.1 9.21 }

4 Reading: Ch-1 to 12 (R4) PROLOG

Exercises: R4 {1 to 12}

5 Reading: Ch-18 (R1); Ch- 1 to 5 (R2); Ch-4 (R3) Connectionist Models/ANN

Exercises: R1{ch 18 (1-5)}

R2 { ch 5 (5-10) }

6 Reading: Ch-15, 21 (R1) Perception and Action, NLP

Exercises: R1 { ch 21 (1-4), ch 15 (1-20) }

7 Reading: Ch-20 (R1) Expert Systems;

ch 9-10 (R1)-Semantic Nets, Frames, Conceptual Dependency, Scripts, CYC

Exercises: R1{ch 20 (1-2)}

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Syllabus:

Unit Topics Weeks

1 AI History and Applications: Defining AI: Acting Humanly (Turing Test Approach), Thinking

Humanly (Cognitive Modeling Approach), Thinking Rationally (laws of thought approach), Acting

Rationally (Rational Agent Approach); Foundations of Artificial Intelligence; History of AI, AI

techniques, Expert Systems.

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2 Problem Solving by Search: Defining the Problem as a State Space Search; Uninformed

Search Strategies: Breadth-first Search, Depth-first Search, Depth-limited Search, Iterative

Deepening depth-first search, Comparing uninformed search strategies; Heuristic Search

Techniques: Hill Climbing, Simulated Annealing, Best First Search: OR Graphs, Heuristic

Functions, A* Algorithm, AND-OR Graphs, Genetic Algorithm and its Applications; Adversarial

Search: Zero-sum perfect information Games, Optimal Decisions and Strategies in Games,

Minimax Algorithm, Alpha-beta Pruning.

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3 Knowledge Representation: Representations and mappings, Approaches to Knowledge

Representation, Procedural versus Declarative Knowledge; Predicate Logic: Representing

Simple facts, Instance and Isa relationships in Logic, Proposition versus Predicate Logic,

Computable Functions and Predicates, Rules of Inferences and Resolution, Forward versus

Backward Reasoning, Logic Programming and Horn Clauses; Introduction to PROLOG

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4 AI Programming Language (PROLOG): Introduction, How Prolog Works, Backtracking, CUT

and FAIL operators, Built-in Goals, Lists, Search in Prolog; BFS, DFS, Best First Search.03

5 Connectionist Models/ANN: Foundations for Connectionist Networks, Biological Inspiration;

Different Architectures and Output Functions: Feedforward, Feedback, Recurrent Networks,

Step, Sigmoid and Sigmoid Function; Different Models: MacCulloch and Pitts Model, Hopfield

Model, Boltzmann Machines and Energy Computations, Backpropagation Model; Learning

Problems and Issues: Perceptron Learning, Backpropagation Learning, Attractor Networks or

Memories.

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6 Perception and Action: Real-time Search; NLP, Perception: Vision, Speech Recognition;

Action: Navigation, Manipulation; Robotics: Robot Architectures.01

7 Misc. Topics: Knowledge Representation Methods: Semantic Nets, Frames, Conceptual

Dependency, Scripts, CYC etc.01

Text Resources:

T Stuart Russel and Peter Norvig: Artificial IntelligenceA Modern Approach, 2nd Ed. Pearson Education,

ISBN: 0-13-790395-2

R1 Elaine Rich, Kevin Knight and B. Nair: Artificial Intelligence, 2009, Tata McGraw Hill, 3rd Ed, ISBN-10: 0-

07-008770-9

R2 B. Yegna Narayana: Artificial Neural Network, EEE, PHI, 2001, ISBN 81-203-1253-8

R3 Tom M. Mitchell: Machine Learning, McGraw-Hill, International Edition 1997, ISBN 0-07-115467-1

R4 Ivan Bratko: PROLOG Programming, 3rd Ed., 2001, Pearson Education, ISBN: 81-7808-257-8

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Teaching Plan AI July 2011