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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.
01
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.
02
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
01
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.
03
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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