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UNIT – I

Scope of AI: Games, theorem proving, natural language processing, vision and speech processing, robotics, expert systems, AI techniques-search knowledge, abstraction.

Problem Solving (Blind): State space search; production systems, search space control; depth-first, breadth-first search.

Heuristic Based Search: Heuristic search, Hill climbing, best-first search, branch and bound, Problem Reduction, Constraint Satisfaction End, Means-End Analysis.

 

UNIT – II

Game Playing: Game Tree, Minimax Algorithm, Alpha Beta Cutoff, Modified Minimax Algorithm, Horizon Effect, Futility Cut-off.

Knowledge Representation: Predicate Logic: Unificatioin, Modus Ponens, Modus Tolens, Resolution in Predicate Logic, Conflict Resolution Forward Chaining, Backward Chaining, Declarative and Procedural Representation, Rule based Systems.

Structured Knowledge Representation: Semantic Nets: Slots, exceptions and default frames, conceptual dependency, scripts.                                                        

 

UNIT – III

Handling Uncertainty: Non-Monotonic Reasoning, Probabilistic reasoning, use of certainty factors, fuzzy logic.

Natural Language Processing: Introduction, Syntactic Processing, Semantic Processing, Pragmatic Processing.                                                                        

UNIT – IV

Learning: Concept of learning, learning automation, genetic algorithm, learning by inductions, neural nets.

Expert Systems: Need and justification for expert systems, knowledge acquisition, Case Studies: MYCIN, RI.                                                                                  

 

TEXT BOOKS:

1.         E. Rich and K. Knight, “Artificial Intelligence”, TMH, 2^nd Ed., 1992.

2.         N. J. Nilsson, “Principles of AI”, Narosa Publ. House, 1990.

3.         M. N. Hoda, “Foundation Course in Artificial Intelligence”, Vikas Pub., 2004.

REFERENCES BOOKS:

1.         P. H. Winston, "Artificial Intelligence", Pearson Education, 3rd Edition, 2002.

2.         D. W. Patterson, “Introduction to AI and Expert Systems”, PHI, 1992.

3.         R. J. Schalkoff, “Artificial Intelligence – An Engineering Approach”, McGraw Hill Int. Ed. Singapore, 1992.

4.         M. Sasikumar, S. Ramani, “Rule Based Expert Systems”, Narosa Publishing House, 1994.

5.         Tim Johns, “Artificial Intelligence, Application Programming”, Wiley Dreamtech, 2005.

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Artificial Intelligence Teacher Coaching Tuition Tutor Tutoring Tutors Tution Online Tutor Online Tutoring Private Teacher Private Tuition Tutions Private Tutor Tuitions Tutoring Services Tutoring Service Home Tutor Tuition Teacher Private Tutoring Home

Online | Offline Tuitions for Artificial Intelligence

Visit http://www.learninggeeks.info for Registration.

Call us @ 9718322472, 9810333483

Mail us for any queries: learninggeeks@gmail.com

UNIT – I

Scope of AI: Games, theorem proving, natural language processing, vision and speech processing, robotics, expert systems, AI techniques-search knowledge, abstraction.

Problem Solving (Blind): State space search; production systems, search space control; depth-first, breadth-first search.

Heuristic Based Search: Heuristic search, Hill climbing, best-first search, branch and bound, Problem Reduction, Constraint Satisfaction End, Means-End Analysis.

 

UNIT – II

Game Playing: Game Tree, Minimax Algorithm, Alpha Beta Cutoff, Modified Minimax Algorithm, Horizon Effect, Futility Cut-off.

Knowledge Representation: Predicate Logic: Unificatioin, Modus Ponens, Modus Tolens, Resolution in Predicate Logic, Conflict Resolution Forward Chaining, Backward Chaining, Declarative and Procedural Representation, Rule based Systems.

Structured Knowledge Representation: Semantic Nets: Slots, exceptions and default frames, conceptual dependency, scripts.                                                        

 

UNIT – III

Handling Uncertainty: Non-Monotonic Reasoning, Probabilistic reasoning, use of certainty factors, fuzzy logic.

Natural Language Processing: Introduction, Syntactic Processing, Semantic Processing, Pragmatic Processing.                                                                        

UNIT – IV

Learning: Concept of learning, learning automation, genetic algorithm, learning by inductions, neural nets.

Expert Systems: Need and justification for expert systems, knowledge acquisition, Case Studies: MYCIN, RI.                                                                                  

 

TEXT BOOKS:

1.         E. Rich and K. Knight, “Artificial Intelligence”, TMH, 2^nd Ed., 1992.

2.         N. J. Nilsson, “Principles of AI”, Narosa Publ. House, 1990.

3.         M. N. Hoda, “Foundation Course in Artificial Intelligence”, Vikas Pub., 2004.

REFERENCES BOOKS:

1.         P. H. Winston, "Artificial Intelligence", Pearson Education, 3rd Edition, 2002.

2.         D. W. Patterson, “Introduction to AI and Expert Systems”, PHI, 1992.

3.         R. J. Schalkoff, “Artificial Intelligence – An Engineering Approach”, McGraw Hill Int. Ed. Singapore, 1992.

4.         M. Sasikumar, S. Ramani, “Rule Based Expert Systems”, Narosa Publishing House, 1994.

5.         Tim Johns, “Artificial Intelligence, Application Programming”, Wiley Dreamtech, 2005.

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Artificial Intelligence tuitions, AI tutors, Tutions for Artificial Intelligence, Online Tuitions, Home tutions

Online | Offline Tuitions for Artificial Intelligence.

Visit http://www.learninggeeks.info for Registration.

Call us @ 9718322472, 9810333483

Mail us for any queries: learninggeeks@gmail.com

Overview of A.I:  Introduction to AI, Importance of AI, AI and its related field, AI techniques, Criteria for success.

Problems, problem space and search: Defining the problem as a state space search, Production system and its characteristics, Issues in the design of the search problem

Heuristic search techniques :Generate and test, hill climbing, best first search technique, problem reduction, constraint     satisfaction         

 

Knowledge representation:            Definition and importance of knowledge, Knowledge representation, Various approaches used in knowledge representation, Issues in knowledge representation

Using Predicate Logic :Represent ting Simple Facts in logic, Repsenting instances and isa relationship, Computable function and predicate.                                            

 

Natural language processing :Introduction syntactic processing, Semantic processing, Discourse and pragmatic processing

Learning: Introduction learning, Rote learning, Learning by taking advice, Learning in problem solving, Learning from example-induction, Explanation based learning

 

Expert System: Introduction,Repsenting using domain specific knowledge,Expert system shells.

LISP and other AI Programming Language                                                         

 

 

Text Book:

1.         E. Rich and K. Knight,   "Artificial intelligence", TMH, 2nd ed., 1999.

 

Reference:

1.         D.W. Patterson, "Introduction to AI and Expert Systems", PHI, 1999

2.         Nils J Nilsson ,"Artificial Intelligence -A new Synthesis" 2nd Edition (2000), Harcourt Asia Ltd.

 

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