Artificial Intelligence Detailed Syllabus for B.Tech third year second sem is covered here. This gives the details about credits, number of hours and other details along with reference books for the course.
The detailed syllabus for Artificial Intelligence B.Tech 2016-2017 (R16) third year second sem is as follows.
B.Tech. III Year II Sem. L/T/P/C
Course Code:CS613PE 3/0/0/3
Course Objectives:
- To learn the difference between optimal reasoning vs human like reasoning
- To understand the notions of state space representation, exhaustive search, heuristic search along with the time and space complexities
- To learn different knowledge representation techniques
- To understand the applications of AI: namely Game Playing, Theorem Proving, Expert Systems, Machine Learning and Natural Language Processing
Course Outcomes:
- Possess the ability to formulate an efficient problem space for a problem expressed in English.
- Possess the ability to select a search algorithm for a problem and characterize its time and space complexities.
- Possess the skill for representing knowledge using the appropriate technique
- Possess the ability to apply AI techniques to solve problems of Game Playing, Expert Systems, Machine Learning and Natural Language Processing
UNIT – I: Introduction, History, Intelligent Systems, Foundations of AI, Sub areas of AI, Applications. Problem Solving – State-Space Search and Control Strategies: Introduction, General Problem Solving, Characteristics of Problem, Exhaustive Searches, Heuristic Search Techniques, Iterative-Deepening A*, Constraint Satisfaction. Game Playing, Bounded Look-ahead Strategy and use of Evaluation Functions, Alpha-Beta Pruning
UNIT – II: Logic Concepts and Logic Programming: Introduction, Propositional Calculus, Propositional Logic, Natural Deduction System, Axiomatic System, Semantic Tableau System in Propositional Logic, Resolution Refutation in Propositional Logic, Predicate Logic, Logic Programming. Knowledge Representation: Introduction, Approaches to Knowledge Representation, Knowledge Representation using Semantic Network, Extended Semantic Networks for KR, Knowledge Representation using Frames.
UNIT – III: Expert System and Applications: Introduction, Phases in Building Expert Systems, Expert
System Architecture, Expert Systems Vs Traditional Systems, Truth Maintenance Systems, Application of Expert Systems, List of Shells and Tools. Uncertainty Measure – Probability Theory: Introduction, Probability Theory, Bayesian Belief Networks, Certainty Factor Theory, Dempster-Shafer Theory.
TEXT BOOKS:
- Saroj Kaushik. Artificial Intelligence. Cengage Learning. 2011
- Russell, Norvig: Artificial intelligence, A Modern Approach, Pearson Education, Second Edition. 2004
REFERENCE BOOK:
- Rich, Knight, Nair: Artificial intelligence, Tata McGraw Hill, Third Edition 2009.
- Introduction to Artificial Intelligence by Eugene Charniak, Pearson.
- Introduction to Artificial Intelligence and expert systems Dan W.Patterson. PHI.
- Artificial Intelligence by George Fluger rearson fifth edition.
For all other B.Tech 3rd Year 2nd Sem syllabus go to JNTUH B.Tech Computer Science and Engineering 3rd Year 2nd Sem Course Structure for (R16) Batch.
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