VTU B.TECH Aeronautical-Engineering 2nd year SEMESTER – VI Syllabus For Artificial intelligence& expert systems PDF 2022
February 12, 2022 2022-02-12 22:17VTU B.TECH Aeronautical-Engineering 2nd year SEMESTER – VI Syllabus For Artificial intelligence& expert systems PDF 2022
VTU B.TECH Aeronautical-Engineering 2nd year SEMESTER – VI Syllabus For Artificial intelligence& expert systems PDF 2022
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You will be able to find information about Artificial intelligence& expert systems along with its Course Objectives and Course outcomes and also a list of textbook and reference books in this blog.You will get to learn a lot of new stuff and resolve a lot of questions you may have regarding Artificial intelligence& expert systems after reading this blog. Artificial intelligence& expert systems has 5 units altogether and you will be able to find notes for every unit on the CynoHub app. Artificial intelligence& expert systems can be learnt easily as long as you have a well planned study schedule and practice all the previous question papers, which are also available on the CynoHub app.
All of the Topic and subtopics related to Artificial intelligence& expert systems are mentioned below in detail. If you are having a hard time understanding Artificial intelligence& expert systems or any other Engineering Subject of any semester or year then please watch the video lectures on the official CynoHub app as it has detailed explanations of each and every topic making your engineering experience easy and fun.
Artificial intelligence& expert systems Unit One
Module-1
AI: The AI Problems, The Underlying Assumption, What Is An AI Techniques, The Level Of The Model, Criteria For Success, Some General References, One Final Word.Problems, State Space Search & Heuristic Search Techniques: Defining The Problems As A State Space Search, Production Systems, Production Characteristics, Production System Characteristics, And Issues In The Design Of Search Programs, Additional Problems. Generate-And-Test, Hill Climbing, Best-First Search, Problem Reduction, Constraint Satisfaction, Means-Ends Analysis
Artificial intelligence& expert systems Unit Two
Module-2
Knowledge Representation Issues: Representations And Mappings, Approaches To Knowledge Representation. Using Predicate Logic : Representation Simple Facts In Logic, Representing Instance And Isa Relationships, Computable Functions And Predicates, Resolution Representing Knowledge Using Rules: Procedural Versus Declarative Knowledge, Logic Programming, Forward Versus Backward Reasoning.
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Artificial intelligence& expert systems Unit Three
Module-3
Symbolic Reasoning Under Uncertainty: Introduction To Nonmonotonic Reasoning, Logics For Non-monotonic Reasoning. Turning performance limitations. Drag estimation. Take-off and landing – methods,procedures and data reduction. Statistical Reasoning: Probability And Bays’ Theorem, Certainty Factors And Rule-Base Systems, Bayesian Networks, DempsterShafer Theory, Fuzzy Logic.
Artificial intelligence& expert systems Unit Four
Module-4
Weak Slot-and-Filler Structures : Semantic Nets, Frames. Strong Slot-and-Filler Structures : Conceptual Dependency, Scripts, CYC. Game Playing: Overview, And Example Domain : Overview, MiniMax, Alpha-Beta Cut-off, Refinements, Iterative deepening, The Blocks World, Components Of A Planning System, Goal Stack Planning, NonlinearPlanning Using Constraint Posting, Hierarchical Planning, Reactive Systems, Other Planning Techniques
Artificial intelligence& expert systems Unit Five
Module-5
Understanding: What is understanding? , What makes it hard?, As constraint satisfaction. Natural Language Processing: Introduction, Syntactic Processing, Semantic Analysis, Semantic Analysis,Discourse And Pragmatic Processing, Spell Checking.Connectionist Models : Introduction: Hopfield Network, Learning In Neural Network, Application Of Neural Networks, Recurrent Networks, Distributed Representations, Connectionist AI And Symbolic AI
Artificial intelligence& expert systems Course Objectives
Understand the basic techniques of artificial intelligence. •Understand the Non-monotonic reasoning and statistical reasoning. •Acquire the knowledge on filler structures, and understanding.
Artificial intelligence& expert systems Course Outcomes
At the end of the course the student will be able to:1.CO1: Apply the basic techniques of artificial intelligence. 2.CO2: Distinguish Non-monotonic reasoning and statistical reasoning. 3.CO3: Evaluate the natural language processing and connectionist models.
Artificial intelligence& expert systems Text Books
Artificial Intelligence-Elaine Rich And Kevin Knight
Artificial Intelligence- A Modern Approach-Stuart Russel, Peter Norvig
Artificial intelligence& expert systems Reference Books
Introduction to Prolog Programming-Carl Townsend
PROLOG Programming For Artificial Intelligence-Ivan Bratko( Addison-Wesley)
Programming with PROLOG- Clocksin and Mellish
Scoring Marks in Artificial intelligence& expert systems
Scoring a really good grade in Artificial intelligence& expert systems is a difficult task indeed and CynoHub is here to help!. Please watch the video below and find out how to get 1st rank in your B.tech examinations . This video will also inform students on how to score high grades in Artificial intelligence& expert systems . There are a lot of reasons for getting a bad score in your Artificial intelligence& expert systems exam and this video will help you rectify your mistakes and help you improve your grades.
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