Knowledge Engineering for BE Anna University R21CBCS (V, VI(Vertical VII - CSE / IT / CSE(AI&ML), Vertical IV - CS&BS, Vertical I - AI&DS - CCS350))  (Paperback, Tanmayi R. Nagale)

Syllabus Knowledge Engineering - (CCS350) UNIT I REASONING UNDER UNCERTAINTY Introduction - Abductive reasoning - Probabilistic reasoning : Enumerative Probabilities - Subjective Bayesian view - Belief Functions - Baconian Probability - Fuzzy Probability - Uncertainty methods - Evidence-based reasoning - Intelligent Agent - Mixed-Initiative Reasoning - Knowledge Engineering. (Chapter - 1) UNIT II METHODOLOGY AND MODELING Conventional Design and Development - Development tools and Reusable Ontologies - Agent Design and Development using Learning Technology - Problem Solving through Analysis and Synthesis - Inquiry-driven Analysis and Synthesis - Evidence-based Assessment - Believability Assessment - Drill-Down Analysis, Assumption-based Reasoning, and What-If Scenarios. (Chapter - 2) UNIT III ONTOLOGIES - DESIGN AND DEVELOPMENT Concepts and Instances - Generalization Hierarchies - Object Features - Defining Features - Representation - Transitivity - Inheritance - Concepts as Feature Values - Ontology Matching. Design and Development Methodologies - Steps in Ontology Development - Domain Understanding and Concept Elicitation - Modelling-based Ontology Specification. (Chapter - 3) UNIT IV REASONIING WITH ONTOLOGIES AND RULES Production System Architecture - Complex Ontology-based Concepts - Reduction and Synthesis rules and the Inference Engine - Evidence-based hypothesis analysis - Rule and Ontology Matching - Partially Learned Knowledge - Reasoning with Partially Learned Knowledge. (Chapter - 4) UNIT V LEARNING AND RULE LEARNING Machine Learning - Concepts - Generalization and Specialization Rules - Types - Formal definition of Generalization. Modelling, Learning and Problem Solving - Rule learning and Refinement - Overview - Rule Generation and Analysis - Hypothesis Learning. (Chapter - 5)