Knowledge Representation and Reasoning

Course Objectives

The objectives of this course are:

1. Investigate Key Concepts: Explore the fundamental concepts of KR techniques and notations.
2. Model Organizational Knowledge: Use KR as a knowledge engineering approach to model organizational knowledge.
3. Study Ontologies: Introduce ontologies as a KR paradigm and their applications.
4. Understand KR Techniques: Learn various KR processes, knowledge acquisition, and sharing methods using ontologies.

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Course Outcomes

Upon completion of this course, students will be able to:

1. Analyze and Design: Analyze and design knowledge-based systems for computer implementation.
2. Understand Logic-Based Principles: Gain theoretical knowledge about logic-based representation and reasoning.
3. Knowledge Engineering Process: Understand the knowledge-engineering process.
4. Implement Systems: Implement production systems, frames, inheritance systems, and approaches to handle uncertain or incomplete knowledge.

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Knowledge Representation and Reasoning Class Notes Knowledge Representation and Reasoning Assignment Knowledge Representation and Reasoning Assignment 87 KRR Mid 1 Imp QnA KRR Mid 1

Syllabus

UNIT 1 The Key Concepts

• Knowledge, Representation, Reasoning:
  • Definition and importance of knowledge representation and reasoning.
  • Why knowledge representation is critical in AI systems.
  • Role of logic in knowledge representation.
• Logic:
  • Historical background of logic.
  • Representing knowledge in logic.
  • Varieties of logic: Propositional, Predicate, Modal, Fuzzy, etc.
  • Measures of logic: Name, Type, Unity Amidst Diversity.

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UNIT 2 Ontology

• Ontological Categories:
  • Philosophical background of ontology.
  • Top-level categories in ontology.
• Describing Physical Entities:
  • Defining abstractions, sets, collections, types, and categories.
  • Space and time in ontological representations.

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UNIT 3 Knowledge Representations

• Knowledge Engineering:
  • Techniques and methodologies in knowledge engineering.
• Frames, Rules, and Data:
  • Representing structure in frames.
  • Rules and data integration.
• Object-Oriented Systems:
  • Object-oriented approaches to knowledge representation.
• Natural Language Semantics:
  • Levels of representation in natural language processing.

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UNIT 4 Processes

• Times, Events, and Situations:
  • Classification of processes.
  • Procedures, histories, and concurrent processes.
• Computation and Constraint Satisfaction:
  • Computational models for reasoning.
  • Constraint satisfaction problems.
• Contexts:
  • Syntax and semantics of contexts.
  • First-order reasoning and modal reasoning in contexts.
  • Encapsulating objects in contexts.

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UNIT - V: Knowledge Soup

• Vagueness, Uncertainty, Randomness, and Ignorance:
  • Limitations of traditional logic.
  • Introduction to fuzzy logic and nonmonotonic logic.
• Theories, Models, and the World:
  • Bridging theories, models, and real-world applications.
• Semiotics:
  • Role of signs and symbols in knowledge representation.
• Knowledge Acquisition and Sharing:
  • Sharing ontologies and conceptual schemas.
  • Relating different knowledge representations.
  • Tools for knowledge acquisition and language patterns.

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Textbooks

1. “Knowledge Representation: Logical, Philosophical, and Computational Foundations”

   • Author: John F. Sowa
   • Publisher: Thomson Learning

2. “Knowledge Representation and Reasoning”

   • Authors: Ronald J. Brachman, Hector J. Levesque
   • Publisher: Elsevier