Artificial Intelligence Syllabus - BSc 6th Semester
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Questions and Answers

What is the primary limitation of simple relational knowledge?

  • It allows for complex inference.
  • It represents information in a hierarchal manner.
  • It incorporates procedural rules.
  • It has little opportunity for inference. (correct)

    • In inheritable knowledge, how are classes organized?

  • In a hierarchy of classes. (correct)
  • In a flat structure without relationships.
  • In a non-hierarchical collection.
  • In a random order based on attributes.

    • How does inferential knowledge help in knowledge representation?

  • It focuses solely on relationships between objects.
  • It provides a direct method for data storage.
  • It allows for the derivation of new facts. (correct)
  • It incorporates procedural guidelines for tasks.

    • Which programming languages are mentioned as usable in procedural knowledge?

    <p>LISP and Prolog.</p> Signup and view all the answers

    What is a characteristic feature of the procedural knowledge approach?

    <p>It uses the If-Then rule to dictate operations.</p> Signup and view all the answers

    What best describes an atomic proposition?

    <p>It can be either true or false and contains a single proposition symbol.</p> Signup and view all the answers

    What is the primary goal of semantic analysis?

    <p>To draw exact meaning from the text.</p> Signup and view all the answers

    Which of the following is an example of a compound proposition?

    <p>It is raining today, and the street is wet.</p> Signup and view all the answers

    What are logical connectives used for in propositional logic?

    <p>To combine atomic propositions into complex propositions.</p> Signup and view all the answers

    Which term describes the relationship between a generic term and instances of that term?

    <p>Hyponymy</p> Signup and view all the answers

    Which of the following statements is a false proposition?

    <p>2+2=5.</p> Signup and view all the answers

    What is the first part of semantic analysis focused on?

    <p>Studying the meaning of individual words.</p> Signup and view all the answers

    Which component of propositional logic encompasses the meanings and rules governing how propositions combine?

    <p>Semantics</p> Signup and view all the answers

    Which of the following best defines homonymy?

    <p>Words with the same spelling or form but different meanings.</p> Signup and view all the answers

    Which aspect of semantic analysis involves the combination of individual words to create sentence meaning?

    <p>Combining semantics</p> Signup and view all the answers

    What distinguishes procedural knowledge from declarative knowledge?

    <p>Procedural knowledge relates to performing tasks, whereas declarative knowledge pertains to facts about the world.</p> Signup and view all the answers

    Which of the following statements best defines a hypothesis?

    <p>A hypothesis is a belief supported by evidence but may still be false.</p> Signup and view all the answers

    What role does the knowledge base play in a Knowledge-Based System (KBS)?

    <p>The knowledge base acts as the repository for storing knowledge.</p> Signup and view all the answers

    What is metaknowledge?

    <p>Knowledge about knowledge and understanding what we know.</p> Signup and view all the answers

    How does a Knowledge-Based System assist human decision-making?

    <p>By capturing knowledge from various sources to solve complex problems.</p> Signup and view all the answers

    Study Notes

    Artificial Intelligence Syllabus

    • The document is study material for a Sixth Semester BSc Computer Science course at the Muslim Association College of Arts and Science, affiliated with the University of Kerala.
    • The syllabus covers Artificial Intelligence (AI), focusing on various aspects of AI, including introductions, defined importance of knowledge, and knowledge-based systems.

    Module I: Overview of Artificial Intelligence

    • Artificial Intelligence (AI) refers to the simulation of human intelligence processes by computer systems. These processes include learning, reasoning, and self-correction. AI encompasses a variety of capabilities such as natural language processing, speech recognition, and decision-making, allowing machines to perform tasks that typically require human intelligence.
    • Importance of AI: Artificial Intelligence (AI) plays a crucial role in modern society, driving advancements across various fields such as healthcare, finance, transportation, and entertainment. Its ability to analyze vast amounts of data, identify patterns, and make predictions has revolutionized industries and improved efficiency. The integration of AI technologies can enhance decision-making processes, improve customer experiences, and create innovative solutions to complex problems.
    • Introduction to knowledge: The concept of knowledge encompasses information, understanding, and skills acquired through experience or education. Knowledge serves as the foundation for learning and innovation, allowing individuals and organizations to navigate challenges effectively and to develop informed strategies in various contexts.
    • Definition and importance of knowledge: Knowledge is defined as justified true belief, encompassing various forms such as procedural, declarative, and contextual knowledge. Its importance lies in its capacity to empower individuals by shaping their thoughts, actions, and interactions. Knowledge fosters critical thinking and enables problem-solving, facilitating personal growth and societal advancement.
    • Knowledge-Based Systems: Knowledge-Based Systems (KBS) leverage artificial intelligence and expert-level knowledge to solve complex problems. These systems utilize stored information to infer conclusions, provide recommendations, or automate decision-making processes. KBS can be beneficial in technical applications such as medical diagnosis, financial forecasting, and intelligent tutoring systems.
    • Representation of knowledge: Effective representation of knowledge is key to enabling machines to understand, process, and utilize information. Techniques such as semantic networks, ontologies, and frames are employed to systematically organize and depict knowledge in a manner that mimics human comprehension, thus enhancing machine reasoning abilities and information retrieval efficiency.
    • Knowledge organization: Organizing knowledge involves structuring information in a coherent manner to facilitate access, retrieval, and application. Taxonomies, classifications, and databases are examples of organizational methods that help categorize knowledge, ensuring that users can efficiently locate relevant information when needed, thus enhancing overall productivity and decision-making capabilities.
    • Knowledge manipulation: Knowledge manipulation refers to the methods used to alter or transform knowledge in various ways, including knowledge creation, updating, and deletion. This process is crucial when dealing with the dynamic nature of information, ensuring that knowledge systems remain relevant, accurate, and effective in their applications.
    • Acquisition of knowledge: Knowledge acquisition is the process through which individuals or systems gather and assimilate information from various sources, including books, experiences, or through interactions with others. Effective acquisition strategies, such as learning algorithms and data mining techniques, are essential for enhancing knowledge-based systems and improving their performance by continuously enriching their knowledge base.

    Module II: Formalized Symbolic Logics

    • Introduction to Propositional Logic
    • Introduction to First-order Predicate Logic (FOPL)
    • Properties of Well-formed formulas (Wffs)
    • Conversion to Clausal Form
    • Inference rules
    • Resolution principle
    • Structured Knowledge:
      • Associative Networks
      • Frame structures
      • Conceptual Dependencies
      • Scripts

    Module III: Search and Control Strategies

    • Preliminary concepts
    • Examples of search problems
    • Uniformed (blind) search
    • Informed search
    • Search in AND-OR graphs
    • Matching techniques
      • Introduction
      • Structures used in matching
      • Measures for matching
      • Partial matching
      • RETE matching algorithm

    Module IV: Natural Language Processing

    • Introduction
    • Overview of linguistics
    • Grammars and languages
    • Basic parsing techniques
    • Semantic analysis and representation structures
    • Natural language generation
    • Natural language systems
    • Expert Systems
      • Introduction
      • Rule-based system architecture
      • Knowledge acquisition and validation
      • Knowledge system building tools

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    Description

    This syllabus outlines the study material for the Sixth Semester BSc Computer Science course focused on Artificial Intelligence at the Muslim Association College of Arts and Science. It covers essential topics such as knowledge-based systems, symbolic logics, and the importance of AI in contemporary applications.

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