Artificial Intelligence Syllabus - BSc 6th Semester

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

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.