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Lecture 5 - Knowledge Representation and Reasoning for Intelligent Agents (English)

Linkedln-  / sandeep-kumar-singh-phd-75b2517   Source - https://drive.google.com/file/d/1j0iP... Notes - https://docs.google.com/document/d/1s... The source material explains how intelligent agents function by relying on a perception-cognition-action cycle, introducing the PEAS framework to describe key elements of an agent and its environment. It highlights that intelligent behavior requires agents to possess knowledge and the ability to reason with it, contrasting this with simple reactive systems. The text surveys several knowledge representation techniques, including logic-based methods (Propositional Logic, First-Order Logic), graphical and structured approaches (Semantic Networks, Frames, Scripts), and formal conceptualizations (Ontologies) for structuring information. Additionally, it details various reasoning mechanisms such as deductive, inductive, and abductive reasoning that enable agents to derive new conclusions from existing knowledge. Finally, the source introduces AI planning and problem-solving as the process of generating action sequences to reach goals, discussing foundational approaches like state-space search and means-ends analysis.