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Knowledge Representation

Knowledge Representation. Course CS II Lecture 2. Principles of Computer Science needed in AI. Data Structures (knowledge representation, scheme) Algorithms (needed to apply knowledge) Languages (the medium of implementation) Programming Techniques. Knowledge Representation.

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Knowledge Representation

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  1. Knowledge Representation Course CS II Lecture 2

  2. Principles of Computer Science needed in AI • Data Structures (knowledge representation, scheme) • Algorithms (needed to apply knowledge) • Languages (the medium of implementation) • Programming Techniques

  3. Knowledge Representation • It is the representation of information for use in intelligent problem solving. • It allows us to derive new knowledge (inferences, conclusions). • Logic • Semantic networks • Conceptual dependencies • Scripts • Frames • Production Systems

  4. Semantic Networks • Semantic networks are knowledge representation schemes involving nodes and links (arcs or arrows) between nodes. • The nodes represent objects or concepts and the links represent relations between nodes.

  5. Conceptual dependencies • Conceptual dependencies model the semantic structure of natural language. • Conceptual dependency theory (CDT) offers a set of four primitive conceptualizations • CDT offers a set of four primitive conceptualizations from which the world of meaning is built.

  6. Primitive Conceptualization • ACTs actions • PPs objects (picture producers) • AAS modifiers of actions (actions aiders) • PAs modifiers of objects (pictures aiders) (See handout)

  7. Scripts • A script is a structured representation describing a stereotyped sequence of events in a particular context. • Typical and predictable situations (see handout)

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