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03 - Semantic Networks

Semantic networks are a knowledge representation scheme.

This lesson will cover the following topics:

  • Knowlege representations
  • Semantic networks
  • Problem-solving with semantic networks
  • Represent & Reason

Representation

In each knowledge representation, there is a language, and that language has a vocabulary. In addition, the representation contains some content (or knowledge).

Example: Newton's 2nd Law of Motion

$$ F = ma $$ Force is equal to mass times acceleration

Introduction to Semantic Networks

How to represent Raven’s Progressive Matrices using a semantic network. State A, and state B.

  1. Label all objects (x is a circle, y is the diamond, z is the black dot), and reference them as nodes
  2. Represent the relationships between nodes, in both states (frames), both A and B.
  3. Represent the transformation between the nodes between states, A and B.

Structure of Semantic Networks

  • 1. Lexically: nodes
  • 2. Structurally: directional links
  • 3. Semantically: application-specific labels

Characteristics of Good Representations

  • Make relationships explicit
  • exposese natural contraints
  • bring objects and relations together
  • exclude extraneous details
  • transparent, concise, complete, fast, computable

Guards and Prisoners Problem

Description

  • Three guard and three prisoners must cross river.
  • Boat may take only one or two people at a time.
  • Prisoners may never outnumber guards on either time (thought prisoner may be alone on either coast).

Modeling using Semantic Nework

Lexicon: Consider each node to be a unique state, represented by: - number of prisoners and guards on left side - number of prisoners and guards on right side - side that boat is on.

Structure:

Semantic:

Inference about State Transitions?

Which transitions (e.g. moves) between states are both legal AND productive? Represent total possible states given transformations possible: