🧠 Advanced Pattern Evaluation (Node-Based Schema)

This schema-based example walks through how a PET system interprets a recurring set of patterns using the same initial example and the formal architecture:

• ON — Object Node
• PN — Pattern Node
• UP — Understanding Pattern
• SPN — Sub-Pattern Node
• CUG — Current Understanding Graph
• HUG — Historical Understanding Graph

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🐕 Example Scenario: “Dog Licks Boy in Park”

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Day 1

• Input: Sight engine reports a composite signal of Dog licks boy in park.
• PN1 is generated and linked to 4 SPNs: Dog, Lick, Boy, Park.
• SPNs are converted (or linked) to ONs.
• These ONs are bound by a relationship edge set (EN).
• A new UP1 is formed in the CUG:
  • PN1 + {ON_Dog, ON_Lick, ON_Boy, ON_Park}
  • Trust initialized (100%)
  • Evaluation: UP1 => Exists

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Day 2

• PN2 with Dog licks boy on street is input.
• SPNs extracted: Dog, Lick, Boy, Street
• All match existing ONs except Street (new ON added)
• PN2 evaluated as similar to UP1.
• Reinforces UP1:
  • Trust level increases
  • Edge added for location variance
• No new UP is created. PN2 is archived in HUG, referenced by UP1.

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Day 3

• PN3 with Dog licks boy in park again.
• Full match with UP1 and ON set.
• Reinforcement increases further.
• No contradiction or ambiguity.
• Archived in HUG.

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Day 4 – Contradiction

• PN4: Dog bites boy in car
• ONs: Dog, Bite (new), Boy, Car (new)
• Mapped to new UP2 in CUG:
  • Represents contradiction to UP1
• Recursion Engine activates:
  • Evaluates impact on UP1:
    • Location (car vs. park)
    • Action (bite vs. lick)
  • Partial overlap with ONs (Dog, Boy)
  • Adjusts trust in UP1
  • Lick re-evaluated across UPs and ONs

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🧠 Recap of Node Dynamics

Element	Description
PN	Individual pattern instance
ON	Canonical object/action node
UP	Generalized pattern of understanding
SPN	Extracted sub-nodes from sensor input
EN	Edges that bind ONs in a relational context
CUG	Graph of current trusted UPs
HUG	Archive of past PN evaluations

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🧠 Concept Refinement from Contradictions

“Lick” is now connected across:

• Dog licks boy (affection)
• Tiger licks cub (grooming)
• Dog licks wound (healing)
• Team licks opponent (domination)

From these:

• Lick ON forms higher abstraction:
  • → Affection (exists)
  • → Grooming (exists)
  • → Domination (context-dependent)

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🧭 Summary

• Every PN contributes to memory (HUG) and may refine or challenge UPs (CUG).
• Contradictions lower trust, generate new UPs, or reclassify ON meanings.
• Continuity is recursively evaluated from stable UPs reinforced over time.
• New understanding often emerges from contradictions, not repetition.

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Last updated: 2025-06-12