This walkthrough builds on the initial Dog Encounter scenario and explores how new experiences reinforce or alter prior interpretations.

Initially, the system associates “Dog” with “Danger” through stories or past warnings. This understanding is stored in a long-term Understanding Node that flags the link as NonExist (a danger to avoid).

Later, a real-world encounter with a friendly dog (e.g., licking, tail wagging) is processed by the system. A new Understanding Node is created—this time linking “Dog” to “Lick” or “Comfort” with a flag of Exist.

Through this scenario, we illustrate:

• How PET handles conflicting interpretations via separate Understanding Nodes.
• The role of experience in gradually shifting neural net weighting.
• The process by which future predictions (short-term graph DB) adapt to new data.

This is a key mechanism for how PET systems evolve their understanding over time while preserving past context.