Journal 2025-07-27

tl;dr Problem-solving is the navigation of scale-free conceptual spaces to achieve intentional state changes, where agents with finite working memory dynamically adjust their viewport through decomposition and abstraction operations until unknown system patterns align with known behavioral templates.

Micro Burn-Out

I wish to mint this phrase. You know exactly what it means without any more explanation. I've been hit by one, was overwhelmed by what I was working on, while also determined to give myself a holiday. So after months of fairly intensive work on code projects, I switched off for a couple of weeks. Now, totally lacking momentum. Ok, figure out where I was trying to do.

Adaptive Problem-Solving through Scale-Free Knowledge Navigation

An approach to problem-solving is to regard it as navigating between different scales of conceptual representation to fit patterns within the constraints of finite working memory. This is the motivation behind the Ragno and ZPT ontologies, and what I've been playing with in the Semem project.

Ragno is an attempt to capture a knowledgebase, independent of domain. That is the corpus. ZPT and attempt to describe a navigation system over that corpus. The controls are : Zoom (abstraction level), Pan (conceptual domain), Tilt (representation perspective) - serving to find the optimal knowledge frame for pattern matching within memory constraints.

Consider :

• Pattern Matching as Navigation - Problem-solving involves finding the right "view" into a knowledge space where unknown patterns match known ones
• Working Memory as Viewport - The agent's finite capacity defines the maximum complexity processable at once (like a camera's field of view)
• Scale-Free Architecture - Concepts exist at multiple abstraction levels simultaneously, allowing zoom in/out navigation Dynamic Chunking - Decomposition/abstraction operations adapt the knowledge representation to fit the working memory constraints
• Dynamic Chunking - Decomposition/abstraction operations adapt the knowledge representation to fit the working memory constraints