A recursive-reflexive, adaptive logic simulation framework that models a self-consistent, contradiction-aware reasoning system. Inspired by cognitive architectures and meta-logic principles.
- Self-consistent logic generation
- Meta and meta-meta abstraction layers
- Trust-weighted connections (synaptic-like)
- Contradiction detection and pruning
- Goal-driven reinforcement mechanism
- CSV export of logic graph for external analysis
- Graph visualization with dynamic coloring and weights
| File | Description |
|---|---|
reflexive_logic_model_with_CSV_export.py |
Core simulation model with logic cycles and CSV export |
load_logic_graph_from_csv.py |
Loader and visualizer for CSV-based logic graphs |
nodes.csv (generated) |
Exported list of all logic nodes (id, type, value) |
edges.csv (generated) |
Exported connections between nodes with weight values |
reflexive_logic_nodes_ORIGINAL.csv |
Original base logic nodes |
reflexive_logic_edges_ORIGINAL.csv |
Original base connections |
-
Run the main model:
python reflexive_logic_model_with_CSV_export.py
This generates:
nodes.csvedges.csv
-
Visualize the logic graph:
python load_logic_graph_from_csv.py
- Sky Blue → Input nodes
- Light Coral → Meta nodes (first-order consistency checks)
- Gold → Meta-meta nodes (higher-order patterns)
Edge thickness reflects trust weight (stronger = more reliable relation).
- Contradictions are not errors, but signals for restructuring.
- Goal bias promotes or demotes trust in reasoning pathways.
- Self-reinforcing patterns evolve into stable axioms.
- Reflexivity enables logic rules to be altered by experience.
This project is licensed under the Creative Commons Attribution-NonCommercial 4.0 International License.
You are free to share and adapt the code for personal, academic, or non-commercial use, provided that you:
- Attribute the original author
- Link back to this repository
- Indicate any changes made
Use of this codebase in commercial applications or for-profit systems requires explicit permission and licensing.
For licensing requests or custom collaboration, contact:
Tim Isabella GitHub Profile https://www.linkedin.com/in/timisabella
Tim Isabella GitHub Profile https://www.linkedin.com/in/timisabella