Boundary Maintenance Intelligence

A novel framework proposing that intelligence is fundamentally about maintaining boundaries (Markov blankets) between regions with different computational dynamics, rather than maximizing rewards.

Core Thesis

Traditional AI defines agents through reward maximization. This project explores an alternative view: agents are entities that maintain boundaries between regions with different dynamics. This thermodynamic perspective suggests that:

1. Survival = Boundary Maintenance: Agents persist by maintaining their Markov blankets
2. Intelligence = Boundary Discovery: Smart agents find and exploit natural boundaries
3. Learning = Boundary Refinement: Improvement comes from better boundary models

Key Components

boundary_world.py

The main simulation environment with four regions exhibiting different dynamics:

• Additive dynamics (top-left)
• Multiplicative dynamics (top-right)
• Oscillatory dynamics (bottom-left)
• Diffusion dynamics (bottom-right)

Agents must discover and maintain boundaries between these regions to survive.

boundary_agent.py

Implementation of agents that detect and maintain boundaries through gradient detection and navigation strategies.

Papers & Documentation

• boundary_maintenance_paper.tex/pdf - Full theoretical paper
• boundary_maintenance_paper.md - Markdown version
• Visualization results showing emergent behavior

Running the Simulation

# Run the boundary world simulation
python boundary_world.py

# The simulation will:
# 1. Create a world with different dynamic regions
# 2. Spawn boundary-maintaining agents
# 3. Show how agents discover and patrol boundaries
# 4. Demonstrate emergence of intelligent behavior

Theoretical Foundation

This framework connects to:

• Free Energy Principle: Minimizing surprise through boundary maintenance
• Autopoiesis: Self-creation through boundary preservation
• Thermodynamics: Intelligence as entropy management at boundaries
• Information Theory: Boundaries as information bottlenecks

Key Insights

1. No Explicit Rewards Needed: Agents behave intelligently purely through boundary maintenance
2. Emergence from Physics: Complex behavior emerges from simple thermodynamic principles
3. Unifies Life and Intelligence: Both phenomena reduce to boundary maintenance at different scales

Relationship to Sequential Decision Making

Unlike traditional sequential decision frameworks (MDPs, expectimax, RL) which assume:

• Explicit reward functions
• Action-value mappings
• Policy optimization

This framework proposes that intelligent behavior emerges from:

• Maintaining separations between different dynamics
• Minimizing mixing across boundaries
• Preserving computational coherence

Future Directions

• Multi-scale boundary hierarchies
• Learning boundary representations
• Connection to consciousness as self-boundary maintenance
• Applications to artificial life and emergent intelligence

Citation

If you use this framework, please cite:

Boundary Maintenance as the Foundation of Intelligence
[Your Name], 2024

License

MIT