locsec/cognitive
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cognitive/knowledge_base/BioFirm/active_inference_connections.md
Daniel Ari Friedman d30a2cc79e BioFirm
2025-02-11 09:04:09 -08:00

9.4 KiB
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BioFirm_Active_Inference_Integration

Overview

The BioFirm framework implements a specialized application of the cognitive/free_energy_principle and cognitive/active_inference for bioregional stewardship. This document outlines the key theoretical and practical connections between these frameworks.

Core Theoretical Connections

1. cognitive/active_inference#Markov_Blankets in BioFirm

2. cognitive/free_energy_principle Application

3. cognitive/predictive_processing

Implementation Framework

1. cognitive/hierarchical_processing

class HierarchicalProcessor:
    """Implements hierarchical active inference processing"""
    def __init__(self, scales: List[str]):
        self.scales = scales
        self.processors = {
            scale: ScaleProcessor(scale) for scale in scales
        }
        self.couplings = self._initialize_couplings()
        
    def process_hierarchy(self, observations: Dict[str, np.ndarray]):
        """Process observations across hierarchical levels"""
        beliefs = {}
        for scale in self.scales:
            beliefs[scale] = self.processors[scale].update_beliefs(
                observations[scale],
                self._get_messages(scale, beliefs)
            )
        return beliefs

2. cognitive/belief_propagation

class BeliefPropagator:
    """Implements belief propagation for active inference"""
    def __init__(self, network: nx.Graph):
        self.network = network
        self.messages = defaultdict(dict)
        
    def propagate_beliefs(self, 
                         initial_beliefs: Dict[str, np.ndarray],
                         max_iterations: int = 100):
        """Propagate beliefs through network"""
        for _ in range(max_iterations):
            self._update_messages()
            self._update_beliefs()
            if self._check_convergence():
                break

3. cognitive/adaptive_control

class AdaptiveController:
    """Implements adaptive control for active inference"""
    def __init__(self, 
                 control_params: Dict[str, Any],
                 learning_rate: float = 0.01):
        self.params = control_params
        self.learning_rate = learning_rate
        
    def adapt_control(self, 
                     performance: PerformanceMetrics,
                     context: SystemContext):
        """Adapt control parameters based on performance"""
        gradient = self._compute_gradient(performance)
        self._update_params(gradient)
        self._store_adaptation(context)

Mathematical Framework

1. mathematics/variational_free_energy

The variational free energy is defined as:

F = E_q[ln q(s) - ln p(s,o)]

where:

2. mathematics/expected_free_energy

The expected free energy for policy selection:

G = E_q[ln q(s') - ln p(s',o')]

where:

3. mathematics/policy_selection

Optimal policy selection through minimization:

π* = argmin_π G(π)

where:

Integration Patterns

1. systems/cross_scale_integration

class CrossScaleIntegrator:
    """Manages integration across scales"""
    def __init__(self, scales: List[str]):
        self.scales = scales
        self.integrators = {
            scale: ScaleIntegrator(scale) for scale in scales
        }
        
    def integrate_scales(self, 
                        states: Dict[str, BioregionalState],
                        couplings: Dict[Tuple[str, str], float]):
        """Integrate states across scales"""
        integrated_states = {}
        for scale in self.scales:
            integrated_states[scale] = self.integrators[scale].integrate(
                states[scale],
                self._get_coupled_states(scale, states, couplings)
            )
        return integrated_states

2. systems/temporal_integration

class TemporalIntegrator:
    """Manages temporal integration of states"""
    def __init__(self, 
                 temporal_horizon: int,
                 integration_method: str = 'euler'):
        self.horizon = temporal_horizon
        self.method = integration_method
        
    def integrate_trajectory(self,
                           initial_state: BioregionalState,
                           dynamics: SystemDynamics) -> List[BioregionalState]:
        """Integrate system trajectory through time"""
        trajectory = [initial_state]
        for t in range(self.horizon):
            next_state = self._step_forward(
                trajectory[-1],
                dynamics
            )
            trajectory.append(next_state)
        return trajectory

3. systems/domain_integration

class DomainIntegrator:
    """Manages integration across domains"""
    def __init__(self, domains: List[str]):
        self.domains = domains
        self.couplings = self._initialize_couplings()
        
    def integrate_domains(self,
                         domain_states: Dict[str, np.ndarray]) -> BioregionalState:
        """Integrate states across domains"""
        integrated_state = BioregionalState()
        for domain in self.domains:
            integrated_state = self._update_state(
                integrated_state,
                domain,
                domain_states[domain]
            )
        return integrated_state

Extensions and Future Directions

1. cognitive/meta_learning

2. systems/resilience_patterns

3. cognitive/collective_intelligence

See Also