Adaptive Neuro-Symbolic Planning for wildfire evacuation logistics networks during mission-critical recovery windows

Adaptive Neuro-Symbolic Planning for wildfire evacuation logistics networks during mission-critical recovery windows

Introduction: The Learning Journey That Sparked This Research

It began with a simulation that failed catastrophically. During my exploration of multi-agent reinforcement learning for disaster response, I was testing a fleet coordination algorithm against synthetic wildfire data from California's 2020 season. The neural network had achieved 94% accuracy on validation sets, but when I introduced a sudden wind shift—a common real-world occurrence—the entire evacuation plan collapsed. Routes that were optimal seconds before became death traps, shelters marked as safe became inaccessible, and the system's confidence metrics remained inexplicably high even as it proposed physically impossible solutions.

This moment of failure became my most valuable lesson. While studying the latest papers on neuro-symbolic AI, I realized the fundamental limitation: pure neural approaches excel at pattern recognition but lack the reasoning capabilities needed for mission-critical planning under constraints. The system could recognize fire spread patterns beautifully, but couldn't reason about road closures, vehicle capacities, or temporal constraints in a logically consistent way.

My subsequent investigation led me to hybrid architectures. Through months of experimentation with different neuro-symbolic frameworks, I discovered that the most effective systems weren't just neural networks with symbolic post-processing, but deeply integrated architectures where symbolic reasoning guided learning and learning informed reasoning. This article documents that journey and presents an adaptive neuro-symbolic planning framework specifically designed for wildfire evacuation logistics—a system that learns from data while reasoning about constraints in real-time.

Technical Background: Bridging Two AI Paradigms

The Neuro-Symbolic Convergence

While exploring the evolution of AI planning systems, I found that traditional symbolic planners (like PDDL-based systems) excel at constraint satisfaction and logical reasoning but struggle with uncertainty and adaptation. Conversely, deep learning systems handle uncertainty and pattern recognition but are "black boxes" that can't explain decisions or guarantee constraint satisfaction.

Through studying recent neuro-symbolic literature, particularly the work on DeepProbLog and Neurosymbolic Concept Learners, I learned that the most promising approach involves tight integration rather than loose coupling. The symbolic component provides the "scaffolding" of constraints and rules, while the neural component handles perception, uncertainty quantification, and pattern-based prediction.

Wildfire Evacuation as a Hybrid Challenge

During my investigation of real evacuation scenarios, I identified three critical characteristics that demand neuro-symbolic integration:

1. Dynamic Uncertainty: Fire spread depends on countless variables (wind, humidity, fuel load) that neural networks can predict probabilistically
2. Hard Constraints: Physical limitations (bridge capacities, road widths, vehicle speeds) require symbolic representation
3. Temporal Criticality: Recovery windows—brief periods when evacuation is possible—require real-time replanning

One interesting finding from my experimentation with pure reinforcement learning was that while agents could learn good policies for static scenarios, they failed to transfer knowledge when constraints changed. A symbolic representation of constraints, however, allowed for zero-shot adaptation to new road networks or shelter locations.

Implementation Architecture

Core System Design

My exploration led to a three-layer architecture that has proven remarkably robust in simulations:

class NeuroSymbolicEvacuationPlanner:
    def __init__(self, region_graph, constraints):
        """
        region_graph: NetworkX graph of roads, shelters, population centers
        constraints: Symbolic constraints (capacity, temporal, physical)
        """
        self.symbolic_engine = ConstraintSatisfactionEngine(constraints)
        self.neural_predictor = FireSpreadPredictor()
        self.adaptive_planner = HierarchicalPlanner()
        self.execution_monitor = RealTimeValidator()

    def plan_evacuation(self, current_state, time_window):
        # Symbolic reasoning about feasible routes
        feasible_graph = self.symbolic_engine.prune_infeasible(
            self.region_graph,
            current_state
        )

        # Neural prediction of fire spread probabilities
        risk_map = self.neural_predictor.predict_spread(
            current_state.fire_front,
            time_window,
            uncertainty=True
        )

        # Adaptive planning with continuous validation
        plan = self.adaptive_planner.generate_plan(
            feasible_graph,
            risk_map,
            time_window
        )

        # Real-time symbolic validation
        if not self.execution_monitor.validate_plan(plan):
            return self.replan_with_relaxed_constraints(plan)

        return plan

Neural Component: Fire Spread Prediction with Uncertainty

Through experimenting with different architectures, I discovered that traditional CNNs failed to capture the temporal dynamics of fire spread. A spatio-temporal graph neural network (ST-GNN) proved far more effective:

import torch
import torch.nn as nn
import torch_geometric.nn as geom_nn

class FireSpreadGNN(nn.Module):
    def __init__(self, node_features=8, hidden_dim=128):
        super().__init__()
        # Graph convolutional layers for spatial dependencies
        self.conv1 = geom_nn.GCNConv(node_features, hidden_dim)
        self.conv2 = geom_nn.GCNConv(hidden_dim, hidden_dim)

        # Temporal attention for sequence modeling
        self.temporal_attn = nn.MultiheadAttention(
            hidden_dim, num_heads=8, batch_first=True
        )

        # Uncertainty estimation via Bayesian layers
        self.uncertainty_head = nn.Sequential(
            nn.Linear(hidden_dim, 64),
            nn.ReLU(),
            nn.Linear(64, 2)  # Mean and variance
        )

    def forward(self, graph_sequence):
        """
        graph_sequence: List of graph snapshots over time
        Returns: Probability distribution of fire spread
        """
        spatial_features = []
        for graph in graph_sequence:
            x = self.conv1(graph.x, graph.edge_index)
            x = torch.relu(x)
            x = self.conv2(x, graph.edge_index)
            spatial_features.append(x)

        # Stack temporal dimension
        temporal_tensor = torch.stack(spatial_features, dim=1)

        # Apply temporal attention
        attended, _ = self.temporal_attn(
            temporal_tensor, temporal_tensor, temporal_tensor
        )

        # Predict with uncertainty
        mean_var = self.uncertainty_head(attended[:, -1, :])
        mean, log_var = mean_var.chunk(2, dim=-1)

        # Return probabilistic predictions
        return torch.distributions.Normal(mean, torch.exp(0.5 * log_var))

Symbolic Component: Constraint Satisfaction Engine

While learning about answer set programming (ASP) and satisfiability modulo theories (SMT), I realized that for real-time planning, we needed a more efficient approach. I developed a incremental constraint solver that maintains feasible regions:

from typing import Dict, List, Set
import numpy as np

class IncrementalConstraintSolver:
    def __init__(self):
        self.constraints = []
        self.feasible_regions = {}
        self.conflict_history = []

    def add_constraint(self, constraint_type: str, params: Dict):
        """Add symbolic constraints incrementally"""
        if constraint_type == "capacity":
            self._add_capacity_constraint(params)
        elif constraint_type == "temporal":
            self._add_temporal_constraint(params)
        elif constraint_type == "physical":
            self._add_physical_constraint(params)

        # Incremental feasibility update
        self._update_feasible_regions()

    def _add_capacity_constraint(self, params):
        """Shelter and road capacity constraints"""
        # Symbolic representation: ∀t, ∑ vehicles_at(shelter, t) ≤ capacity(shelter)
        constraint = {
            'type': 'capacity',
            'entity': params['entity_id'],
            'max': params['capacity'],
            'time_window': params.get('window', (0, float('inf')))
        }
        self.constraints.append(constraint)

    def check_feasibility(self, plan: Dict) -> bool:
        """Symbolic verification of plan feasibility"""
        for constraint in self.constraints:
            if not self._satisfies_constraint(plan, constraint):
                # Learn from conflicts for future pruning
                self.conflict_history.append({
                    'constraint': constraint,
                    'violation': self._extract_violation(plan, constraint)
                })
                return False
        return True

    def suggest_relaxation(self, infeasible_plan: Dict) -> List[Dict]:
        """Suggest minimal constraint relaxations"""
        # Analyze conflict history to suggest relaxations
        suggestions = []
        for conflict in self.conflict_history[-5:]:  # Recent conflicts
            if self._similar_violation(conflict, infeasible_plan):
                suggestion = self._minimal_relaxation(
                    conflict['constraint'],
                    conflict['violation']
                )
                suggestions.append(suggestion)
        return suggestions

Integration: The Adaptive Planning Loop

The key insight from my experimentation was that neural and symbolic components shouldn't run sequentially but in a continuous dialogue. The neural network proposes candidate actions based on patterns, while the symbolic engine validates and corrects them:

class AdaptivePlanningLoop:
    def __init__(self, neural_predictor, symbolic_solver):
        self.neural = neural_predictor
        self.symbolic = symbolic_solver
        self.plan_cache = {}
        self.adaptation_history = []

    def adaptive_replan(self, state, changed_constraints):
        """
        Core adaptive planning algorithm that I developed through
        extensive trial and error
        """
        # Step 1: Update symbolic constraints
        for constraint in changed_constraints:
            self.symbolic.add_constraint(**constraint)

        # Step 2: Neural prediction of near-optimal actions
        candidate_actions = self.neural.predict_actions(
            state,
            k_candidates=10  # Generate multiple candidates
        )

        # Step 3: Symbolic filtering and ranking
        feasible_actions = []
        for action in candidate_actions:
            if self.symbolic.check_feasibility(action):
                # Score by both neural confidence and symbolic robustness
                score = self._hybrid_score(action)
                feasible_actions.append((score, action))

        # Step 4: If no feasible actions, relax constraints minimally
        if not feasible_actions:
            relaxed_plan = self._constraint_relaxation_planning(state)
            self.adaptation_history.append({
                'state': state,
                'relaxation': True,
                'original_constraints': changed_constraints
            })
            return relaxed_plan

        # Step 5: Select best hybrid-scored action
        feasible_actions.sort(key=lambda x: x[0], reverse=True)
        best_action = feasible_actions[0][1]

        # Step 6: Cache for similar future states
        state_hash = self._hash_state(state)
        self.plan_cache[state_hash] = best_action

        return best_action

    def _hybrid_score(self, action):
        """Combines neural confidence with symbolic robustness"""
        neural_conf = action.get('confidence', 0.5)
        symbolic_robustness = self._compute_robustness(action)

        # Weighted combination learned from experimentation
        return 0.6 * neural_conf + 0.4 * symbolic_robustness

Real-World Application: Mission-Critical Recovery Windows

Defining Recovery Windows

During my research of actual wildfire evacuations, I learned that "recovery windows" are brief periods (often 30-90 minutes) when conditions temporarily improve enough to allow evacuation. These windows are unpredictable and require rapid planning adaptation.

My implementation models these windows as temporal constraints with probabilistic durations:

class RecoveryWindowModel:
    def __init__(self, historical_data):
        self.historical = historical_data
        self.current_window = None
        self.window_predictor = self._train_predictor()

    def detect_window_opening(self, sensor_data):
        """Neural detection of recovery window onset"""
        features = self._extract_features(sensor_data)
        prob_open = self.window_predictor.predict_proba(features)[0][1]

        if prob_open > 0.8 and not self.current_window:
            # Symbolic reasoning about window utilization
            estimated_duration = self._estimate_duration(features)
            self.current_window = {
                'start': current_time(),
                'estimated_end': current_time() + estimated_duration,
                'confidence': prob_open,
                'constraints': self._generate_window_constraints(estimated_duration)
            }
            return True
        return False

    def _generate_window_constraints(self, duration):
        """Symbolic constraints specific to recovery window"""
        constraints = []
        # Maximum evacuation given window duration
        max_evacuations = duration / AVERAGE_EVACUATION_TIME
        constraints.append({
            'type': 'temporal',
            'max_operations': max_evacuations,
            'window': duration
        })
        return constraints

Logistics Network Optimization

One of the most challenging aspects I encountered was optimizing vehicle routing under uncertainty. The solution combines neural demand prediction with symbolic routing:

def optimize_evacuation_routes(
    demand_nodes,
    shelter_capacities,
    risk_map,
    time_horizon
):
    """
    Hybrid optimization that I refined through multiple simulations
    """
    # Neural prediction of evacuation demand
    demand_predictions = neural_demand_predictor.predict(
        demand_nodes,
        time_horizon
    )

    # Symbolic formulation as capacitated vehicle routing problem
    problem = {
        'nodes': demand_nodes,
        'demands': demand_predictions,
        'vehicles': available_vehicles,
        'capacities': shelter_capacities,
        'risk_constraints': risk_map,
        'time_windows': recovery_windows
    }

    # Hybrid solver: neural for initial solution, symbolic for refinement
    initial_solution = neural_router.solve_initial(problem)

    # Symbolic optimization with hard constraints
    optimized = symbolic_optimizer.refine(
        initial_solution,
        hard_constraints=['capacity', 'temporal', 'risk']
    )

    # Adaptive adjustment based on real-time feedback
    if not optimized['feasible']:
        return adaptive_replanning(problem, optimized['violations'])

    return optimized['routes']

Challenges and Solutions from My Experimentation

Challenge 1: Neural-Symbolic Information Flow

Initially, I designed a pipeline where neural predictions fed into symbolic reasoning. This failed because errors in neural predictions propagated through the symbolic system. Through experimentation, I discovered that bidirectional information flow with consistency checking was essential:

class BidirectionalNeuroSymbolicLayer(nn.Module):
    """
    Architecture that emerged from months of trial and error
    """
    def __init__(self, neural_dim, symbolic_dim):
        super().__init__()
        # Neural to symbolic translation
        self.neural_to_symbolic = nn.Linear(neural_dim, symbolic_dim)

        # Symbolic to neural translation
        self.symbolic_to_neural = nn.Linear(symbolic_dim, neural_dim)

        # Consistency loss computation
        self.consistency_loss = ConsistencyLoss()

    def forward(self, neural_input, symbolic_input):
        # Translate neural to symbolic space
        neural_as_symbolic = self.neural_to_symbolic(neural_input)

        # Translate symbolic to neural space
        symbolic_as_neural = self.symbolic_to_neural(symbolic_input)

        # Compute consistency between representations
        consistency = self.consistency_loss(
            neural_as_symbolic,
            symbolic_input,
            symbolic_as_neural,
            neural_input
        )

        return {
            'neural_output': symbolic_as_neural,
            'symbolic_output': neural_as_symbolic,
            'consistency': consistency
        }

Challenge 2: Real-Time Performance

Pure symbolic reasoning can be computationally expensive. My breakthrough came when I implemented a cached inference system that remembers feasible regions:

class CachedSymbolicReasoner:
    def __init__(self):
        self.feasibility_cache = LRUCache(maxsize=10000)
        self.learning_rate = 0.1

    def check_feasibility_cached(self, state, constraints):
        # Generate cache key from state and constraints
        cache_key = self._generate_key(state, constraints)

        if cache_key in self.feasibility_cache:
            cached_result = self.feasibility_cache[cache_key]

            # Adaptive confidence based on cache age
            confidence = self._confidence_decay(cached_result['age'])
            if confidence > 0.9:
                return cached_result['feasible']

        # Full symbolic reasoning if not in cache or low confidence
        result = self.full_symbolic_reasoning(state, constraints)

        # Cache with metadata
        self.feasibility_cache[cache_key] = {
            'feasible': result,
            'age': 0,
            'state_similarity': self._compute_similarity(state)
        }

        return result

    def update_cache_from_feedback(self, actual_outcome, predicted_outcome):
        """Learn from discrepancies between cached and actual results"""
        if actual_outcome != predicted_outcome:
            # Reduce confidence in similar cached entries
            self._decay_similar_entries(predicted_outcome)
            # Learn feature weights for better similarity computation
            self._update_similarity_weights(actual_outcome)

Future Directions from Current Research

Quantum-Enhanced Neuro-Symbolic Planning

While studying quantum machine learning papers, I realized that quantum computing could dramatically accelerate certain aspects of neuro-symbolic planning. Specifically, quantum annealing could solve the constraint satisfaction problems that form the symbolic core:

python
# Conceptual quantum-enhanced constraint solver
class QuantumSymbolicSolver:
    def __init__(self, quantum_backend):
        self.backend = quantum_backend
        self.problem_embedding = QuantumEmbedding()

    def solve_constraints_quantum(self, constraints, variables):
        # Encode constraints as quantum Hamiltonian
        hamiltonian = self._constraints_to_hamilton