Add Resolver Agent – 6th agent for conflict resolution and system resilience

[Copilot]

Adds a comprehensive Resolver Agent as the 6th intelligent agent in the Agentic AGI robotics system, acting as the system mediator and health guardian across all other agents.

Core Agent

• agents/base_agent.py – Abstract base with state management, typed message passing, and lifecycle hooks shared by all agents
• agents/resolver_agent.py – Priority-6 (highest) orchestrator integrating all subsystems; exposes integrate_with_agents(), resolve_conflict(), recover_from_error(), arbitrate_resources(), detect_deadlock(), and generate_health_report()

Resolver Subsystems (resolver/)

• conflict_resolution.py – Detects 4 conflict types (perception/planning, control, task, resource); resolves via 6 strategies: priority, voting, expertise, cost, time-based, ML
• error_recovery.py – Classifies 6 error types across 5 severity levels (INFO→FATAL); auto-recovers with per-type strategies and escalates after MAX_RECOVERY_ATTEMPTS
• arbitrator.py – Priority queue, round-robin, weighted-fair, and emergency-override arbitration
• health_monitor.py – Per-agent CPU/memory/response-time/error-rate tracking with threshold alerts and trend-based failure prediction
• deadlock_detector.py – DFS cycle detection on wait graph; breaks deadlocks by preempting the lowest-priority participant
• fallback_planner.py – Depth-ordered fallback hierarchies (0=retry → 3=safe shutdown) for 5 subsystems
• predictor.py – PyTorch feed-forward network for ML conflict prediction; falls back to heuristics when PyTorch is unavailable

Integration & Infrastructure

• ros2_interface/resolver_node.py – ROS2 node subscribing to all 5 agent status topics + /system/errors//system/conflicts; publishes resolutions, health, alerts, and recovery actions; includes no-op stubs so the module imports cleanly without ROS2
• config/ – YAML configs for resolver behaviour, agent priorities, and per-error-type recovery strategies
• dashboard/ – Flask dashboard with Server-Sent Events for real-time agent health; degrades gracefully to console polling without Flask

Usage

resolver = ResolverAgent()
resolver.integrate_with_agents([perception, planning, control, communication, coordination])
resolver.start_monitoring()  # background thread at 1 Hz

# Conflict between two agents
resolution = resolver.resolve_conflict({
    "type": "control",
    "agents": ["planning", "control"],
})
# → {"winning_agent": "planning", "strategy": "priority_based", ...}

# Auto-recover from a sensor failure
resolver.recover_from_error({"type": "sensor_failure", "sensor": "camera", "severity": 2})
# → switches to backup sensor; escalates to human after 3 failed attempts

health = resolver.generate_health_report()
# → {"overall_status": "healthy", "agents": {...}, "alerts": [...]}

Tests

87 tests across 6 files covering unit, integration (all 5 agents), and edge cases (max retry escalation, deadlock cycle breaking, fallback depth).

Original prompt

Add Resolver Agent - The 6th Agent for Conflict Resolution and System Resilience

Objective

Add a comprehensive Resolver Agent as the 6th intelligent agent in the Agentic AGI robotics system. This agent will handle conflict resolution, error recovery, agent arbitration, deadlock breaking, and system health monitoring to ensure robust and reliable robot operations.

Role of the Resolver Agent

The Resolver Agent acts as the "System Mediator and Health Guardian" - it monitors all other agents, resolves conflicts, handles errors gracefully, and ensures the system continues operating even when problems arise.

Technology Stack

• Python 3.10+
• LangChain - Agent reasoning and decision-making
• PyTorch - ML-based conflict prediction
• ROS2 - Real-time monitoring and control
• Redis/SQLite - State persistence
• Prometheus/Grafana - Health metrics (optional)

Core Components to Implement

1. Resolver Agent Module (agents/resolver_agent.py)

class ResolverAgent(BaseAgent):
    """
    The 6th Agent: System Resolver and Health Guardian
    
    Responsibilities:
    - Conflict resolution between agents
    - Error detection and recovery
    - Deadlock detection and breaking
    - Resource arbitration
    - Priority management
    - System health monitoring
    - Fallback strategy execution
    """
    
    def __init__(self):
        self.conflict_detector = ConflictDetector()
        self.error_handler = ErrorRecoverySystem()
        self.arbitrator = AgentArbitrator()
        self.health_monitor = HealthMonitor()
        self.deadlock_detector = DeadlockDetector()
        self.fallback_planner = FallbackPlanner()
        
    def monitor_agents(self):
        """Continuously monitor all 5 agents"""
        
    def resolve_conflict(self, conflict):
        """Resolve conflicts between agents"""
        
    def recover_from_error(self, error):
        """Handle errors and recover gracefully"""
        
    def arbitrate_resources(self, requests):
        """Decide resource allocation when contested"""
        
    def detect_deadlock(self):
        """Find and break deadlocks"""
        
    def assess_system_health(self):
        """Check overall system health"""
        
    def execute_fallback(self, failure):
        """Execute alternative strategies"""

2. Conflict Resolution Algorithms (resolver/conflict_resolution.py)

Types of Conflicts to Handle:

a) Agent Disagreements

class ConflictDetector:
    """
    Detect conflicts between agents:
    - Perception Agent sees object, Planning Agent thinks it doesn't exist
    - Control Agent wants to move left, Navigation says go right
    - Communication Agent receives contradictory commands
    - Coordination Agent assigns same task to multiple robots
    """
    
    def detect_perception_planning_conflict(self):
        """Perception vs Planning disagreements"""
        
    def detect_control_conflict(self):
        """Multiple agents trying to control robot"""
        
    def detect_task_conflict(self):
        """Overlapping or contradictory tasks"""
        
    def detect_resource_conflict(self):
        """Multiple agents need same resource"""

b) Conflict Resolution Strategies

class ConflictResolver:
    """
    Resolution strategies:
    1. Priority-based (higher priority wins)
    2. Voting (majority rules)
    3. Expert arbitration (most qualified agent decides)
    4. Time-based (first-come-first-served)
    5. Cost-based (least cost approach)
    6. ML-based (learned from past resolutions)
    """
    
    def resolve_by_priority(self, conflict):
        """Use agent priorities"""
        
    def resolve_by_voting(self, conflict):
        """Democratic voting among agents"""
        
    def resolve_by_expertise(self, conflict):
        """Expert agent decides"""
        
    def resolve_by_cost(self, conflict):
        """Choose lowest cost solution"""
        
    def resolve_by_ml(self, conflict):
        """ML model predicts best resolution"""

c) Conflict Prediction

class ConflictPredictor:
    """
    Predict conflicts before they happen using ML
    """
    def train_predictor(self, historical_conflicts):
        """Train PyTorch model on past conflicts"""
        
    def predict_conflict_probability(self, agent_states):
        """Predict if conflict is likely"""
        
    def suggest_preventive_actions(self):
        """Suggest actions to avoid conflicts"""

3. Error Recovery System (resolver/error_recovery.py)

class ErrorRecoverySystem:
    """
    Handle all types of errors:
    - Sensor failures (camera down, lidar malfunction)
    - Agent crashes (one agent stops responding)
    - Planning failures (no valid plan found)
    - Execution failures (robot can't reach goal)
    - Communication failures (lost connection)
    - Hardware failures (motor error, battery low)
    """
    
    def...

</details>



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