⚙️ Session 9: Advanced Coordination - Complex Multi-Agent Algorithms¶

class ByzantineFaultTolerantConsensus:
    """Advanced consensus mechanism tolerating Byzantine failures in multi-agent systems"""

    def __init__(self, agents: List['BaseDataAgent'], fault_tolerance: int = 1):
        self.data_agents = agents
        self.fault_tolerance = fault_tolerance  # Number of Byzantine agents to tolerate
        self.minimum_honest_agents = 3 * fault_tolerance + 1
        self.consensus_rounds = []
        self.signature_validator = DigitalSignatureValidator()

        if len(agents) < self.minimum_honest_agents:
            raise ValueError(f"Need at least {self.minimum_honest_agents} agents for f={fault_tolerance} Byzantine tolerance")

    async def byzantine_consensus_decision(
        self, proposal: str, data_context: Dict[str, Any]
    ) -> Dict[str, Any]:
        """Execute Byzantine fault-tolerant consensus for critical data decisions"""

        consensus_round = {
            'round_id': str(uuid.uuid4()),
            'proposal': proposal,
            'timestamp': datetime.now(),
            'participants': [agent.agent_id for agent in self.data_agents]
        }

        # Phase 1: Pre-prepare phase with proposal validation
        pre_prepare_result = await self._execute_pre_prepare_phase(
            consensus_round, proposal, data_context
        )

        if not pre_prepare_result['success']:
            return {'consensus_reached': False, 'reason': 'Pre-prepare phase failed'}

        # Phase 2: Prepare phase with agent attestations
        prepare_result = await self._execute_prepare_phase(
            consensus_round, pre_prepare_result['prepared_proposal']
        )

        if not prepare_result['sufficient_attestations']:
            return {'consensus_reached': False, 'reason': 'Insufficient prepare attestations'}

        # Phase 3: Commit phase with final validation
        commit_result = await self._execute_commit_phase(
            consensus_round, prepare_result['attestations']
        )

        # Record consensus round for audit
        self.consensus_rounds.append(consensus_round)

        return {
            'consensus_reached': commit_result['consensus_achieved'],
            'decision': commit_result['final_decision'] if commit_result['consensus_achieved'] else None,
            'participating_agents': commit_result['honest_participants'],
            'byzantine_agents_detected': commit_result['byzantine_detections'],
            'consensus_confidence': commit_result['confidence_score']
        }

async def _execute_prepare_phase(
    self, consensus_round: Dict, prepared_proposal: Dict
) -> Dict[str, Any]:
    """Execute prepare phase with cryptographic attestations"""

    prepare_tasks = []
    for agent in self.data_agents:
        task = self._request_agent_prepare_attestation(
            agent, consensus_round, prepared_proposal
        )
        prepare_tasks.append(task)

    # Collect attestations with timeout protection
    attestation_results = await asyncio.gather(
        *prepare_tasks, return_exceptions=True
    )

    # Validate and process attestations
    valid_attestations = []
    byzantine_detections = []

    for i, result in enumerate(attestation_results):
        if isinstance(result, Exception):
            continue

        agent_id = self.data_agents[i].agent_id

        # Validate digital signature
        if await self.signature_validator.validate_attestation(result, agent_id):
            # Validate attestation content consistency
            if await self._validate_attestation_consistency(result, prepared_proposal):
                valid_attestations.append(result)
            else:
                byzantine_detections.append({
                    'agent_id': agent_id,
                    'detection_type': 'inconsistent_attestation',
                    'evidence': result
                })
        else:
            byzantine_detections.append({
                'agent_id': agent_id,
                'detection_type': 'invalid_signature',
                'evidence': result
            })

    # Check if we have sufficient honest attestations
    required_attestations = 2 * self.fault_tolerance + 1
    sufficient_attestations = len(valid_attestations) >= required_attestations

    return {
        'sufficient_attestations': sufficient_attestations,
        'attestations': valid_attestations,
        'byzantine_detections': byzantine_detections,
        'honest_agent_count': len(valid_attestations)
    }

class MultiCriteriaAuctionCoordinator:
    """Advanced auction system optimizing for multiple performance criteria"""

    def __init__(self, agents: List['BaseDataAgent']):
        self.data_agents = agents
        self.auction_history = []
        self.performance_predictor = AgentPerformancePredictor()
        self.quality_assessor = DataQualityAssessor()

    async def conduct_multi_criteria_auction(
        self, task: str, requirements: Dict[str, Any], criteria_weights: Dict[str, float]
    ) -> Dict[str, Any]:
        """Conduct auction optimizing for multiple performance criteria"""

        auction_session = {
            'auction_id': str(uuid.uuid4()),
            'task': task,
            'requirements': requirements,
            'criteria_weights': criteria_weights,
            'timestamp': datetime.now()
        }

        # Phase 1: Enhanced capability assessment with predictive modeling
        capability_assessments = await self._enhanced_capability_assessment(
            task, requirements, criteria_weights
        )

        # Phase 2: Multi-dimensional bid collection
        multi_dimensional_bids = await self._collect_multi_dimensional_bids(
            task, requirements, criteria_weights, capability_assessments
        )

        # Phase 3: Advanced bid evaluation with quality prediction
        bid_evaluations = await self._evaluate_multi_criteria_bids(
            multi_dimensional_bids, criteria_weights, capability_assessments
        )

        # Phase 4: Winner selection with risk assessment
        winner_selection = await self._select_optimal_winner(
            bid_evaluations, criteria_weights, requirements
        )

        # Record auction for learning
        auction_session.update({
            'winner': winner_selection,
            'bid_evaluations': bid_evaluations,
            'total_bids': len(multi_dimensional_bids)
        })
        self.auction_history.append(auction_session)

        return {
            'auction_successful': winner_selection['winner_found'],
            'winning_agent': winner_selection['agent_id'] if winner_selection['winner_found'] else None,
            'winning_bid': winner_selection['bid'] if winner_selection['winner_found'] else None,
            'evaluation_scores': winner_selection['evaluation_scores'] if winner_selection['winner_found'] else None,
            'risk_assessment': winner_selection['risk_assessment'] if winner_selection['winner_found'] else None,
            'auction_summary': auction_session
        }

async def _evaluate_multi_criteria_bids(
    self, bids: List[Dict], criteria_weights: Dict[str, float],
    capability_assessments: Dict[str, Dict]
) -> Dict[str, Any]:
    """Evaluate bids using sophisticated multi-criteria analysis"""

    bid_evaluations = {}

    for bid in bids:
        agent_id = bid['agent_id']
        agent_capabilities = capability_assessments[agent_id]

        # Calculate scores for each evaluation criterion
        criterion_scores = {}

        # Cost efficiency score (lower cost = higher score)
        if 'cost' in criteria_weights:
            max_cost = max(b['bid_details']['estimated_cost'] for b in bids)
            min_cost = min(b['bid_details']['estimated_cost'] for b in bids)
            cost_range = max_cost - min_cost if max_cost > min_cost else 1

            normalized_cost = (max_cost - bid['bid_details']['estimated_cost']) / cost_range
            criterion_scores['cost'] = normalized_cost

        # Quality prediction score
        if 'quality' in criteria_weights:
            quality_prediction = await self.performance_predictor.predict_quality(
                agent_id, bid['task_characteristics'], agent_capabilities
            )
            criterion_scores['quality'] = quality_prediction['expected_quality']

        # Performance prediction score
        if 'performance' in criteria_weights:
            performance_prediction = await self.performance_predictor.predict_performance(
                agent_id, bid['task_characteristics'], agent_capabilities
            )
            criterion_scores['performance'] = performance_prediction['expected_throughput'] / 1000.0  # Normalize

        # Reliability score based on historical performance
        if 'reliability' in criteria_weights:
            reliability_score = await self._calculate_agent_reliability(
                agent_id, bid['task_characteristics']
            )
            criterion_scores['reliability'] = reliability_score

        # Innovation score for novel approaches
        if 'innovation' in criteria_weights:
            innovation_score = await self._assess_bid_innovation(
                bid, self.auction_history
            )
            criterion_scores['innovation'] = innovation_score

        # Calculate weighted overall score
        overall_score = sum(
            criterion_scores[criterion] * criteria_weights[criterion]
            for criterion in criterion_scores.keys()
        )

        bid_evaluations[agent_id] = {
            'overall_score': overall_score,
            'criterion_scores': criterion_scores,
            'bid_details': bid,
            'risk_factors': await self._assess_bid_risk_factors(bid, agent_capabilities)
        }

    return bid_evaluations

class AdaptiveHierarchicalCoordinator:
    """Advanced hierarchical coordinator with dynamic structure optimization"""

    def __init__(self, agent_pool: List['BaseDataAgent']):
        self.agent_pool = agent_pool
        self.hierarchy_optimizer = HierarchyOptimizer()
        self.capability_analyzer = AgentCapabilityAnalyzer()
        self.coordination_patterns = CoordinationPatternLibrary()

    async def create_optimal_hierarchy(
        self, complex_task: str, performance_requirements: Dict[str, Any]
    ) -> Dict[str, Any]:
        """Create optimally structured hierarchy for complex data processing task"""

        # Phase 1: Task complexity analysis
        complexity_analysis = await self._analyze_task_complexity(
            complex_task, performance_requirements
        )

        # Phase 2: Agent capability profiling
        agent_profiles = await self._profile_agent_capabilities(
            self.agent_pool, complex_task
        )

        # Phase 3: Optimal hierarchy design
        hierarchy_design = await self._design_optimal_hierarchy(
            complexity_analysis, agent_profiles, performance_requirements
        )

        # Phase 4: Dynamic role assignment
        role_assignments = await self._assign_dynamic_roles(
            hierarchy_design, agent_profiles
        )

        # Phase 5: Coordination protocol setup
        coordination_protocols = await self._setup_coordination_protocols(
            hierarchy_design, role_assignments
        )

        return {
            'hierarchy_structure': hierarchy_design,
            'role_assignments': role_assignments,
            'coordination_protocols': coordination_protocols,
            'expected_performance': await self._predict_hierarchy_performance(
                hierarchy_design, role_assignments, complexity_analysis
            )
        }

async def _assign_dynamic_roles(
    self, hierarchy_design: Dict, agent_profiles: Dict
) -> Dict[str, Any]:
    """Assign roles dynamically based on agent capabilities and hierarchy requirements"""

    role_assignments = {}
    unassigned_agents = list(agent_profiles.keys())

    # Sort roles by criticality and complexity
    sorted_roles = sorted(
        hierarchy_design['roles'],
        key=lambda r: (r['criticality'], r['complexity']),
        reverse=True
    )

    for role in sorted_roles:
        # Find best-suited agent for this role
        best_agent = await self._find_optimal_agent_for_role(
            role, agent_profiles, unassigned_agents
        )

        if best_agent:
            role_assignments[role['role_id']] = {
                'agent_id': best_agent['agent_id'],
                'assignment_confidence': best_agent['suitability_score'],
                'expected_performance': best_agent['performance_prediction'],
                'role_adaptations': await self._generate_role_adaptations(
                    role, best_agent['capabilities']
                )
            }

            unassigned_agents.remove(best_agent['agent_id'])

            # Update agent profiles to reflect assignment
            agent_profiles[best_agent['agent_id']]['current_assignment'] = role['role_id']
            agent_profiles[best_agent['agent_id']]['remaining_capacity'] *= (
                1.0 - role['resource_requirements']['cpu_utilization']
            )
        else:
            # Handle unassignable role
            role_assignments[role['role_id']] = {
                'status': 'unassigned',
                'reason': 'No suitable agent available',
                'fallback_strategy': await self._generate_fallback_strategy(role)
            }

    return {
        'assignments': role_assignments,
        'unassigned_agents': unassigned_agents,
        'assignment_efficiency': len([a for a in role_assignments.values() if 'agent_id' in a]) / len(sorted_roles),
        'load_distribution': await self._analyze_load_distribution(role_assignments, agent_profiles)
    }

class GuaranteedDeliveryProtocol:
    """Advanced communication protocol with guaranteed delivery and ordering"""

    def __init__(self, communication_hub: 'DataCommunicationHub'):
        self.hub = communication_hub
        self.message_acknowledgments = {}
        self.delivery_confirmations = {}
        self.retry_scheduler = MessageRetryScheduler()
        self.ordering_manager = MessageOrderingManager()

    async def send_guaranteed_message(
        self, message: DataAgentMessage, delivery_guarantees: Dict[str, Any]
    ) -> Dict[str, Any]:
        """Send message with specified delivery guarantees"""

        # Prepare message with delivery metadata
        enhanced_message = await self._prepare_guaranteed_message(
            message, delivery_guarantees
        )

        # Register for delivery tracking
        delivery_token = await self._register_delivery_tracking(
            enhanced_message, delivery_guarantees
        )

        # Attempt initial delivery
        initial_delivery = await self._attempt_message_delivery(
            enhanced_message, delivery_guarantees
        )

        if initial_delivery['success']:
            # Setup acknowledgment waiting if required
            if delivery_guarantees.get('require_acknowledgment', False):
                await self._setup_acknowledgment_tracking(
                    enhanced_message, delivery_token, delivery_guarantees
                )

            return {
                'delivery_initiated': True,
                'delivery_token': delivery_token,
                'initial_attempt': initial_delivery
            }
        else:
            # Setup retry mechanism
            retry_schedule = await self._setup_retry_mechanism(
                enhanced_message, delivery_guarantees, initial_delivery['error']
            )

            return {
                'delivery_initiated': True,
                'delivery_token': delivery_token,
                'initial_attempt': initial_delivery,
                'retry_schedule': retry_schedule
            }

class MessageOrderingManager:
    """Manages message ordering across distributed agent communications"""

    def __init__(self):
        self.conversation_sequences = {}
        self.pending_ordered_messages = {}
        self.ordering_violations_detected = []

    async def ensure_message_ordering(
        self, message: DataAgentMessage, ordering_requirements: Dict[str, Any]
    ) -> Dict[str, Any]:
        """Ensure message is delivered in correct order based on requirements"""

        conversation_id = message.conversation_id
        if not conversation_id:
            # Messages without conversation don't need ordering
            return {'ordering_required': False, 'can_deliver_immediately': True}

        ordering_type = ordering_requirements.get('type', 'strict')

        if ordering_type == 'strict':
            return await self._handle_strict_ordering(message, ordering_requirements)
        elif ordering_type == 'causal':
            return await self._handle_causal_ordering(message, ordering_requirements)
        elif ordering_type == 'partial':
            return await self._handle_partial_ordering(message, ordering_requirements)
        else:
            return {'ordering_required': False, 'can_deliver_immediately': True}

    async def _handle_strict_ordering(
        self, message: DataAgentMessage, requirements: Dict[str, Any]
    ) -> Dict[str, Any]:
        """Handle strict sequential message ordering"""

        conversation_id = message.conversation_id
        expected_sequence = self.conversation_sequences.get(conversation_id, 0)
        message_sequence = message.data_payload.get('sequence_number', -1)

        if message_sequence == expected_sequence:
            # Message is in correct order
            self.conversation_sequences[conversation_id] = expected_sequence + 1

            # Check if any pending messages can now be delivered
            deliverable_messages = await self._check_pending_messages(conversation_id)

            return {
                'ordering_required': True,
                'can_deliver_immediately': True,
                'sequence_updated': True,
                'pending_deliverable': deliverable_messages
            }
        elif message_sequence > expected_sequence:
            # Future message received - must wait for earlier messages
            if conversation_id not in self.pending_ordered_messages:
                self.pending_ordered_messages[conversation_id] = {}

            self.pending_ordered_messages[conversation_id][message_sequence] = message

            return {
                'ordering_required': True,
                'can_deliver_immediately': False,
                'reason': f'Waiting for sequence {expected_sequence}, got {message_sequence}',
                'pending_position': message_sequence - expected_sequence
            }
        else:
            # Old message received - potential duplicate or violation
            self.ordering_violations_detected.append({
                'conversation_id': conversation_id,
                'expected_sequence': expected_sequence,
                'received_sequence': message_sequence,
                'timestamp': datetime.now(),
                'violation_type': 'sequence_regression'
            })

            return {
                'ordering_required': True,
                'can_deliver_immediately': False,
                'reason': 'Sequence violation detected',
                'violation_recorded': True
            }