Technical
AIMatrix Architecture
TwinML (Twin Markup Language) Specification

TwinML (Twin Markup Language) Specification

TwinML (Twin Markup Language) is an object-oriented design language specifically created for modeling digital systems, digital twins, and intelligent agents. Similar to how traditional object-oriented programming languages enable software development through classes, objects, and inheritance, TwinML provides the same paradigms for digital system modeling.

Core Philosophy

TwinML follows the principle that digital systems should be modeled using the same proven patterns that make software development scalable and maintainable:

  • Object-Oriented Design: Components, Systems, Objects with clear interfaces
  • Inheritance & Composition: Reuse patterns that reduce development time
  • Multi-Language Support: Write once, deploy in any supported language
  • Type Safety: Compile-time guarantees for system correctness
  • Event-Driven Architecture: Reactive systems with clear event flow

Language Elements

1. Components (@Component)

Components are reusable building blocks that encapsulate specific functionality. They can have properties, methods, lifecycle hooks, and can emit/consume events.

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@Component
class WarehouseComponent : BaseComponent() {
    // Properties with validation
    @Property(min = 0, max = 100000)
    var capacity: Int = 0
    
    @Property(required = true)
    var location: String = ""
    
    @Property(default = AutomationLevel.MANUAL)
    var automationLevel: AutomationLevel = AutomationLevel.MANUAL
    
    // State management
    @State
    private var currentInventory: MutableMap<String, Int> = mutableMapOf()
    
    // Methods
    @Method
    suspend fun addInventory(item: String, quantity: Int) {
        val current = currentInventory.getOrDefault(item, 0)
        currentInventory[item] = current + quantity
        emit(InventoryChangedEvent(item, current + quantity))
    }
    
    @Method
    suspend fun removeInventory(item: String, quantity: Int): Boolean {
        val current = currentInventory.getOrDefault(item, 0)
        if (current >= quantity) {
            currentInventory[item] = current - quantity
            emit(InventoryChangedEvent(item, current - quantity))
            return true
        }
        return false
    }
    
    // Lifecycle hooks
    @OnInitialize
    suspend fun initialize() {
        logger.info("Warehouse initialized at $location with capacity $capacity")
    }
    
    @OnDestroy
    suspend fun cleanup() {
        logger.info("Warehouse at $location shutting down")
    }
    
    // Event handlers
    @EventHandler
    suspend fun onCapacityChange(event: CapacityChangeEvent) {
        capacity = event.newCapacity
        emit(WarehouseUpdatedEvent(location, capacity))
    }
}

2. Systems (@System)

Systems coordinate multiple components to achieve complex goals. They manage component lifecycles, handle inter-component communication, and implement business logic.

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@System
class LogisticsSystem : BaseSystem() {
    // Dependency injection of components
    @Inject
    lateinit var warehouse: WarehouseComponent
    
    @Inject
    lateinit var fleetManager: FleetManagementComponent
    
    @Inject  
    lateinit var routeOptimizer: RouteOptimizerComponent
    
    // System properties
    @Property
    var maxDeliveryTime: Duration = Duration.ofHours(24)
    
    @Property
    var costPerMile: BigDecimal = BigDecimal("2.50")
    
    // System state
    @State
    private val activeDeliveries: MutableSet<Delivery> = mutableSetOf()
    
    @State
    private val deliveryQueue: PriorityQueue<DeliveryRequest> = PriorityQueue()
    
    // System methods
    @Method
    suspend fun scheduleDelivery(request: DeliveryRequest): Delivery {
        // Validate inventory availability
        if (!warehouse.hasInventory(request.item, request.quantity)) {
            throw InsufficientInventoryException(request.item, request.quantity)
        }
        
        // Optimize route
        val route = routeOptimizer.findOptimalRoute(
            from = warehouse.location,
            to = request.destination,
            constraints = RouteConstraints(
                maxTime = maxDeliveryTime,
                maxCost = request.budget
            )
        )
        
        // Assign vehicle
        val vehicle = fleetManager.assignVehicle(route, request.loadRequirements)
        
        // Create delivery
        val delivery = Delivery(
            id = generateDeliveryId(),
            request = request,
            route = route,
            vehicle = vehicle,
            estimatedDelivery = calculateDeliveryTime(route)
        )
        
        activeDeliveries.add(delivery)
        
        // Reserve inventory
        warehouse.reserveInventory(request.item, request.quantity)
        
        emit(DeliveryScheduledEvent(delivery))
        return delivery
    }
    
    // Background processing
    @BackgroundTask(interval = "30s")
    suspend fun processDeliveryQueue() {
        while (deliveryQueue.isNotEmpty() && canScheduleMoreDeliveries()) {
            val request = deliveryQueue.poll()
            try {
                scheduleDelivery(request)
            } catch (e: Exception) {
                emit(DeliveryFailedEvent(request, e.message))
            }
        }
    }
    
    // Event handlers
    @EventHandler
    suspend fun onVehicleLocationUpdate(event: VehicleLocationUpdateEvent) {
        val delivery = activeDeliveries.find { it.vehicle.id == event.vehicleId }
        delivery?.let {
            updateDeliveryProgress(it, event.location)
        }
    }
}

3. Objects (@Object)

Objects represent data entities with properties, validation, relationships, and business logic. They can be persisted, queried, and participate in transactions.

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@Object
@Table("inventory_items")
class InventoryItem : BaseObject() {
    @Id
    @GeneratedValue
    var id: Long = 0
    
    @Property(required = true, maxLength = 100)
    @Column("item_name")
    var name: String = ""
    
    @Property(required = true)
    @Column("sku")
    @Unique
    var sku: String = ""
    
    @Property(min = 0.0)
    @Column("unit_cost")  
    var unitCost: BigDecimal = BigDecimal.ZERO
    
    @Property(min = 0)
    @Column("quantity_on_hand")
    var quantityOnHand: Int = 0
    
    @Property(min = 0)
    @Column("reorder_point")
    var reorderPoint: Int = 0
    
    @Property
    @Column("created_at")
    var createdAt: LocalDateTime = LocalDateTime.now()
    
    @Property
    @Column("updated_at")
    var updatedAt: LocalDateTime = LocalDateTime.now()
    
    // Relationships
    @OneToMany(mappedBy = "item")
    var transactions: List<InventoryTransaction> = emptyList()
    
    @ManyToOne
    @JoinColumn("category_id")
    var category: ItemCategory? = null
    
    // Computed properties
    @Computed
    val totalValue: BigDecimal
        get() = unitCost * quantityOnHand.toBigDecimal()
    
    @Computed
    val needsReorder: Boolean
        get() = quantityOnHand <= reorderPoint
    
    // Business logic methods
    @Method
    fun adjustQuantity(adjustment: Int, reason: String): InventoryTransaction {
        val previousQuantity = quantityOnHand
        quantityOnHand += adjustment
        updatedAt = LocalDateTime.now()
        
        return InventoryTransaction(
            item = this,
            previousQuantity = previousQuantity,
            adjustment = adjustment,
            newQuantity = quantityOnHand,
            reason = reason,
            timestamp = LocalDateTime.now()
        )
    }
    
    @Method
    @Transactional
    suspend fun transfer(targetWarehouse: Warehouse, quantity: Int) {
        if (quantity > quantityOnHand) {
            throw InsufficientQuantityException(sku, quantityOnHand, quantity)
        }
        
        // Create transfer record
        val transfer = InventoryTransfer(
            item = this,
            fromWarehouse = warehouse,
            toWarehouse = targetWarehouse,
            quantity = quantity,
            status = TransferStatus.IN_TRANSIT
        )
        
        // Adjust quantities
        adjustQuantity(-quantity, "Transfer to ${targetWarehouse.name}")
        
        emit(InventoryTransferInitiatedEvent(transfer))
    }
    
    // Validation
    @Validate
    fun validateSku(): ValidationResult {
        if (!sku.matches(Regex("^[A-Z]{2}\\d{4}$"))) {
            return ValidationResult.error("SKU must be in format: AA1234")
        }
        return ValidationResult.success()
    }
    
    // Lifecycle hooks
    @BeforePersist
    fun setTimestamps() {
        if (createdAt == LocalDateTime.MIN) {
            createdAt = LocalDateTime.now()
        }
        updatedAt = LocalDateTime.now()
    }
}

4. Interfaces (@Interface)

Interfaces define contracts for communication between different parts of the system. They specify events, method signatures, and data contracts.

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@Interface
interface SupplyChainEvents {
    // Event definitions
    @Event
    suspend fun onInventoryLow(item: String, currentLevel: Int, reorderPoint: Int)
    
    @Event
    suspend fun onDeliveryScheduled(delivery: Delivery)
    
    @Event
    suspend fun onDeliveryCompleted(deliveryId: String, actualDeliveryTime: LocalDateTime)
    
    @Event
    suspend fun onWarehouseCapacityChanged(warehouseId: String, newCapacity: Int)
    
    @Event
    suspend fun onVehicleBreakdown(vehicleId: String, location: GeoLocation, severity: BreakdownSeverity)
}

@Interface
interface InventoryManager {
    // Method contracts
    @Method
    suspend fun checkAvailability(sku: String): InventoryStatus
    
    @Method
    suspend fun reserveInventory(sku: String, quantity: Int): ReservationId
    
    @Method
    suspend fun releaseReservation(reservationId: ReservationId)
    
    @Method
    suspend fun fulfillReservation(reservationId: ReservationId): FulfillmentResult
    
    // Query contracts
    @Query
    suspend fun findLowStockItems(threshold: Int = 10): List<InventoryItem>
    
    @Query
    suspend fun getInventoryValue(): BigDecimal
    
    @Query
    suspend fun getInventoryByCategory(category: ItemCategory): List<InventoryItem>
}

@Interface
interface RouteOptimizer {
    @Method
    suspend fun findOptimalRoute(
        from: GeoLocation,
        to: GeoLocation,
        constraints: RouteConstraints
    ): Route
    
    @Method
    suspend fun optimizeMultipleDeliveries(
        startLocation: GeoLocation,
        deliveries: List<DeliveryRequest>
    ): List<Route>
    
    @Method
    suspend fun recalculateRoute(
        currentRoute: Route,
        newConstraints: RouteConstraints
    ): Route
}

5. Digital Twin Composition

Digital twins combine components, systems, and objects into cohesive models that represent real-world entities or processes.

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@TwinML
@DigitalTwin("supply-chain-v2.1")
class SupplyChainTwin : DigitalTwin(), SupplyChainEvents {
    // Twin metadata
    @Metadata
    val version = "2.1.0"
    
    @Metadata
    val author = "Supply Chain Team"
    
    @Metadata
    val description = "Complete supply chain digital twin with predictive capabilities"
    
    // Component composition
    @Component
    val mainWarehouse = WarehouseComponent {
        capacity = 50000
        location = "Chicago Distribution Center"
        automationLevel = AutomationLevel.FULLY_AUTOMATED
        layout = WarehouseLayout.CROSS_DOCK
    }
    
    @Component
    val secondaryWarehouses = listOf(
        WarehouseComponent {
            capacity = 25000
            location = "Atlanta Regional Hub"
            automationLevel = AutomationLevel.SEMI_AUTOMATED
        },
        WarehouseComponent {
            capacity = 15000
            location = "Denver Mountain Hub"
            automationLevel = AutomationLevel.MANUAL
        }
    )
    
    @Component
    val fleetManager = FleetManagementComponent {
        vehicles = listOf(
            Vehicle(type = VehicleType.SEMI_TRUCK, capacity = 40000),
            Vehicle(type = VehicleType.BOX_TRUCK, capacity = 12000),
            Vehicle(type = VehicleType.VAN, capacity = 3000)
        )
        maintenanceSchedule = MaintenanceSchedule.PREVENTIVE
    }
    
    // System orchestration
    @System
    val logistics = LogisticsSystem {
        warehouses = listOf(mainWarehouse) + secondaryWarehouses
        fleetManager = this@SupplyChainTwin.fleetManager
        routeOptimizer = aiOptimizer
    }
    
    @System
    val aiOptimizer = AIRouteOptimizerSystem {
        algorithm = OptimizationAlgorithm.GENETIC_ALGORITHM
        realTimeTraffic = true
        weatherIntegration = true
        carbonOptimization = true
    }
    
    @System
    val demandForecasting = DemandForecastingSystem {
        forecastHorizon = Duration.ofDays(90)
        modelType = ForecastModel.NEURAL_NETWORK
        externalDataSources = listOf(
            WeatherData, EconomicIndicators, SeasonalTrends
        )
    }
    
    // Object management
    @ObjectRepository
    val inventory = InventoryRepository()
    
    @ObjectRepository  
    val orders = OrderRepository()
    
    @ObjectRepository
    val suppliers = SupplierRepository()
    
    // Twin-level methods
    @Method
    suspend fun optimizeFullChain() {
        // Coordinate across all systems
        val forecast = demandForecasting.getForecast(Duration.ofDays(30))
        val currentInventory = inventory.getCurrentLevels()
        val activeOrders = orders.getActiveOrders()
        
        // Multi-system optimization
        val optimization = ChainOptimization(
            forecast = forecast,
            inventory = currentInventory,
            orders = activeOrders,
            capacity = getAllWarehouseCapacities(),
            fleet = fleetManager.getAvailableCapacity()
        )
        
        val result = aiOptimizer.optimizeChain(optimization)
        
        // Apply optimizations
        logistics.applyOptimization(result.logistics)
        inventory.applyReorderPlan(result.inventory)
        fleetManager.applySchedule(result.fleet)
        
        emit(ChainOptimizationAppliedEvent(result))
    }
    
    // Event handlers (implementing SupplyChainEvents interface)
    override suspend fun onInventoryLow(item: String, currentLevel: Int, reorderPoint: Int) {
        logger.warn("Low inventory alert: $item at $currentLevel (reorder at $reorderPoint)")
        
        // Trigger automatic reordering
        val supplier = suppliers.findPreferredSupplier(item)
        val orderQuantity = calculateOptimalOrderQuantity(item, currentLevel)
        
        val purchaseOrder = PurchaseOrder(
            supplier = supplier,
            item = item,
            quantity = orderQuantity,
            urgency = if (currentLevel < reorderPoint * 0.5) Urgency.HIGH else Urgency.NORMAL
        )
        
        orders.submitPurchaseOrder(purchaseOrder)
        emit(AutoReorderTriggeredEvent(item, orderQuantity))
    }
    
    override suspend fun onDeliveryScheduled(delivery: Delivery) {
        logger.info("Delivery scheduled: ${delivery.id} to ${delivery.destination}")
        
        // Update demand forecasting model
        demandForecasting.recordDelivery(delivery)
        
        // Notify customer systems
        emit(CustomerNotificationEvent(delivery.customerId, delivery))
    }
    
    // Twin lifecycle
    @OnStart
    suspend fun initializeTwin() {
        logger.info("Initializing Supply Chain Digital Twin v$version")
        
        // Start all systems
        logistics.start()
        aiOptimizer.start()  
        demandForecasting.start()
        
        // Load initial data
        inventory.loadCurrentState()
        orders.loadActiveOrders()
        
        // Begin optimization cycle
        startPeriodicOptimization()
    }
    
    @OnStop
    suspend fun shutdownTwin() {
        logger.info("Shutting down Supply Chain Digital Twin")
        
        // Graceful shutdown
        logistics.stop()
        aiOptimizer.stop()
        demandForecasting.stop()
        
        // Persist final state
        persistTwinState()
    }
}

Multi-Language Implementation

Kotlin (First-Class)

Kotlin serves as the native execution environment with direct performance benefits:

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// Direct execution - no transpilation overhead
@TwinML
class ManufacturingTwin : DigitalTwin() {
    @Component
    val productionLine = ProductionLineComponent {
        throughput = 1000 // units/hour
        efficiency = 0.95
        qualityThreshold = 0.99
    }
    
    // Native coroutines for high-performance async operations
    @Method
    suspend fun processOrder(order: ProductionOrder): ProductionResult = coroutineScope {
        val qualityCheck = async { runQualityInspection(order) }
        val production = async { productionLine.manufacture(order) }
        
        val results = awaitAll(qualityCheck, production)
        ProductionResult(results[0] as QualityResult, results[1] as ManufacturingResult)
    }
}

Python → Kotlin Transpilation

Python code transpiles to Kotlin while preserving NumPy/Pandas semantics:

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# Python TwinML
@twin_ml
class DataAnalyticsTwin(DigitalTwin):
    @component
    def analytics_engine(self):
        return AnalyticsComponent(
            algorithms=['linear_regression', 'neural_network'],
            data_sources=['sales', 'inventory', 'weather']
        )
    
    @method
    async def analyze_demand(self, timeframe: str) -> DemandAnalysis:
        # NumPy operations transpile to optimized Kotlin equivalents
        import numpy as np
        import pandas as pd
        
        data = await self.get_historical_data(timeframe)
        df = pd.DataFrame(data)
        
        # Statistical analysis
        trends = np.polyfit(df['date'].values, df['demand'].values, 2)
        forecast = np.poly1d(trends)
        
        return DemandAnalysis(
            trends=trends.tolist(),
            forecast=forecast(df['date'].values).tolist()
        )

# Transpiles to Kotlin:
@TwinML  
class DataAnalyticsTwin : DigitalTwin() {
    @Component
    val analyticsEngine = AnalyticsComponent {
        algorithms = listOf("linear_regression", "neural_network")
        dataSources = listOf("sales", "inventory", "weather")
    }
    
    @Method
    suspend fun analyzeDemand(timeframe: String): DemandAnalysis {
        val data = getHistoricalData(timeframe)
        
        // NumPy operations converted to Kotlin equivalents
        val trends = KotlinMath.polyfit(data.dates, data.demand, 2)
        val forecast = KotlinMath.poly1d(trends)
        
        return DemandAnalysis(
            trends = trends,
            forecast = forecast(data.dates)
        )
    }
}

TypeScript → Kotlin Transpilation

TypeScript async patterns map to Kotlin coroutines:

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// TypeScript TwinML
@TwinML
class WebIntegrationTwin extends DigitalTwin {
    @Component
    apiGateway = new APIGatewayComponent({
        endpoints: ['/orders', '/inventory', '/customers'],
        rateLimiting: true,
        authentication: 'JWT'
    });
    
    @Method
    async processWebhook(event: WebhookEvent): Promise<WebhookResponse> {
        // Promise-based async code
        try {
            const validation = await this.validateWebhook(event);
            const processing = await this.processEvent(event);
            const response = await this.generateResponse(processing);
            
            return response;
        } catch (error) {
            throw new WebhookProcessingError(error.message);
        }
    }
}

// Transpiles to Kotlin:
@TwinML
class WebIntegrationTwin : DigitalTwin() {
    @Component
    val apiGateway = APIGatewayComponent {
        endpoints = listOf("/orders", "/inventory", "/customers")
        rateLimiting = true
        authentication = AuthenticationType.JWT
    }
    
    @Method
    suspend fun processWebhook(event: WebhookEvent): WebhookResponse {
        return try {
            val validation = validateWebhook(event)
            val processing = processEvent(event) 
            val response = generateResponse(processing)
            response
        } catch (e: Exception) {
            throw WebhookProcessingError(e.message)
        }
    }
}

C# → Kotlin Transpilation

C# async/await patterns convert to Kotlin structured concurrency:

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// C# TwinML
[TwinML]
public class EnterpriseTwin : DigitalTwin
{
    [Component]
    public DatabaseComponent Database { get; set; } = new DatabaseComponent
    {
        ConnectionString = "Server=localhost;Database=Enterprise;",
        PoolSize = 100,
        Timeout = TimeSpan.FromSeconds(30)
    };
    
    [Method]
    public async Task<AnalysisResult> PerformAnalysis(AnalysisRequest request)
    {
        // .NET async patterns
        var dataTask = Database.GetDataAsync(request.DataSet);
        var configTask = GetAnalysisConfig(request.Type);
        
        await Task.WhenAll(dataTask, configTask);
        
        var data = dataTask.Result;
        var config = configTask.Result;
        
        return await RunAnalysis(data, config);
    }
}

// Transpiles to Kotlin:
@TwinML
class EnterpriseTwin : DigitalTwin() {
    @Component
    val database = DatabaseComponent {
        connectionString = "Server=localhost;Database=Enterprise;"
        poolSize = 100
        timeout = Duration.ofSeconds(30)
    }
    
    @Method
    suspend fun performAnalysis(request: AnalysisRequest): AnalysisResult = coroutineScope {
        val dataDeferred = async { database.getData(request.dataSet) }
        val configDeferred = async { getAnalysisConfig(request.type) }
        
        val data = dataDeferred.await()
        val config = configDeferred.await()
        
        runAnalysis(data, config)
    }
}

Runtime Execution Model

Compilation & Loading

  1. Source Analysis: Parse TwinML annotations and extract component definitions
  2. Dependency Resolution: Build component dependency graph
  3. Code Generation: Generate Kotlin execution model
  4. Bytecode Compilation: Compile to JVM bytecode
  5. Dynamic Loading: Load into AMX Engine runtime
  6. Lifecycle Management: Initialize, start, monitor, and stop components

Execution Environment 

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// AMX Engine manages the complete execution lifecycle
class TwinMLExecutionEnvironment {
    private val componentRegistry = ComponentRegistry()
    private val eventBus = EventBus()
    private val lifecycleManager = LifecycleManager()
    
    suspend fun loadTwin(twinDefinition: TwinMLDefinition): DigitalTwin {
        // 1. Parse and validate TwinML
        val components = parseTwinMLComponents(twinDefinition)
        val systems = parseTwinMLSystems(twinDefinition)
        val objects = parseTwinMLObjects(twinDefinition)
        
        // 2. Resolve dependencies
        val dependencyGraph = buildDependencyGraph(components, systems, objects)
        
        // 3. Initialize in dependency order
        val twin = DigitalTwin()
        for (node in dependencyGraph.topologicalSort()) {
            when (node.type) {
                NodeType.COMPONENT -> {
                    val component = componentRegistry.create(node.definition)
                    twin.addComponent(component)
                }
                NodeType.SYSTEM -> {
                    val system = systemRegistry.create(node.definition)
                    twin.addSystem(system)
                }
                NodeType.OBJECT -> {
                    val obj = objectRegistry.create(node.definition)
                    twin.addObject(obj)
                }
            }
        }
        
        // 4. Wire event handlers
        eventBus.wireEventHandlers(twin)
        
        // 5. Start lifecycle management
        lifecycleManager.manage(twin)
        
        return twin
    }
    
    suspend fun executeMethod(
        twin: DigitalTwin,
        componentId: String,
        methodName: String,
        parameters: Map<String, Any>
    ): Any {
        val component = twin.getComponent(componentId)
        val method = component.getMethod(methodName)
        
        return withContext(Dispatchers.Default) {
            method.invoke(parameters)
        }
    }
}

Advanced Features

State Management & Persistence 

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@Component
class PersistentComponent : BaseComponent() {
    // Automatic state persistence
    @State(persistent = true)
    var criticalState: MutableMap<String, Any> = mutableMapOf()
    
    // Snapshot capabilities
    @Method
    suspend fun createSnapshot(): ComponentSnapshot {
        return ComponentSnapshot(
            componentId = id,
            state = criticalState.toMap(),
            timestamp = Instant.now(),
            version = version
        )
    }
    
    @Method
    suspend fun restoreFromSnapshot(snapshot: ComponentSnapshot) {
        criticalState.clear()
        criticalState.putAll(snapshot.state)
        emit(StateRestoredEvent(snapshot.timestamp))
    }
}

Event-Driven Architecture 

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@TwinML
class EventDrivenTwin : DigitalTwin() {
    // Event sourcing
    @EventStore
    val eventStore = EventStore()
    
    // Event handlers with complex routing
    @EventHandler(
        events = [InventoryChangedEvent::class, OrderProcessedEvent::class],
        filter = "event.priority >= Priority.HIGH",
        async = true
    )
    suspend fun handleHighPriorityEvents(event: Event) {
        when (event) {
            is InventoryChangedEvent -> handleInventoryChange(event)
            is OrderProcessedEvent -> handleOrderProcessed(event)
        }
    }
    
    // Event aggregation
    @EventAggregator(
        window = Duration.ofMinutes(5),
        events = [SensorReadingEvent::class]
    )
    suspend fun aggregateSensorData(events: List<SensorReadingEvent>): SensorSummary {
        return SensorSummary(
            average = events.map { it.value }.average(),
            min = events.minOf { it.value },
            max = events.maxOf { it.value },
            count = events.size
        )
    }
}

Performance Optimization 

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@Component
class HighPerformanceComponent : BaseComponent() {
    // Caching with TTL
    @Cache(ttl = Duration.ofMinutes(10))
    suspend fun expensiveComputation(input: String): ComputationResult {
        // Expensive operation that benefits from caching
        return performComplexCalculation(input)
    }
    
    // Batch processing
    @BatchProcessor(
        batchSize = 100,
        maxWaitTime = Duration.ofSeconds(5)
    )
    suspend fun processBatch(items: List<ProcessingItem>): List<ProcessingResult> {
        return items.parallelMap { processItem(it) }
    }
    
    // Resource management
    @ResourceManaged(
        maxConcurrency = 10,
        memoryLimit = "1GB",
        cpuLimit = "2 cores"
    )
    suspend fun resourceIntensiveOperation(data: LargeDataSet): ProcessingResult {
        // Operation with resource constraints
        return processLargeDataSet(data)
    }
}

Integration with AMX Engine

TwinML seamlessly integrates with AMX Engine’s comprehensive processing platform:

ETL Pipeline Integration 

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@System
class DataPipelineSystem : BaseSystem(), ETLCapable {
    @ETLPipeline("customer-data")
    suspend fun processCustomerData(): ETLResult {
        return etl {
            extract {
                from("postgresql://customers")
                from("api://crm-system")
                from("files://csv-imports")
            }
            
            transform {
                clean { removeNulls(); standardizePhoneNumbers() }
                enrich { addGeoLocation(); calculateLifetimeValue() }
                validate { checkEmailFormat(); validateAddresses() }
            }
            
            load {
                to("vector-db://customer-embeddings")
                to("timeseries://customer-events") 
                to("cache://customer-lookup")
            }
        }
    }
}

Webhook Handler Integration 

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@Component
class NotificationComponent : BaseComponent(), WebhookCapable {
    @WebhookHandler(
        platform = "telegram",
        path = "/telegram/updates",
        authentication = WebhookAuth.TOKEN
    )
    suspend fun handleTelegramUpdate(update: TelegramUpdate): TelegramResponse {
        val message = update.message
        val chatId = message.chat.id
        
        // Process the message using TwinML components
        val response = processUserMessage(message.text)
        
        return TelegramResponse.sendMessage(
            chatId = chatId,
            text = response,
            replyMarkup = createKeyboard(response.options)
        )
    }
    
    @WebhookHandler(
        platform = "whatsapp", 
        path = "/whatsapp/webhooks",
        authentication = WebhookAuth.SIGNATURE
    )
    suspend fun handleWhatsAppMessage(message: WhatsAppMessage): WhatsAppResponse {
        // Similar processing for WhatsApp
        return processWhatsAppMessage(message)
    }
}

Formal Language Syntax and Grammar

BNF Grammar Definition

TwinML follows a formal grammar structure that ensures consistent parsing and validation:

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<twin_ml_file> ::= <package_declaration>? <import_declarations>* <twin_definitions>+

<package_declaration> ::= "package" <qualified_name> ";"

<import_declarations> ::= "import" <qualified_name> (".*" | "as" <identifier>)? ";"

<twin_definitions> ::= <component_definition> | <system_definition> | <object_definition> | 
                      <interface_definition> | <digital_twin_definition>

<component_definition> ::= <annotations>* "class" <identifier> <inheritance>? "{" <component_members>* "}"

<system_definition> ::= <annotations>* "class" <identifier> <inheritance>? "{" <system_members>* "}"

<object_definition> ::= <annotations>* "class" <identifier> <inheritance>? "{" <object_members>* "}"

<interface_definition> ::= <annotations>* "interface" <identifier> <inheritance>? "{" <interface_members>* "}"

<digital_twin_definition> ::= <annotations>* "class" <identifier> <inheritance>? "{" <twin_members>* "}"

<annotations> ::= "@" <identifier> ("(" <annotation_parameters>? ")")?

<annotation_parameters> ::= <annotation_parameter> ("," <annotation_parameter>)*

<annotation_parameter> ::= <identifier> "=" <literal_value>

<inheritance> ::= ":" <type_list>

<type_list> ::= <type> ("," <type>)*

<component_members> ::= <property_declaration> | <method_declaration> | <state_declaration> | 
                       <event_handler_declaration> | <lifecycle_declaration>

<property_declaration> ::= <annotations>* <visibility>? "var" <identifier> ":" <type> ("=" <expression>)?

<method_declaration> ::= <annotations>* <visibility>? ("suspend")? "fun" <identifier> 
                        "(" <parameters>? ")" (":" <return_type>)? <method_body>

<state_declaration> ::= <annotations>* <visibility>? "var" <identifier> ":" <type> ("=" <expression>)?

<event_handler_declaration> ::= <annotations>* <visibility>? ("suspend")? "fun" <identifier> 
                               "(" <parameters>? ")" <method_body>

<lifecycle_declaration> ::= <annotations>* <visibility>? ("suspend")? "fun" <identifier> 
                           "(" <parameters>? ")" <method_body>

<parameters> ::= <parameter> ("," <parameter>)*

<parameter> ::= <identifier> ":" <type> ("=" <default_value>)?

<method_body> ::= "{" <statements>* "}" | "=" <expression>

<statements> ::= <expression_statement> | <control_flow> | <declaration>

<control_flow> ::= <if_statement> | <when_statement> | <for_loop> | <while_loop> | <try_catch>

<expression> ::= <assignment> | <logical_or> | <lambda_expression>

<assignment> ::= <postfix_expression> <assignment_operator> <expression>

<logical_or> ::= <logical_and> ("||" <logical_and>)*

<logical_and> ::= <equality> ("&&" <equality>)*

<equality> ::= <comparison> (("==" | "!=") <comparison>)*

<comparison> ::= <additive> (("<" | ">" | "<=" | ">=") <additive>)*

<additive> ::= <multiplicative> (("+" | "-") <multiplicative>)*

<multiplicative> ::= <unary> (("*" | "/" | "%") <unary>)*

<unary> ::= ("+" | "-" | "!" | "++") <postfix_expression> | <postfix_expression>

<postfix_expression> ::= <primary> (<postfix_operation>)*

<postfix_operation> ::= "." <identifier> | "[" <expression> "]" | "(" <arguments>? ")"

<primary> ::= <identifier> | <literal> | "(" <expression> ")" | <this_expression> | <super_expression>

<literal> ::= <number_literal> | <string_literal> | <boolean_literal> | <null_literal> | 
             <collection_literal>

<type> ::= <simple_type> | <nullable_type> | <function_type> | <generic_type>

<simple_type> ::= <qualified_name>

<nullable_type> ::= <type> "?"

<function_type> ::= "(" <type_list>? ")" "->" <type>

<generic_type> ::= <simple_type> "<" <type_list> ">"

<qualified_name> ::= <identifier> ("." <identifier>)*

<identifier> ::= [a-zA-Z_][a-zA-Z0-9_]*

<visibility> ::= "public" | "private" | "protected" | "internal"

Lexical Rules

Keywords 

// Core Language Keywords
class, interface, object, fun, var, val, suspend, async, await
if, else, when, for, while, do, try, catch, finally
return, break, continue, throw, in, is, as, this, super
public, private, protected, internal, abstract, sealed, open, final
true, false, null

// TwinML Specific Keywords
twin, component, system, digital, state, behavior, property
event, handler, lifecycle, simulate, sync, optimize

Operators 

// Arithmetic: +, -, *, /, %, ++, --
// Comparison: ==, !=, <, >, <=, >=, ===, !==
// Logical: &&, ||, !
// Assignment: =, +=, -=, *=, /=, %=
// Range: .., ..< 
// Elvis: ?:
// Safe call: ?.
// Not-null assertion: !!
// Type check/cast: is, !is, as, as?

Literals 

// Integer: 42, 0xFF, 0b1010, 123L
// Floating: 3.14, 2.7E10, 1.23F
// String: "Hello", """Multi-line string"""
// Character: 'a', '\n', '\u0041'
// Boolean: true, false
// Null: null

Type System

Primitive Types 

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// Numeric Types
Int         // 32-bit signed integer (-2^31 to 2^31-1)
Long        // 64-bit signed integer (-2^63 to 2^63-1)
Short       // 16-bit signed integer (-32,768 to 32,767)
Byte        // 8-bit signed integer (-128 to 127)
Float       // 32-bit IEEE 754 floating point
Double      // 64-bit IEEE 754 floating point
UInt        // 32-bit unsigned integer (0 to 2^32-1)
ULong       // 64-bit unsigned integer (0 to 2^64-1)
UShort      // 16-bit unsigned integer (0 to 65,535)
UByte       // 8-bit unsigned integer (0 to 255)

// Other Basic Types
Boolean     // true or false
Char        // 16-bit Unicode character
String      // Sequence of characters (immutable)

// Special Types
Unit        // Singleton type (equivalent to void)
Nothing     // Type with no instances (bottom type)
Any         // Root of type hierarchy (top type)
Any?        // Nullable version of Any

Collection Types 

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// Immutable Collections
List<T>         // Ordered collection
Set<T>          // Unique elements
Map<K, V>       // Key-value pairs
Array<T>        // Fixed-size array

// Mutable Collections
MutableList<T>      // Modifiable list
MutableSet<T>       // Modifiable set
MutableMap<K, V>    // Modifiable map

// Specialized Arrays
IntArray        // Array of primitive ints
DoubleArray     // Array of primitive doubles
BooleanArray    // Array of primitive booleans
// ... other primitive arrays

TwinML Specific Types 

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// Core TwinML Types
ComponentId         // Unique identifier for components
SystemId           // Unique identifier for systems  
ObjectId           // Unique identifier for objects
EventId            // Unique identifier for events
StateId            // Unique identifier for state variables

// Time and Duration Types
Timestamp          // Point in time with nanosecond precision
Duration           // Time interval
TimeWindow         // Time range with start and end
Schedule           // Recurring time pattern

// Measurement Types
Measurement<T>     // Physical measurement with units
Temperature        // Temperature measurement
Pressure          // Pressure measurement
Speed             // Speed/velocity measurement
Distance          // Distance measurement
Mass              // Mass/weight measurement
Energy            // Energy measurement

// Digital Twin Types
TwinState         // Overall state of digital twin
ComponentState    // State of individual component
SystemState       // State of system
Behavior<T>       // Behavioral pattern
Property<T>       // Observable property
Event<T>          // System event
Simulation        // Simulation context

Type Inference and Nullability 

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// Type Inference
val number = 42              // Inferred as Int
val text = "Hello"           // Inferred as String
val list = listOf(1, 2, 3)   // Inferred as List<Int>

// Nullability
var nullable: String? = null     // Can be null
var nonNull: String = "text"     // Cannot be null

// Safe calls and Elvis operator
val length = nullable?.length    // Safe call - returns Int? 
val safeLength = nullable?.length ?: 0  // Elvis - returns Int

// Not-null assertion (use carefully)
val definiteLength = nullable!!.length   // Throws if null

Generic Types 

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// Generic Classes
class Container<T>(val item: T)
class Pair<A, B>(val first: A, val second: B)

// Generic Functions
fun <T> identity(value: T): T = value
fun <T> List<T>.middle(): T? = if (isEmpty()) null else this[size / 2]

// Type Constraints
fun <T : Comparable<T>> max(a: T, b: T): T = if (a > b) a else b

// Variance
interface Producer<out T> {          // Covariant
    fun produce(): T
}

interface Consumer<in T> {           // Contravariant
    fun consume(item: T)
}

interface Processor<T> {             // Invariant
    fun process(input: T): T
}

// Reified Type Parameters (inline functions only)
inline fun <reified T> isInstance(value: Any): Boolean = value is T

State Machine Definitions

Basic State Machine 

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@StateMachine
class OrderStateMachine : BaseStateMachine<OrderState, OrderEvent>() {
    
    // State definitions
    @State
    object Created : OrderState()
    
    @State  
    object PaymentPending : OrderState()
    
    @State
    object PaymentProcessed : OrderState()
    
    @State
    object Shipped : OrderState()
    
    @State
    object Delivered : OrderState()
    
    @State
    object Cancelled : OrderState()
    
    // Event definitions
    @Event
    data class PaymentSubmitted(val amount: BigDecimal) : OrderEvent()
    
    @Event
    data class PaymentConfirmed(val transactionId: String) : OrderEvent()
    
    @Event
    object ShipmentDispatched : OrderEvent()
    
    @Event
    object DeliveryConfirmed : OrderEvent()
    
    @Event
    data class OrderCancelled(val reason: String) : OrderEvent()
    
    // Transition definitions
    @Transitions
    override fun defineTransitions() = transitions {
        from(Created) {
            on<PaymentSubmitted> transitionTo PaymentPending
            on<OrderCancelled> transitionTo Cancelled
        }
        
        from(PaymentPending) {
            on<PaymentConfirmed> transitionTo PaymentProcessed
            on<OrderCancelled> transitionTo Cancelled
        }
        
        from(PaymentProcessed) {
            on<ShipmentDispatched> transitionTo Shipped
            on<OrderCancelled> transitionTo Cancelled
        }
        
        from(Shipped) {
            on<DeliveryConfirmed> transitionTo Delivered
            // Note: Cannot cancel once shipped
        }
        
        // Terminal states
        from(Delivered) {
            // No outgoing transitions
        }
        
        from(Cancelled) {
            // No outgoing transitions  
        }
    }
    
    // State entry/exit actions
    @OnEnter(PaymentPending::class)
    suspend fun onEnterPaymentPending() {
        startPaymentTimeout()
        emit(PaymentProcessingStartedEvent())
    }
    
    @OnExit(PaymentPending::class)
    suspend fun onExitPaymentPending() {
        cancelPaymentTimeout()
    }
    
    // Transition guards
    @Guard
    suspend fun canProcessPayment(event: PaymentSubmitted): Boolean {
        return event.amount > BigDecimal.ZERO && validatePaymentMethod()
    }
    
    // Transition effects
    @Effect
    suspend fun processPayment(event: PaymentSubmitted) {
        paymentService.processPayment(event.amount)
    }
}

Hierarchical State Machine 

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@StateMachine
class VehicleStateMachine : BaseStateMachine<VehicleState, VehicleEvent>() {
    
    // Hierarchical states
    @CompositeState
    class Operational : VehicleState() {
        @State
        object Idle : Operational()
        
        @State  
        object Moving : Operational()
        
        @State
        object Loading : Operational()
        
        @State
        object Unloading : Operational()
    }
    
    @CompositeState
    class Maintenance : VehicleState() {
        @State
        object Scheduled : Maintenance()
        
        @State
        object InProgress : Maintenance()
        
        @State
        object Completed : Maintenance()
    }
    
    @State
    object OutOfService : VehicleState()
    
    // Nested state machine transitions
    @Transitions
    override fun defineTransitions() = transitions {
        // Operational state transitions
        from<Operational> {
            from(Operational.Idle) {
                on<StartMoving> transitionTo Operational.Moving
                on<StartLoading> transitionTo Operational.Loading
            }
            
            from(Operational.Moving) {
                on<StopMoving> transitionTo Operational.Idle
                on<ArrivedAtDestination> transitionTo Operational.Unloading
            }
            
            from(Operational.Loading) {
                on<LoadingComplete> transitionTo Operational.Idle
            }
            
            from(Operational.Unloading) {
                on<UnloadingComplete> transitionTo Operational.Idle
            }
            
            // Any operational state can transition to maintenance
            on<MaintenanceRequired> transitionTo Maintenance.Scheduled
        }
        
        // Maintenance state transitions
        from<Maintenance> {
            from(Maintenance.Scheduled) {
                on<MaintenanceStarted> transitionTo Maintenance.InProgress
            }
            
            from(Maintenance.InProgress) {
                on<MaintenanceCompleted> transitionTo Maintenance.Completed
            }
            
            from(Maintenance.Completed) {
                on<ReturnToService> transitionTo Operational.Idle
            }
        }
        
        // Critical failures from any state
        fromAny {
            on<CriticalFailure> transitionTo OutOfService
        }
    }
    
    // Composite state actions
    @OnEnter(Operational::class)
    suspend fun onEnterOperational() {
        enableOperationalSystems()
        startLocationTracking()
    }
    
    @OnExit(Operational::class)  
    suspend fun onExitOperational() {
        stopLocationTracking()
        disableOperationalSystems()
    }
}

Parallel State Machine 

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@StateMachine
class ManufacturingLineStateMachine : BaseStateMachine<LineState, LineEvent>() {
    
    // Parallel regions
    @ParallelRegion("production")
    val productionRegion = ProductionStateMachine()
    
    @ParallelRegion("quality")
    val qualityRegion = QualityControlStateMachine()
    
    @ParallelRegion("maintenance")
    val maintenanceRegion = MaintenanceStateMachine()
    
    // Synchronization points
    @SyncState
    object AllSystemsReady : LineState() {
        @SyncCondition
        fun isReady(): Boolean {
            return productionRegion.currentState is Production.Ready &&
                   qualityRegion.currentState is QualityControl.Ready &&
                   maintenanceRegion.currentState is Maintenance.Operational
        }
    }
    
    // Cross-region events
    @CrossRegionEvent
    data class EmergencyStop(val reason: String) : LineEvent()
    
    @Transitions
    override fun defineTransitions() = transitions {
        // Synchronized start
        from(AllSystemsReady) {
            on<StartProduction> {
                transitionTo ParallelState {
                    productionRegion transitionTo Production.Running
                    qualityRegion transitionTo QualityControl.Active
                    maintenanceRegion transitionTo Maintenance.Monitoring
                }
            }
        }
        
        // Emergency stop affects all regions
        fromAny {
            on<EmergencyStop> {
                transitionTo ParallelState {
                    productionRegion transitionTo Production.Stopped
                    qualityRegion transitionTo QualityControl.Suspended  
                    maintenanceRegion transitionTo Maintenance.Emergency
                }
            }
        }
    }
}

Behavior Specifications

Reactive Behaviors 

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@Behavior
class TemperatureControlBehavior : ReactiveBehavior<TemperatureEvent, ControlAction>() {
    
    @Property
    var targetTemperature: Temperature = Temperature.celsius(20.0)
    
    @Property  
    var tolerance: Temperature = Temperature.celsius(1.0)
    
    @Property
    var pidController: PIDController = PIDController(kp = 1.0, ki = 0.1, kd = 0.05)
    
    // Reactive rules
    @ReactiveRule(priority = HIGH)
    suspend fun onTemperatureReading(event: TemperatureReadingEvent): ControlAction? {
        val current = event.temperature
        val error = targetTemperature - current
        
        return when {
            error.abs() <= tolerance -> {
                // Within tolerance - maintain current state
                ControlAction.Maintain
            }
            error > Temperature.ZERO -> {
                // Too cold - increase heating
                val output = pidController.calculate(error.celsius, event.timestamp)
                ControlAction.Heat(output.coerceIn(0.0, 100.0))
            }
            else -> {
                // Too hot - increase cooling
                val output = pidController.calculate(error.celsius, event.timestamp)
                ControlAction.Cool(output.abs().coerceIn(0.0, 100.0))
            }
        }
    }
    
    @ReactiveRule(priority = CRITICAL)
    suspend fun onTemperatureAlarm(event: TemperatureAlarmEvent): ControlAction {
        return when (event.type) {
            AlarmType.HIGH_TEMPERATURE -> ControlAction.EmergencyCooling
            AlarmType.LOW_TEMPERATURE -> ControlAction.EmergencyHeating
            AlarmType.SENSOR_FAILURE -> ControlAction.SafeShutdown
        }
    }
    
    // Behavior lifecycle
    @OnActivate
    suspend fun activateTemperatureControl() {
        pidController.reset()
        emit(BehaviorActivatedEvent(this::class.simpleName))
    }
    
    @OnDeactivate
    suspend fun deactivateTemperatureControl() {
        emit(BehaviorDeactivatedEvent(this::class.simpleName))
    }
}

Predictive Behaviors 

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@Behavior
class DemandPredictionBehavior : PredictiveBehavior<SalesData, DemandForecast>() {
    
    @Property
    var forecastHorizon: Duration = Duration.ofDays(30)
    
    @Property
    var confidenceThreshold: Double = 0.85
    
    @MLModel
    var neuralNetwork: NeuralNetwork = NeuralNetwork {
        layers {
            input(48)      // 48 features (sales history, seasonality, etc.)
            hidden(128) { activation = ReLU }
            hidden(64)  { activation = ReLU }  
            hidden(32)  { activation = ReLU }
            output(7)      // 7-day forecast
        }
        
        optimizer = Adam(learningRate = 0.001)
        loss = MeanSquaredError
    }
    
    @PredictiveRule
    suspend fun predictDemand(historicalData: List<SalesData>): DemandForecast {
        // Feature engineering
        val features = extractFeatures(historicalData)
        
        // Generate prediction
        val prediction = neuralNetwork.predict(features)
        val confidence = calculateConfidence(prediction, historicalData)
        
        // Create forecast
        return DemandForecast(
            horizon = forecastHorizon,
            predictions = prediction.toList(),
            confidence = confidence,
            timestamp = Instant.now()
        )
    }
    
    @FeatureExtractor
    private fun extractFeatures(data: List<SalesData>): DoubleArray {
        return doubleArrayOf(
            // Trend features
            *calculateTrend(data),
            
            // Seasonality features  
            *calculateSeasonality(data),
            
            // Statistical features
            data.map { it.quantity }.average(),
            data.map { it.quantity }.standardDeviation(),
            
            // External factors
            *getWeatherFeatures(),
            *getEconomicIndicators(),
            *getMarketingCampaignFeatures()
        )
    }
    
    @ModelUpdate(frequency = "daily")
    suspend fun retrainModel() {
        val trainingData = getTrainingData(Duration.ofDays(365))
        val validationData = getValidationData(Duration.ofDays(30))
        
        neuralNetwork.train(
            trainingData = trainingData,
            validationData = validationData,
            epochs = 100,
            batchSize = 32
        )
        
        val accuracy = validateModel(validationData)
        emit(ModelUpdatedEvent(accuracy))
    }
}

Adaptive Behaviors 

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@Behavior
class QualityControlBehavior : AdaptiveBehavior<QualityMetrics, QualityAction>() {
    
    @Property
    var qualityThreshold: Double = 0.95
    
    @Property
    var adaptationRate: Double = 0.01
    
    @State
    private var performanceHistory: CircularBuffer<QualityMetrics> = CircularBuffer(1000)
    
    @AdaptiveRule
    suspend fun adaptQualityThreshold(metrics: QualityMetrics): QualityAction {
        performanceHistory.add(metrics)
        
        // Analyze recent performance
        val recentPerformance = performanceHistory.last(100)
        val averageQuality = recentPerformance.map { it.qualityScore }.average()
        val defectRate = recentPerformance.map { it.defectRate }.average()
        
        // Adaptive adjustment
        val newThreshold = when {
            defectRate < 0.01 -> {
                // Very low defect rate - can be less strict
                qualityThreshold - adaptationRate
            }
            defectRate > 0.05 -> {
                // High defect rate - be more strict
                qualityThreshold + adaptationRate
            }
            else -> qualityThreshold // Keep current threshold
        }
        
        qualityThreshold = newThreshold.coerceIn(0.80, 0.99)
        
        // Determine action based on current metrics
        return when {
            metrics.qualityScore < qualityThreshold -> QualityAction.Reject
            metrics.qualityScore < qualityThreshold + 0.02 -> QualityAction.Inspect  
            else -> QualityAction.Accept
        }
    }
    
    @LearningRule
    suspend fun learnFromOutcomes(outcome: QualityOutcome) {
        // Update behavior based on real-world outcomes
        when (outcome.result) {
            OutcomeResult.FALSE_POSITIVE -> {
                // We accepted something that was actually defective
                // Increase strictness
                qualityThreshold += adaptationRate * 2
            }
            OutcomeResult.FALSE_NEGATIVE -> {
                // We rejected something that was actually good
                // Decrease strictness  
                qualityThreshold -= adaptationRate
            }
            OutcomeResult.TRUE_POSITIVE,
            OutcomeResult.TRUE_NEGATIVE -> {
                // Correct decision - no adjustment needed
            }
        }
        
        emit(BehaviorAdaptedEvent(qualityThreshold))
    }
}

Event Handling

Event Definitions 

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// Base event types
@EventType
sealed class SystemEvent {
    abstract val timestamp: Instant
    abstract val eventId: EventId
    abstract val sourceId: ComponentId
}

@EventType
data class ComponentStateChangedEvent(
    override val timestamp: Instant = Instant.now(),
    override val eventId: EventId = EventId.generate(),
    override val sourceId: ComponentId,
    val previousState: ComponentState,
    val newState: ComponentState,
    val reason: String? = null
) : SystemEvent()

@EventType
data class PropertyChangedEvent<T>(
    override val timestamp: Instant = Instant.now(),
    override val eventId: EventId = EventId.generate(), 
    override val sourceId: ComponentId,
    val propertyName: String,
    val previousValue: T,
    val newValue: T
) : SystemEvent()

@EventType
data class MethodInvokedEvent(
    override val timestamp: Instant = Instant.now(),
    override val eventId: EventId = EventId.generate(),
    override val sourceId: ComponentId,
    val methodName: String,
    val parameters: Map<String, Any>,
    val executionTime: Duration
) : SystemEvent()

// Domain-specific events
@EventType
data class OrderProcessedEvent(
    override val timestamp: Instant = Instant.now(),
    override val eventId: EventId = EventId.generate(),
    override val sourceId: ComponentId,
    val orderId: String,
    val customerId: String,
    val totalAmount: BigDecimal,
    val items: List<OrderItem>
) : SystemEvent()

Event Handlers 

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@Component
class OrderProcessingComponent : BaseComponent() {
    
    // Simple event handler
    @EventHandler
    suspend fun handleOrderCreated(event: OrderCreatedEvent) {
        logger.info("Processing new order: ${event.orderId}")
        
        // Validate order
        val validation = validateOrder(event.order)
        if (!validation.isValid) {
            emit(OrderRejectedEvent(event.orderId, validation.errors))
            return
        }
        
        // Process order
        val result = processOrder(event.order)
        emit(OrderProcessedEvent(
            orderId = event.orderId,
            customerId = event.order.customerId,
            totalAmount = event.order.totalAmount,
            items = event.order.items
        ))
    }
    
    // Conditional event handler
    @EventHandler(
        condition = "event.totalAmount > 1000.0",
        priority = EventPriority.HIGH
    )
    suspend fun handleHighValueOrder(event: OrderCreatedEvent) {
        // Special handling for high-value orders
        logger.info("High-value order detected: ${event.orderId}")
        
        // Require additional verification
        val verification = requireManagerApproval(event.order)
        if (!verification.approved) {
            emit(OrderHoldEvent(event.orderId, "Manager approval required"))
            return
        }
    }
    
    // Filtered event handler
    @EventHandler(
        filter = "event.customerId in premiumCustomers",
        async = true
    )
    suspend fun handlePremiumCustomerOrder(event: OrderCreatedEvent) {
        // Priority processing for premium customers
        priorityQueue.add(event.order)
        emit(PriorityProcessingEvent(event.orderId))
    }
    
    // Error handling
    @EventHandler
    @ErrorHandler(
        retries = 3,
        backoff = ExponentialBackoff(initialDelay = 1000, multiplier = 2.0)
    )
    suspend fun handlePaymentProcessed(event: PaymentProcessedEvent) {
        try {
            fulfillOrder(event.orderId)
            emit(OrderFulfilledEvent(event.orderId))
        } catch (e: InventoryException) {
            emit(InventoryShortageEvent(event.orderId, e.missingItems))
            throw e // Will be retried
        } catch (e: Exception) {
            emit(OrderFulfillmentFailedEvent(event.orderId, e.message))
            // Don't rethrow - won't be retried
        }
    }
}

Event Aggregation 

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@EventAggregator
class SalesAnalyticsAggregator : BaseEventAggregator() {
    
    // Time-based aggregation
    @Aggregate(
        window = Duration.ofHours(1),
        events = [OrderProcessedEvent::class]
    )
    suspend fun aggregateHourlySales(events: List<OrderProcessedEvent>): HourlySalesSummary {
        return HourlySalesSummary(
            timestamp = Instant.now().truncatedTo(ChronoUnit.HOURS),
            totalOrders = events.size,
            totalRevenue = events.sumOf { it.totalAmount },
            averageOrderValue = events.map { it.totalAmount }.average().toBigDecimal(),
            topSellingItems = findTopSellingItems(events.flatMap { it.items })
        )
    }
    
    // Count-based aggregation  
    @Aggregate(
        count = 100,
        events = [CustomerInteractionEvent::class]
    )
    suspend fun analyzeCustomerBehavior(events: List<CustomerInteractionEvent>): CustomerBehaviorAnalysis {
        val uniqueCustomers = events.map { it.customerId }.toSet()
        val interactionTypes = events.groupBy { it.interactionType }
        
        return CustomerBehaviorAnalysis(
            uniqueCustomers = uniqueCustomers.size,
            totalInteractions = events.size,
            interactionsByType = interactionTypes.mapValues { it.value.size },
            averageSessionDuration = events.map { it.sessionDuration }.average()
        )
    }
    
    // Complex aggregation with multiple event types
    @Aggregate(
        window = Duration.ofDays(1),
        events = [OrderProcessedEvent::class, OrderCancelledEvent::class, OrderReturnedEvent::class]
    )
    suspend fun generateDailyReport(events: List<SystemEvent>): DailySalesReport {
        val processed = events.filterIsInstance<OrderProcessedEvent>()
        val cancelled = events.filterIsInstance<OrderCancelledEvent>()
        val returned = events.filterIsInstance<OrderReturnedEvent>()
        
        return DailySalesReport(
            date = LocalDate.now(),
            ordersProcessed = processed.size,
            ordersCancelled = cancelled.size,
            ordersReturned = returned.size,
            grossRevenue = processed.sumOf { it.totalAmount },
            refunds = returned.sumOf { it.refundAmount },
            netRevenue = processed.sumOf { it.totalAmount } - returned.sumOf { it.refundAmount },
            cancellationRate = cancelled.size.toDouble() / (processed.size + cancelled.size),
            returnRate = returned.size.toDouble() / processed.size
        )
    }
}

Simulation Directives

Basic Simulation 

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@Simulation
class SupplyChainSimulation : BaseSimulation() {
    
    @SimulationParameter
    var simulationDuration: Duration = Duration.ofDays(30)
    
    @SimulationParameter
    var timeAcceleration: Double = 10.0  // 10x real time
    
    @SimulationParameter
    var randomSeed: Long = 12345
    
    // Simulation components
    @SimulatedComponent
    val warehouse = WarehouseComponent {
        capacity = 10000
        initialInventory = mapOf(
            "Product_A" to 500,
            "Product_B" to 300,
            "Product_C" to 200
        )
    }
    
    @SimulatedComponent
    val customers = CustomerGeneratorComponent {
        arrivalRate = ExponentialDistribution(mean = 2.0) // 2 customers per hour
        orderSizeDistribution = NormalDistribution(mean = 3.0, stddev = 1.0)
        productPreferences = mapOf(
            "Product_A" to 0.5,
            "Product_B" to 0.3,
            "Product_C" to 0.2
        )
    }
    
    @SimulatedComponent
    val suppliers = SupplierComponent {
        leadTime = UniformDistribution(min = 2.0, max = 7.0) // 2-7 days
        reliability = 0.95  // 95% on-time delivery
        minimumOrderQuantity = 100
    }
    
    // Scenario definitions
    @Scenario("normal_operations")
    suspend fun normalOperationsScenario() {
        // Standard operating conditions
        customers.setArrivalRate(2.0)
        suppliers.setReliability(0.95)
        warehouse.setAutomationLevel(AutomationLevel.SEMI_AUTOMATED)
    }
    
    @Scenario("high_demand")  
    suspend fun highDemandScenario() {
        // Black Friday / holiday rush simulation
        customers.setArrivalRate(10.0)  // 5x normal demand
        warehouse.addTemporaryWorkers(20)
        suppliers.enableExpressDelivery(true)
    }
    
    @Scenario("supply_disruption")
    suspend fun supplyDisruptionScenario() {
        // Simulate supply chain disruption
        suppliers.setReliability(0.60)  // Reduced reliability
        suppliers.setLeadTime(UniformDistribution(min = 7.0, max = 21.0))
        
        // Activate contingency suppliers
        val contingencySupplier = SupplierComponent {
            leadTime = UniformDistribution(min = 1.0, max = 3.0)
            reliability = 0.99
            costMultiplier = 1.5  // More expensive but reliable
        }
        addSimulationComponent(contingencySupplier)
    }
    
    // Simulation events
    @SimulationEvent(time = Duration.ofDays(15))
    suspend fun midSimulationEvent() {
        // Warehouse expansion after 15 days
        warehouse.expandCapacity(2000)
        emit(WarehouseExpandedEvent(warehouse.capacity))
    }
    
    @SimulationEvent(time = Duration.ofDays(20))
    suspend fun introducenewProduct() {
        // New product introduction
        warehouse.addProduct("Product_D", initialStock = 1000)
        customers.addProductPreference("Product_D", 0.15)
    }
    
    // Metrics collection
    @Metrics
    suspend fun collectMetrics(): SimulationMetrics {
        return SimulationMetrics(
            timestamp = getCurrentSimulationTime(),
            warehouseUtilization = warehouse.getUtilizationRate(),
            averageOrderFulfillmentTime = calculateAverageOrderFulfillmentTime(),
            stockoutFrequency = calculateStockoutFrequency(),
            customerSatisfaction = calculateCustomerSatisfaction(),
            totalCost = calculateTotalCost(),
            totalRevenue = calculateTotalRevenue()
        )
    }
    
    // Simulation lifecycle
    @OnSimulationStart
    suspend fun initializeSimulation() {
        logger.info("Starting supply chain simulation")
        random = Random(randomSeed)
        metricsCollector.start()
    }
    
    @OnSimulationEnd
    suspend fun finalizeSimulation() {
        val finalMetrics = collectMetrics()
        val report = generateSimulationReport(finalMetrics)
        
        logger.info("Simulation completed: $report")
        persistSimulationResults(report)
    }
}

Monte Carlo Simulation 

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@MonteCarloSimulation(runs = 1000)
class RiskAnalysisSimulation : BaseSimulation() {
    
    @StochasticParameter
    val demandVariability = NormalDistribution(mean = 100.0, stddev = 25.0)
    
    @StochasticParameter
    val supplyDelays = ExponentialDistribution(rate = 0.1)
    
    @StochasticParameter
    val marketPrice = LogNormalDistribution(mu = 4.6, sigma = 0.2)
    
    @MonteCarloRun
    suspend fun runSingleSimulation(): SimulationResult {
        // Sample from distributions
        val demand = demandVariability.sample()
        val delay = supplyDelays.sample()
        val price = marketPrice.sample()
        
        // Run simulation with sampled parameters
        val revenue = calculateRevenue(demand, price)
        val cost = calculateCost(demand, delay)
        val profit = revenue - cost
        
        return SimulationResult(
            demand = demand,
            delay = delay,
            price = price,
            revenue = revenue,
            cost = cost,
            profit = profit
        )
    }
    
    @ResultAnalysis
    suspend fun analyzeResults(results: List<SimulationResult>): RiskAnalysis {
        val profits = results.map { it.profit }
        
        return RiskAnalysis(
            expectedProfit = profits.average(),
            profitStdDev = profits.standardDeviation(),
            valueAtRisk95 = profits.percentile(0.05),  // 95% VaR
            conditionalVaR95 = profits.filter { it <= profits.percentile(0.05) }.average(),
            probabilityOfLoss = profits.count { it < 0 }.toDouble() / profits.size,
            worstCase = profits.minOrNull() ?: 0.0,
            bestCase = profits.maxOrNull() ?: 0.0
        )
    }
}

Agent-Based Simulation 

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@AgentBasedSimulation
class MarketSimulation : BaseSimulation() {
    
    // Agent types
    @Agent
    class Buyer : SimulationAgent() {
        @Property
        var budget: Double = 1000.0
        
        @Property
        var pricePreference: Double = 50.0
        
        @Behavior
        suspend fun makePurchaseDecision(): PurchaseDecision {
            val availableProducts = getMarketProducts()
            val affordableProducts = availableProducts.filter { it.price <= budget }
            
            if (affordableProducts.isEmpty()) {
                return PurchaseDecision.WaitForBetterPrice
            }
            
            val bestProduct = affordableProducts.minByOrNull { 
                abs(it.price - pricePreference) 
            }
            
            return if (bestProduct != null) {
                budget -= bestProduct.price
                PurchaseDecision.Buy(bestProduct)
            } else {
                PurchaseDecision.WaitForBetterPrice
            }
        }
    }
    
    @Agent
    class Seller : SimulationAgent() {
        @Property
        var inventory: Int = 100
        
        @Property
        var basePrice: Double = 45.0
        
        @Property
        var priceAdjustmentRate: Double = 0.1
        
        @Behavior
        suspend fun adjustPrice(): Double {
            val marketDemand = getMarketDemand()
            val competitorPrices = getCompetitorPrices()
            
            // Simple pricing strategy
            val avgCompetitorPrice = competitorPrices.average()
            val demandFactor = if (marketDemand > inventory) 1.1 else 0.9
            
            basePrice = (basePrice * (1 - priceAdjustmentRate) + 
                        avgCompetitorPrice * priceAdjustmentRate) * demandFactor
            
            return basePrice
        }
        
        @Behavior
        suspend fun restockInventory() {
            if (inventory < 10) {
                inventory += 50  // Restock
                emit(RestockEvent(agentId, 50))
            }
        }
    }
    
    // Population definitions
    @Population(size = 100)
    val buyers = generatePopulation<Buyer> {
        budget = NormalDistribution(1000.0, 200.0).sample()
        pricePreference = NormalDistribution(50.0, 15.0).sample()
    }
    
    @Population(size = 10)
    val sellers = generatePopulation<Seller> {
        inventory = UniformDistribution(50.0, 150.0).sample().toInt()
        basePrice = UniformDistribution(40.0, 60.0).sample()
    }
    
    // Market mechanisms
    @MarketMechanism
    suspend fun conductTradingSession() {
        // Collect all buy and sell orders
        val buyOrders = buyers.mapNotNull { it.makePurchaseDecision() }
        val sellPrices = sellers.map { it.adjustPrice() }
        
        // Match orders (simplified)
        for (buyOrder in buyOrders.filterIsInstance<PurchaseDecision.Buy>()) {
            val availableSellers = sellers.filter { 
                it.inventory > 0 && it.basePrice <= buyOrder.product.price 
            }
            
            if (availableSellers.isNotEmpty()) {
                val seller = availableSellers.minByOrNull { it.basePrice }!!
                seller.inventory--
                emit(TransactionEvent(buyOrder.product, seller.basePrice))
            }
        }
    }
    
    @SimulationStep
    suspend fun runTimeStep() {
        // Update all agents
        buyers.forEach { it.step() }
        sellers.forEach { it.step() }
        
        // Run market mechanism
        conductTradingSession()
        
        // Collect metrics
        collectMarketMetrics()
    }
}

This completes the comprehensive TwinML specification, demonstrating how it provides object-oriented modeling capabilities while integrating seamlessly with AMX Engine’s comprehensive processing platform. The language enables developers to create sophisticated digital systems using familiar programming paradigms while leveraging the full power of AMX Engine’s runtime capabilities.

Product ArchitectureUR² Framework Integration Specification