AI統合OODAループ：次世代意思決定フレームワーク¶

// AI統合観察システム
interface AIObservationResult {
  timestamp: Date;
  rawData: MultiModalData;
  processedInsights: ProcessedInsight[];
  anomalies: DetectedAnomaly[];
  trends: IdentifiedTrend[];
  confidence: number;
  dataQuality: DataQualityMetrics;
}

interface MultiModalData {
  sensorData: SensorReading[];
  imageData: ImageAnalysis[];
  textData: TextAnalysis[];
  audioData: AudioAnalysis[];
  networkData: NetworkMetrics[];
  userBehaviorData: UserActivity[];
}

class AIObservationSystem {
  private dataStreams: Map<string, Observable<any>> = new Map();
  private fusionEngine: DataFusionEngine;
  private anomalyDetector: AnomalyDetectionEngine;
  private trendAnalyzer: TrendAnalysisEngine;
  private qualityAssessor: DataQualityAssessor;

  constructor() {
    this.fusionEngine = new MultiModalDataFusion();
    this.anomalyDetector = new RealTimeAnomalyDetection();
    this.trendAnalyzer = new StatisticalTrendAnalysis();
    this.qualityAssessor = new DataQualityAssessment();
    this.initializeDataStreams();
  }

  private initializeDataStreams(): void {
    // IoTセンサーストリーム
    this.dataStreams.set('sensors', 
      this.createSensorStream().pipe(
        map(data => this.preprocessSensorData(data)),
        share()
      )
    );

    // 画像解析ストリーム
    this.dataStreams.set('vision',
      this.createVisionStream().pipe(
        map(data => this.processImageData(data)),
        share()
      )
    );

    // テキスト分析ストリーム
    this.dataStreams.set('text',
      this.createTextStream().pipe(
        debounceTime(1000),
        map(data => this.processTextData(data)),
        share()
      )
    );

    // 音声分析ストリーム
    this.dataStreams.set('audio',
      this.createAudioStream().pipe(
        bufferTime(5000),
        map(buffer => this.processAudioData(buffer)),
        share()
      )
    );
  }

  // 統合観察の実行
  performAIObservation(): Observable<AIObservationResult> {
    return combineLatest([
      this.dataStreams.get('sensors')!,
      this.dataStreams.get('vision')!,
      this.dataStreams.get('text')!,
      this.dataStreams.get('audio')!
    ]).pipe(
      map(([sensorData, visionData, textData, audioData]) => {
        const multiModalData: MultiModalData = {
          sensorData,
          imageData: visionData,
          textData,
          audioData,
          networkData: this.getCurrentNetworkMetrics(),
          userBehaviorData: this.getCurrentUserActivity()
        };

        return multiModalData;
      }),

      // データ品質評価
      map(data => ({
        data,
        quality: this.qualityAssessor.assess(data)
      })),

      // データ融合
      map(({ data, quality }) => ({
        fusedData: this.fusionEngine.fuse(data),
        quality
      })),

      // 洞察抽出
      map(({ fusedData, quality }) => {
        const insights = this.extractInsights(fusedData);
        const anomalies = this.anomalyDetector.detect(fusedData);
        const trends = this.trendAnalyzer.analyze(fusedData);

        return {
          timestamp: new Date(),
          rawData: fusedData,
          processedInsights: insights,
          anomalies: anomalies,
          trends: trends,
          confidence: this.calculateConfidence(quality, insights),
          dataQuality: quality
        };
      }),

      share()
    );
  }

  // リアルタイム異常検知
  private createAnomalyAlert(): Observable<AnomalyAlert> {
    return this.performAIObservation().pipe(
      filter(result => result.anomalies.length > 0),
      map(result => ({
        severity: this.calculateSeverity(result.anomalies),
        anomalies: result.anomalies,
        recommendedActions: this.generateAnomalyActions(result.anomalies),
        confidence: result.confidence
      }))
    );
  }

  // 予測的観察（次に何を観察すべきかAIが決定）
  async predictiveObservation(currentContext: ObservationContext): Promise<ObservationStrategy> {
    const historicalPatterns = await this.analyzeHistoricalPatterns(currentContext);
    const predictiveModel = await this.loadPredictiveModel();

    const nextObservationPoints = await predictiveModel.predict({
      context: currentContext,
      patterns: historicalPatterns,
      currentState: await this.getCurrentSystemState()
    });

    return {
      priorityTargets: nextObservationPoints,
      observationFrequency: this.calculateOptimalFrequency(nextObservationPoints),
      resourceAllocation: this.optimizeResourceAllocation(nextObservationPoints)
    };
  }
}

// AI統合状況分析システム
interface AIOrientationResult {
  situationAssessment: SituationAssessment;
  contextualAnalysis: ContextualAnalysis;
  predictiveInsights: PredictiveInsight[];
  riskEvaluation: RiskEvaluation;
  opportunityIdentification: OpportunityAnalysis;
  confidence: number;
  recommendedActions: string[];
}

interface SituationAssessment {
  currentState: SystemState;
  stateClassification: string;
  stateProbability: number;
  keyFactors: Factor[];
  changeVelocity: number;
  stabilityIndex: number;
}

class AIOrientationSystem {
  private mlModels: Map<string, MLModel> = new Map();
  private knowledgeBase: KnowledgeBase;
  private contextAnalyzer: ContextualAnalyzer;
  private riskAssessor: RiskAssessmentEngine;
  private opportunityFinder: OpportunityDetector;

  constructor() {
    this.initializeMLModels();
    this.knowledgeBase = new DomainKnowledgeBase();
    this.contextAnalyzer = new AdvancedContextAnalyzer();
    this.riskAssessor = new ProbabilisticRiskAssessment();
    this.opportunityFinder = new PatternBasedOpportunityDetector();
  }

  private initializeMLModels(): void {
    // 状況分類モデル
    this.mlModels.set('classifier', new EnsembleClassifier({
      models: [
        new RandomForestClassifier(),
        new GradientBoostingClassifier(),
        new NeuralNetworkClassifier()
      ]
    }));

    // 時系列予測モデル
    this.mlModels.set('predictor', new LSTMPredictor({
      sequenceLength: 50,
      features: ['system_state', 'external_factors', 'historical_trends']
    }));

    // 異常検知モデル
    this.mlModels.set('anomaly', new IsolationForestDetector({
      contamination: 0.1,
      randomState: 42
    }));
  }

  // AI状況分析の実行
  async performAIOrientation(
    observationData: AIObservationResult
  ): Promise<AIOrientationResult> {
    // 1. 状況分類
    const situationAssessment = await this.assessCurrentSituation(observationData);

    // 2. 文脈分析
    const contextualAnalysis = await this.analyzeContext(
      observationData,
      situationAssessment
    );

    // 3. 予測分析
    const predictiveInsights = await this.generatePredictiveInsights(
      observationData,
      contextualAnalysis
    );

    // 4. リスク評価
    const riskEvaluation = await this.evaluateRisks(
      situationAssessment,
      predictiveInsights
    );

    // 5. 機会分析
    const opportunityIdentification = await this.identifyOpportunities(
      contextualAnalysis,
      predictiveInsights
    );

    // 6. 信頼度計算
    const confidence = this.calculateOrientationConfidence([
      situationAssessment,
      contextualAnalysis,
      predictiveInsights
    ]);

    // 7. 推奨行動生成
    const recommendedActions = await this.generateRecommendations({
      situation: situationAssessment,
      risks: riskEvaluation,
      opportunities: opportunityIdentification,
      context: contextualAnalysis
    });

    return {
      situationAssessment,
      contextualAnalysis,
      predictiveInsights,
      riskEvaluation,
      opportunityIdentification,
      confidence,
      recommendedActions
    };
  }

  // 動的学習・適応機能
  async adaptToNewPattern(
    newData: ObservationData[],
    feedback: HumanFeedback[]
  ): Promise<AdaptationResult> {
    // 概念ドリフト検出
    const driftDetection = await this.detectConceptDrift(newData);

    if (driftDetection.driftDetected) {
      // モデル再訓練
      const retrainingResult = await this.retrainModels(
        newData,
        feedback,
        driftDetection.driftType
      );

      // 性能評価
      const performanceMetrics = await this.evaluateAdaptedModels();

      return {
        adaptationPerformed: true,
        driftType: driftDetection.driftType,
        retrainingResult,
        performanceImprovement: performanceMetrics.improvement,
        newModelConfidence: performanceMetrics.confidence
      };
    }

    return { adaptationPerformed: false };
  }

  // 説明可能性機能
  async explainOrientation(
    orientationResult: AIOrientationResult
  ): Promise<OrientationExplanation> {
    // SHAP値による特徴量重要度
    const featureImportance = await this.calculateFeatureImportance(
      orientationResult.situationAssessment
    );

    // 決定木による論理フロー
    const decisionLogic = await this.extractDecisionLogic(orientationResult);

    // 自然言語による説明生成
    const naturalLanguageExplanation = this.generateNLExplanation(
      orientationResult,
      featureImportance,
      decisionLogic
    );

    return {
      featureImportance,
      decisionLogic,
      naturalLanguageExplanation,
      confidence: orientationResult.confidence,
      keyInsights: this.extractKeyInsights(orientationResult)
    };
  }
}

// AI統合意思決定システム
interface AIDecisionResult {
  optimalDecision: Decision;
  alternativeDecisions: Decision[];
  decisionRationale: DecisionRationale;
  riskAssessment: DecisionRiskAssessment;
  expectedOutcome: OutcomePrediction;
  confidence: number;
  executionPlan: ExecutionPlan;
}

interface Decision {
  id: string;
  action: ActionPlan;
  priority: number;
  resources: ResourceRequirement[];
  timeline: ExecutionTimeline;
  successProbability: number;
  riskLevel: RiskLevel;
}

class AIDecisionSystem {
  private optimizationEngine: OptimizationEngine;
  private scenarioGenerator: ScenarioGenerator;
  private riskCalculator: RiskCalculator;
  private outcomePredictor: OutcomePredictor;
  private constraintManager: ConstraintManager;

  constructor() {
    this.optimizationEngine = new MultiObjectiveOptimizer();
    this.scenarioGenerator = new MonteCarloScenarioGenerator();
    this.riskCalculator = new ProbabilisticRiskCalculator();
    this.outcomePredictor = new EnsembleOutcomePredictor();
    this.constraintManager = new DynamicConstraintManager();
  }

  // AI意思決定の実行
  async performAIDecision(
    orientationResult: AIOrientationResult,
    availableActions: ActionOption[],
    constraints: DecisionConstraint[]
  ): Promise<AIDecisionResult> {
    // 1. 制約条件の設定
    await this.constraintManager.setConstraints(constraints);

    // 2. 決定空間の定義
    const decisionSpace = this.defineDecisionSpace(
      availableActions,
      orientationResult.situationAssessment
    );

    // 3. 目的関数の構築
    const objectiveFunctions = this.buildObjectiveFunctions(
      orientationResult.riskEvaluation,
      orientationResult.opportunityIdentification
    );

    // 4. シナリオ生成
    const scenarios = await this.scenarioGenerator.generate({
      baseState: orientationResult.situationAssessment,
      numScenarios: 1000,
      timeHorizon: '24hours'
    });

    // 5. 最適化実行
    const optimizationResult = await this.optimizationEngine.optimize({
      decisionSpace,
      objectiveFunctions,
      constraints,
      scenarios
    });

    // 6. 結果の評価と検証
    const evaluatedDecisions = await this.evaluateDecisions(
      optimizationResult.solutions,
      scenarios
    );

    // 7. 最適解の選択
    const optimalDecision = this.selectOptimalDecision(evaluatedDecisions);

    // 8. 代替案の準備
    const alternativeDecisions = this.prepareAlternatives(
      evaluatedDecisions,
      optimalDecision
    );

    // 9. 実行計画の生成
    const executionPlan = await this.generateExecutionPlan(
      optimalDecision,
      orientationResult.situationAssessment
    );

    return {
      optimalDecision,
      alternativeDecisions,
      decisionRationale: this.generateRationale(optimalDecision, objectiveFunctions),
      riskAssessment: await this.assessDecisionRisk(optimalDecision, scenarios),
      expectedOutcome: await this.predictOutcome(optimalDecision, scenarios),
      confidence: this.calculateDecisionConfidence(optimizationResult),
      executionPlan
    };
  }

  // リアルタイム決定調整
  adjustDecisionRealTime(
    currentDecision: Decision,
    newObservations: Observable<AIObservationResult>
  ): Observable<DecisionAdjustment> {
    return newObservations.pipe(
      // 重要な変化のみに反応
      filter(obs => this.isSignificantChange(obs, currentDecision)),

      // 決定の再評価
      switchMap(async (obs) => {
        const reEvaluation = await this.reEvaluateDecision(
          currentDecision,
          obs
        );

        if (reEvaluation.adjustmentNeeded) {
          const adjustedDecision = await this.adjustDecision(
            currentDecision,
            obs,
            reEvaluation.adjustmentType
          );

          return {
            type: 'adjustment',
            originalDecision: currentDecision,
            adjustedDecision: adjustedDecision,
            reason: reEvaluation.reason,
            confidence: reEvaluation.confidence
          };
        }

        return {
          type: 'confirmation',
          decision: currentDecision,
          reason: 'No significant change detected'
        };
      }),

      share()
    );
  }

  // 意思決定学習機能
  async learnFromDecisionOutcome(
    decision: Decision,
    actualOutcome: ActualOutcome,
    executionMetrics: ExecutionMetrics
  ): Promise<LearningResult> {
    // 予測と実績の比較
    const predictionAccuracy = this.comparePredictionWithActual(
      decision.expectedOutcome,
      actualOutcome
    );

    // モデル更新
    const modelUpdates = await this.updatePredictionModels(
      decision,
      actualOutcome,
      predictionAccuracy
    );

    // 最適化戦略の改善
    const strategyUpdates = await this.improveOptimizationStrategy(
      decision,
      executionMetrics
    );

    // 制約条件の見直し
    const constraintUpdates = await this.reviewConstraints(
      decision,
      actualOutcome
    );

    return {
      predictionAccuracy,
      modelUpdates,
      strategyUpdates,
      constraintUpdates,
      overallImprovement: this.calculateOverallImprovement([
        modelUpdates,
        strategyUpdates,
        constraintUpdates
      ])
    };
  }
}

// AI統合実行システム
interface AIActionResult {
  executionId: string;
  startTime: Date;
  plannedActions: PlannedAction[];
  executionStatus: ExecutionStatus;
  realTimeMetrics: RealTimeMetric[];
  adjustments: ExecutionAdjustment[];
  completionStatus: CompletionStatus;
}

interface ExecutionStatus {
  currentPhase: string;
  progress: number; // 0-100%
  isOnTrack: boolean;
  deviations: Deviation[];
  nextMilestone: Milestone;
}

class AIActionSystem {
  private executionEngine: AutomatedExecutionEngine;
  private monitoringSystem: RealTimeMonitoringSystem;
  private adjustmentEngine: DynamicAdjustmentEngine;
  private rollbackManager: RollbackManager;
  private metricsCollector: MetricsCollectionSystem;

  constructor() {
    this.executionEngine = new RobustExecutionEngine();
    this.monitoringSystem = new ContinuousMonitoringSystem();
    this.adjustmentEngine = new IntelligentAdjustmentEngine();
    this.rollbackManager = new SafeRollbackManager();
    this.metricsCollector = new ComprehensiveMetricsCollector();
  }

  // AI実行の開始
  async executeAIAction(
    decision: AIDecisionResult,
    executionContext: ExecutionContext
  ): Promise<Observable<AIActionResult>> {
    const executionId = this.generateExecutionId();

    // 実行前チェック
    const preflightCheck = await this.performPreflightCheck(
      decision,
      executionContext
    );

    if (!preflightCheck.canExecute) {
      throw new ExecutionError(
        `Preflight check failed: ${preflightCheck.reason}`
      );
    }

    // 実行計画の詳細化
    const detailedPlan = await this.createDetailedExecutionPlan(
      decision.executionPlan,
      executionContext
    );

    // 実行開始
    return new Observable<AIActionResult>(observer => {
      this.startExecution(executionId, detailedPlan, observer);
    });
  }

  private async startExecution(
    executionId: string,
    plan: DetailedExecutionPlan,
    observer: Observer<AIActionResult>
  ): Promise<void> {
    const startTime = new Date();
    let currentStatus: ExecutionStatus = {
      currentPhase: plan.phases[0].name,
      progress: 0,
      isOnTrack: true,
      deviations: [],
      nextMilestone: plan.phases[0].milestones[0]
    };

    // リアルタイム監視の開始
    const monitoringStream = this.monitoringSystem.startMonitoring(
      executionId,
      plan
    );

    // 監視データに基づく調整
    const adjustmentStream = monitoringStream.pipe(
      filter(metrics => this.needsAdjustment(metrics, plan)),
      switchMap(metrics => this.generateAdjustments(metrics, plan))
    );

    // 実行ストリームの統合
    merge(
      monitoringStream,
      adjustmentStream
    ).subscribe({
      next: (update) => {
        const actionResult = this.buildActionResult(
          executionId,
          startTime,
          plan,
          currentStatus,
          update
        );

        observer.next(actionResult);
        currentStatus = actionResult.executionStatus;
      },
      error: (error) => {
        observer.error(this.handleExecutionError(error, executionId));
      },
      complete: () => {
        observer.complete();
      }
    });

    // 各フェーズの実行
    for (const phase of plan.phases) {
      try {
        await this.executePhase(phase, executionId);
        currentStatus.currentPhase = phase.name;
        currentStatus.progress = this.calculateProgress(phase, plan);
      } catch (error) {
        // フェーズ実行エラーの処理
        const errorHandling = await this.handlePhaseError(
          error,
          phase,
          executionId
        );

        if (errorHandling.shouldRollback) {
          await this.rollbackManager.rollback(executionId, phase);
          observer.error(error);
          return;
        }

        if (errorHandling.shouldAdjust) {
          const adjustedPhase = await this.adjustmentEngine.adjustPhase(
            phase,
            error,
            errorHandling.adjustmentStrategy
          );
          await this.executePhase(adjustedPhase, executionId);
        }
      }
    }
  }

  // フェーズ実行
  private async executePhase(
    phase: ExecutionPhase,
    executionId: string
  ): Promise<PhaseResult> {
    const phaseStartTime = new Date();

    // フェーズ内のアクション実行
    const actionResults = await Promise.allSettled(
      phase.actions.map(action => this.executeAction(action, executionId))
    );

    // 失敗したアクションの処理
    const failedActions = actionResults
      .filter(result => result.status === 'rejected')
      .map((result, index) => ({
        action: phase.actions[index],
        error: (result as PromiseRejectedResult).reason
      }));

    if (failedActions.length > 0 && phase.failureThreshold <= failedActions.length) {
      throw new PhaseExecutionError(
        `Phase ${phase.name} failed: ${failedActions.length} actions failed`,
        failedActions
      );
    }

    return {
      phase: phase.name,
      executionTime: Date.now() - phaseStartTime.getTime(),
      successfulActions: actionResults.filter(r => r.status === 'fulfilled').length,
      failedActions: failedActions.length,
      metrics: await this.metricsCollector.collectPhaseMetrics(phase, executionId)
    };
  }

  // 継続的最適化
  optimizeExecution(
    executionHistory: ExecutionHistory[]
  ): Observable<OptimizationResult> {
    return interval(60000).pipe( // 1分間隔で最適化
      switchMap(async () => {
        // 実行パフォーマンス分析
        const performanceAnalysis = await this.analyzeExecutionPerformance(
          executionHistory
        );

        // ボトルネック特定
        const bottlenecks = await this.identifyBottlenecks(performanceAnalysis);

        // 最適化策の生成
        const optimizations = await this.generateOptimizations(bottlenecks);

        // 最適化の適用
        const appliedOptimizations = await this.applyOptimizations(optimizations);

        return {
          analysisTimestamp: new Date(),
          performanceMetrics: performanceAnalysis,
          identifiedBottlenecks: bottlenecks,
          appliedOptimizations: appliedOptimizations,
          expectedImprovement: this.calculateExpectedImprovement(appliedOptimizations)
        };
      }),

      share()
    );
  }

  // 緊急停止・ロールバック機能
  async emergencyStop(
    executionId: string,
    reason: string
  ): Promise<EmergencyStopResult> {
    // 実行の即座停止
    await this.executionEngine.emergencyStop(executionId);

    // 状態の保存
    const currentState = await this.captureCurrentState(executionId);

    // 安全なロールバックの実行
    const rollbackResult = await this.rollbackManager.safeRollback(
      executionId,
      currentState,
      { reason, priority: 'emergency' }
    );

    // 影響範囲の評価
    const impactAssessment = await this.assessStopImpact(
      executionId,
      currentState
    );

    return {
      stoppedAt: new Date(),
      executionId,
      reason,
      currentState,
      rollbackResult,
      impactAssessment,
      recoveryRecommendations: this.generateRecoveryRecommendations(
        impactAssessment
      )
    };
  }
}

// AI統合OODAループ制御システム
class AIEnhancedOODASystem {
  private observationSystem: AIObservationSystem;
  private orientationSystem: AIOrientationSystem;
  private decisionSystem: AIDecisionSystem;
  private actionSystem: AIActionSystem;
  private coordinationEngine: SystemCoordinationEngine;
  private humanInterface: HumanMachineInterface;

  constructor(config: OODASystemConfig) {
    this.observationSystem = new AIObservationSystem();
    this.orientationSystem = new AIOrientationSystem();
    this.decisionSystem = new AIDecisionSystem();
    this.actionSystem = new AIActionSystem();
    this.coordinationEngine = new SystemCoordinationEngine();
    this.humanInterface = new HumanMachineInterface(config.humanOversight);
  }

  // 完全なOODAループの実行
  async executeOODALoop(
    initialContext: SystemContext,
    loopConfiguration: OODALoopConfig
  ): Promise<Observable<OODALoopResult>> {
    return new Observable<OODALoopResult>(observer => {
      this.runContinuousOODALoop(
        initialContext,
        loopConfiguration,
        observer
      );
    });
  }

  private async runContinuousOODALoop(
    context: SystemContext,
    config: OODALoopConfig,
    observer: Observer<OODALoopResult>
  ): Promise<void> {
    let currentContext = context;
    let loopIteration = 0;

    while (config.continuousMode || loopIteration < config.maxIterations) {
      const loopStartTime = new Date();

      try {
        // OBSERVE フェーズ
        const observationResult = await firstValueFrom(
          this.observationSystem.performAIObservation().pipe(
            timeout(config.observationTimeout)
          )
        );

        // 人間監督ポイント1
        if (config.humanOversight.observationReview) {
          const humanApproval = await this.humanInterface.reviewObservation(
            observationResult
          );
          if (!humanApproval.approved) {
            continue; // 次のループへ
          }
        }

        // ORIENT フェーズ
        const orientationResult = await this.orientationSystem.performAIOrientation(
          observationResult
        );

        // 人間監督ポイント2
        if (config.humanOversight.orientationReview) {
          const humanApproval = await this.humanInterface.reviewOrientation(
            orientationResult
          );
          if (!humanApproval.approved) {
            continue;
          }
        }

        // DECIDE フェーズ
        const decisionResult = await this.decisionSystem.performAIDecision(
          orientationResult,
          config.availableActions,
          config.constraints
        );

        // 人間監督ポイント3（最も重要）
        if (config.humanOversight.decisionApproval) {
          const humanApproval = await this.humanInterface.approveDecision(
            decisionResult
          );
          if (!humanApproval.approved) {
            // 代替案の提示
            const alternativeDecision = await this.handleDecisionRejection(
              decisionResult,
              humanApproval.rejectionReason
            );
            if (alternativeDecision) {
              decisionResult = alternativeDecision;
            } else {
              continue;
            }
          }
        }

        // ACT フェーズ
        const actionStream = await this.actionSystem.executeAIAction(
          decisionResult,
          currentContext
        );

        // アクション結果の監視
        actionStream.subscribe({
          next: (actionResult) => {
            const loopResult: OODALoopResult = {
              iteration: loopIteration,
              timestamp: loopStartTime,
              duration: Date.now() - loopStartTime.getTime(),
              observationResult,
              orientationResult,
              decisionResult,
              actionResult,
              systemState: this.coordinationEngine.getCurrentSystemState(),
              performance: this.calculateLoopPerformance({
                observationResult,
                orientationResult,
                decisionResult,
                actionResult
              })
            };

            observer.next(loopResult);

            // 次のループのコンテキスト更新
            currentContext = this.updateContext(currentContext, loopResult);
          },
          error: (error) => {
            observer.error(this.handleLoopError(error, loopIteration));
          }
        });

        loopIteration++;

        // ループ間隔の調整
        if (config.adaptiveInterval) {
          const optimalInterval = this.calculateOptimalInterval(
            observationResult,
            orientationResult
          );
          await this.delay(optimalInterval);
        } else {
          await this.delay(config.fixedInterval);
        }

      } catch (error) {
        const errorHandling = await this.handleLoopError(error, loopIteration);

        if (errorHandling.shouldTerminate) {
          observer.error(error);
          return;
        }

        if (errorHandling.shouldRetry) {
          // エラー回復待機
          await this.delay(errorHandling.retryDelay);
          continue;
        }
      }
    }

    observer.complete();
  }

  // ループ性能の最適化
  optimizeLoopPerformance(
    performanceHistory: LoopPerformanceHistory[]
  ): Observable<OptimizationRecommendation> {
    return interval(300000).pipe( // 5分間隔
      switchMap(async () => {
        // 性能分析
        const performanceAnalysis = await this.analyzeLoopPerformance(
          performanceHistory
        );

        // ボトルネック特定
        const bottlenecks = this.identifyPerformanceBottlenecks(
          performanceAnalysis
        );

        // 最適化推奨の生成
        const recommendations = await this.generateOptimizationRecommendations(
          bottlenecks
        );

        return {
          timestamp: new Date(),
          performanceAnalysis,
          bottlenecks,
          recommendations,
          expectedImprovement: this.calculateExpectedImprovement(recommendations)
        };
      })
    );
  }
}