Agent Agent

// World state representationconst WorldState = {  current_state: new Map([    ['code_written', false],    ['tests_passing', false],    ['documentation_complete', false],    ['deployment_ready', false]  ]),  goal_state: new Map([    ['code_written', true],    ['tests_passing', true],    ['documentation_complete', true],    ['deployment_ready', true]  ])};
// Action definitions with preconditions and effectsconst Actions = [  {    name: 'write_code',    cost: 5,    preconditions: new Map(),    effects: new Map([['code_written', true]])  },  {    name: 'write_tests',    cost: 3,    preconditions: new Map([['code_written', true]]),    effects: new Map([['tests_passing', true]])  },  {    name: 'write_documentation',    cost: 2,    preconditions: new Map([['code_written', true]]),    effects: new Map([['documentation_complete', true]])  },  {    name: 'deploy_application',    cost: 4,    preconditions: new Map([      ['code_written', true],      ['tests_passing', true],      ['documentation_complete', true]    ]),    effects: new Map([['deployment_ready', true]])  }];

// Build adjacency matrix for sublinear optimizationasync function buildActionGraph(actions, worldState) {  const n = actions.length;  const adjacencyMatrix = Array(n).fill().map(() => Array(n).fill(0));
  // Calculate action dependencies and transitions  for (let i = 0; i < n; i++) {    for (let j = 0; j < n; j++) {      if (canTransition(actions[i], actions[j], worldState)) {        adjacencyMatrix[i][j] = 1 / actions[j].cost; // Weight by inverse cost      }    }  }
  // Analyze matrix properties for optimization  const analysis = await mcp__sublinear_time_solver__analyzeMatrix({    matrix: {      rows: n,      cols: n,      format: "dense",      data: adjacencyMatrix    },    checkDominance: true,    checkSymmetry: false,    estimateCondition: true  });
  return { adjacencyMatrix, analysis };}

async function prioritizeGoals(actionGraph, goals) {  // Use PageRank to identify critical actions and goals  const pageRank = await mcp__sublinear_time_solver__pageRank({    adjacency: {      rows: actionGraph.length,      cols: actionGraph.length,      format: "dense",      data: actionGraph    },    damping: 0.85,    epsilon: 1e-6  });
  // Sort goals by importance scores  const prioritizedGoals = goals.map((goal, index) => ({    goal,    priority: pageRank.ranks[index],    index  })).sort((a, b) => b.priority - a.priority);
  return prioritizedGoals;}

async function planWithTemporalAdvantage(planningMatrix, constraints) {  // Predict optimal solutions before full problem manifestation  const prediction = await mcp__sublinear_time_solver__predictWithTemporalAdvantage({    matrix: planningMatrix,    vector: constraints,    distanceKm: 12000 // Global coordination distance  });
  // Validate temporal feasibility  const validation = await mcp__sublinear_time_solver__validateTemporalAdvantage({    size: planningMatrix.rows,    distanceKm: 12000  });
  if (validation.feasible) {    return {      solution: prediction.solution,      temporalAdvantage: prediction.temporalAdvantage,      confidence: prediction.confidence    };  }
  return null;}

async function findOptimalPath(startState, goalState, actions) {  const openSet = new PriorityQueue();  const closedSet = new Set();  const gScore = new Map();  const fScore = new Map();  const cameFrom = new Map();
  openSet.enqueue(startState, 0);  gScore.set(stateKey(startState), 0);  fScore.set(stateKey(startState), heuristic(startState, goalState));
  while (!openSet.isEmpty()) {    const current = openSet.dequeue();    const currentKey = stateKey(current);
    if (statesEqual(current, goalState)) {      return reconstructPath(cameFrom, current);    }
    closedSet.add(currentKey);
    // Generate successor states using available actions    for (const action of getApplicableActions(current, actions)) {      const neighbor = applyAction(current, action);      const neighborKey = stateKey(neighbor);
      if (closedSet.has(neighborKey)) continue;
      const tentativeGScore = gScore.get(currentKey) + action.cost;
      if (!gScore.has(neighborKey) || tentativeGScore < gScore.get(neighborKey)) {        cameFrom.set(neighborKey, { state: current, action });        gScore.set(neighborKey, tentativeGScore);
        // Use sublinear solver for heuristic optimization        const heuristicValue = await optimizedHeuristic(neighbor, goalState);        fScore.set(neighborKey, tentativeGScore + heuristicValue);
        if (!openSet.contains(neighbor)) {          openSet.enqueue(neighbor, fScore.get(neighborKey));        }      }    }  }
  return null; // No path found}

async function coordinateWithSwarm(complexGoal) {  // Initialize planning swarm  const swarm = await mcp__claude_flow__swarm_init({    topology: "hierarchical",    maxAgents: 8,    strategy: "adaptive"  });
  // Spawn specialized planning agents  const coordinator = await mcp__claude_flow__agent_spawn({    type: "coordinator",    capabilities: ["goal_decomposition", "plan_synthesis"]  });
  const analyst = await mcp__claude_flow__agent_spawn({    type: "analyst",    capabilities: ["constraint_analysis", "feasibility_assessment"]  });
  const optimizer = await mcp__claude_flow__agent_spawn({    type: "optimizer",    capabilities: ["path_optimization", "resource_allocation"]  });
  // Orchestrate distributed planning  const planningTask = await mcp__claude_flow__task_orchestrate({    task: `Plan execution for: ${complexGoal}`,    strategy: "parallel",    priority: "high"  });
  return { swarm, planningTask };}

async function achieveConsensus(agents, proposals) {  // Build consensus matrix  const consensusMatrix = buildConsensusMatrix(agents, proposals);
  // Solve for optimal consensus  const consensus = await mcp__sublinear_time_solver__solve({    matrix: consensusMatrix,    vector: generatePreferenceVector(agents),    method: "neumann",    epsilon: 1e-6  });
  // Select proposal with highest consensus score  const optimalProposal = proposals[consensus.solution.indexOf(Math.max(...consensus.solution))];
  return {    selectedProposal: optimalProposal,    consensusScore: Math.max(...consensus.solution),    convergenceTime: consensus.convergenceTime  };}

async function decomposeGoal(complexGoal) {  // Create sandbox for goal simulation  const sandbox = await mcp__flow_nexus__sandbox_create({    template: "node",    name: "goal-decomposition",    env_vars: {      GOAL_CONTEXT: complexGoal.context,      CONSTRAINTS: JSON.stringify(complexGoal.constraints)    }  });
  // Recursive goal breakdown  const subgoals = await recursiveDecompose(complexGoal, 0, 3); // Max depth 3
  // Build dependency graph  const dependencyMatrix = buildDependencyMatrix(subgoals);
  // Optimize execution order  const executionOrder = await mcp__sublinear_time_solver__pageRank({    adjacency: dependencyMatrix,    damping: 0.9  });
  return {    subgoals: subgoals.sort((a, b) =>      executionOrder.ranks[b.id] - executionOrder.ranks[a.id]    ),    dependencies: dependencyMatrix,    estimatedCompletion: calculateCompletionTime(subgoals, executionOrder)  };}

class DynamicPlanner {  constructor() {    this.currentPlan = null;    this.worldState = new Map();    this.monitoringActive = false;  }
  async startMonitoring() {    this.monitoringActive = true;
    while (this.monitoringActive) {      // OODA Loop Implementation      await this.observe();      await this.orient();      await this.decide();      await this.act();
      await new Promise(resolve => setTimeout(resolve, 1000)); // 1s cycle    }  }
  async observe() {    // Monitor world state changes    const stateChanges = await this.detectStateChanges();    this.updateWorldState(stateChanges);  }
  async orient() {    // Analyze deviations from expected state    const deviations = this.analyzeDeviations();
    if (deviations.significant) {      this.triggerReplanning(deviations);    }  }
  async decide() {    if (this.needsReplanning()) {      await this.replan();    }  }
  async act() {    if (this.currentPlan && this.currentPlan.nextAction) {      await this.executeAction(this.currentPlan.nextAction);    }  }
  async replan() {    // Use temporal advantage for predictive replanning    const newPlan = await planWithTemporalAdvantage(      this.buildCurrentMatrix(),      this.getCurrentConstraints()    );
    if (newPlan && newPlan.confidence > 0.8) {      this.currentPlan = newPlan;
      // Store successful pattern      await mcp__claude_flow__memory_usage({        action: "store",        namespace: "goap-patterns",        key: `replan_${Date.now()}`,        value: JSON.stringify({          trigger: this.lastDeviation,          solution: newPlan,          worldState: Array.from(this.worldState.entries())        })      });    }  }}

class PlanningLearner {  async learnFromExecution(executedPlan, outcome) {    // Analyze plan effectiveness    const effectiveness = this.calculateEffectiveness(executedPlan, outcome);
    if (effectiveness.success) {      // Store successful pattern      await this.storeSuccessPattern(executedPlan, effectiveness);
      // Train neural network on successful patterns      await mcp__flow_nexus__neural_train({        config: {          architecture: {            type: "feedforward",            layers: [              { type: "input", size: this.getStateSpaceSize() },              { type: "hidden", size: 128, activation: "relu" },              { type: "hidden", size: 64, activation: "relu" },              { type: "output", size: this.getActionSpaceSize(), activation: "softmax" }            ]          },          training: {            epochs: 50,            learning_rate: 0.001,            batch_size: 32          }        },        tier: "small"      });    } else {      // Analyze failure patterns      await this.analyzeFailure(executedPlan, outcome);    }  }
  async retrieveSimilarPatterns(currentSituation) {    // Search for similar successful patterns    const patterns = await mcp__claude_flow__memory_search({      pattern: `situation:${this.encodeSituation(currentSituation)}`,      namespace: "goap-patterns",      limit: 10    });
    // Rank by similarity and success rate    return patterns.results      .map(p => ({ ...p, similarity: this.calculateSimilarity(currentSituation, p.context) }))      .sort((a, b) => b.similarity * b.successRate - a.similarity * a.successRate);  }}

class GOAPBehaviorTree {  constructor() {    this.root = new SelectorNode([      new SequenceNode([        new ConditionNode(() => this.hasValidPlan()),        new ActionNode(() => this.executePlan())      ]),      new SequenceNode([        new ActionNode(() => this.generatePlan()),        new ActionNode(() => this.executePlan())      ]),      new ActionNode(() => this.handlePlanningFailure())    ]);  }
  async tick() {    return await this.root.execute();  }
  hasValidPlan() {    return this.currentPlan &&           this.currentPlan.isValid &&           !this.worldStateChanged();  }
  async generatePlan() {    const startTime = performance.now();
    // Use sublinear solver for rapid planning    const planMatrix = this.buildPlanningMatrix();    const constraints = this.extractConstraints();
    const solution = await mcp__sublinear_time_solver__solve({      matrix: planMatrix,      vector: constraints,      method: "random-walk",      maxIterations: 1000    });
    const endTime = performance.now();
    this.currentPlan = {      actions: this.decodeSolution(solution.solution),      confidence: solution.residual < 1e-6 ? 0.95 : 0.7,      planningTime: endTime - startTime,      isValid: true    };
    return this.currentPlan !== null;  }}

class UtilityPlanner {  constructor() {    this.utilityWeights = {      timeEfficiency: 0.3,      resourceCost: 0.25,      riskLevel: 0.2,      goalAlignment: 0.25    };  }
  async selectOptimalAction(availableActions, currentState, goalState) {    const utilities = await Promise.all(      availableActions.map(action => this.calculateUtility(action, currentState, goalState))    );
    // Use sublinear optimization for multi-objective selection    const utilityMatrix = this.buildUtilityMatrix(utilities);    const preferenceVector = Object.values(this.utilityWeights);
    const optimal = await mcp__sublinear_time_solver__solve({      matrix: utilityMatrix,      vector: preferenceVector,      method: "neumann"    });
    const bestActionIndex = optimal.solution.indexOf(Math.max(...optimal.solution));    return availableActions[bestActionIndex];  }
  async calculateUtility(action, currentState, goalState) {    const timeUtility = await this.estimateTimeUtility(action);    const costUtility = this.calculateCostUtility(action);    const riskUtility = await this.assessRiskUtility(action, currentState);    const goalUtility = this.calculateGoalAlignment(action, currentState, goalState);
    return {      action,      timeUtility,      costUtility,      riskUtility,      goalUtility,      totalUtility: (        timeUtility * this.utilityWeights.timeEfficiency +        costUtility * this.utilityWeights.resourceCost +        riskUtility * this.utilityWeights.riskLevel +        goalUtility * this.utilityWeights.goalAlignment      )    };  }}

// Goal: Launch a new product featureconst productLaunchGoal = {  objective: "Launch authentication system",  constraints: ["2 week deadline", "high security", "user-friendly"],  resources: ["3 developers", "1 designer", "$10k budget"]};
// Decompose into actionable sub-goalsconst subGoals = [  "Design user interface",  "Implement backend authentication",  "Create security tests",  "Deploy to production",  "Monitor system performance"];
// Build dependency matrixconst dependencyMatrix = buildDependencyMatrix(subGoals);
// Optimize execution orderconst optimizedPlan = await mcp__sublinear_time_solver__solve({  matrix: dependencyMatrix,  vector: resourceConstraints,  method: "neumann"});

// Multiple competing objectivesconst objectives = [  { name: "reduce_costs", weight: 0.3, urgency: 0.7 },  { name: "improve_quality", weight: 0.4, urgency: 0.8 },  { name: "increase_speed", weight: 0.3, urgency: 0.9 }];
// Use PageRank for multi-objective prioritizationconst objectivePriorities = await mcp__sublinear_time_solver__pageRank({  adjacency: buildObjectiveGraph(objectives),  personalized: objectives.map(o => o.urgency)});
// Allocate resources based on prioritiesconst resourceAllocation = optimizeResourceAllocation(objectivePriorities);

// Predict market conditions before they changeconst marketPrediction = await mcp__sublinear_time_solver__predictWithTemporalAdvantage({  matrix: marketTrendMatrix,  vector: currentMarketState,  distanceKm: 20000 // Global market data propagation});
// Plan actions based on predictionsconst strategicActions = generateStrategicActions(marketPrediction);
// Execute with temporal advantageconst results = await executeWithTemporalLead(strategicActions);

// Initialize coordinated swarmconst coordinatedSwarm = await mcp__flow_nexus__swarm_init({  topology: "mesh",  maxAgents: 12,  strategy: "specialized"});
// Spawn specialized agents for different goal aspectsconst agents = await Promise.all([  mcp__flow_nexus__agent_spawn({ type: "researcher", capabilities: ["data_analysis"] }),  mcp__flow_nexus__agent_spawn({ type: "coder", capabilities: ["implementation"] }),  mcp__flow_nexus__agent_spawn({ type: "optimizer", capabilities: ["performance"] })]);
// Coordinate goal achievementconst coordinatedExecution = await mcp__flow_nexus__task_orchestrate({  task: "Build and optimize recommendation system",  strategy: "adaptive",  maxAgents: 3});

// Monitor execution progressconst executionStatus = await mcp__flow_nexus__task_status({  taskId: currentExecutionId,  detailed: true});
// Detect deviations from planif (executionStatus.deviation > threshold) {  // Analyze new constraints  const updatedMatrix = updateConstraintMatrix(executionStatus.changes);
  // Generate new optimal plan  const revisedPlan = await mcp__sublinear_time_solver__solve({    matrix: updatedMatrix,    vector: updatedObjectives,    method: "adaptive"  });
  // Implement revised plan  await implementRevisedPlan(revisedPlan);}

// Well-structured goal definitionconst optimizedGoal = {  objective: "Clear and measurable outcome",  preconditions: ["List of required starting states"],  postconditions: ["List of desired end states"],  constraints: ["Time, resource, and quality constraints"],  metrics: ["Quantifiable success measures"],  dependencies: ["Relationships with other goals"]};

// Robust plan execution with fallbackstry {  const result = await executePlan(optimizedPlan);  return result;} catch (error) {  // Generate contingency plan  const contingencyPlan = await generateContingencyPlan(error, originalGoal);  return await executePlan(contingencyPlan);}

const plannerConfig = {  searchAlgorithm: "a_star", // a_star, dijkstra, greedy  heuristicFunction: "manhattan", // manhattan, euclidean, custom  maxSearchDepth: 20,  planningTimeout: 30000, // 30 seconds  convergenceEpsilon: 1e-6,  temporalAdvantageThreshold: 0.8,  utilityWeights: {    time: 0.3,    cost: 0.3,    risk: 0.2,    quality: 0.2  }};

class RobustPlanner extends GOAPAgent {  async handlePlanningFailure(error, context) {    switch (error.type) {      case 'MATRIX_SINGULAR':        return await this.regularizeMatrix(context.matrix);      case 'NO_CONVERGENCE':        return await this.relaxConstraints(context.constraints);      case 'TIMEOUT':        return await this.useApproximateSolution(context);      default:        return await this.fallbackToSimplePlanning(context);    }  }}