Effective task allocation ensures that work is distributed fairly and efficiently among agents, maximizing collective productivity while respecting individual capabilities. Like a well-organized community where each member contributes according to their strengths, multi-agent systems achieve optimal outcomes through intelligent task distribution.
Task allocation is the process of assigning tasks to agents in a way that optimizes system performance while satisfying constraints. This is one of the most fundamental problems in multi-agent systems.
Key considerations in task allocation include:
Tasks are distributed cyclically among agents. Simple but doesn't account for agent capabilities or current workload.
class RoundRobinAllocator {
constructor(agents) {
this.agents = agents;
this.currentIndex = 0;
}
allocate(task) {
const agent = this.agents[this.currentIndex];
this.currentIndex = (this.currentIndex + 1) % this.agents.length;
return {
task: task,
assignedAgent: agent.id,
timestamp: Date.now()
};
}
allocateBatch(tasks) {
return tasks.map(task => this.allocate(task));
}
}
// Simple but may not balance workload effectively
// 弘益人間: Equal distribution for fairness
Tasks are assigned to agents with the best matching capabilities and skills.
class CapabilityBasedAllocator {
constructor(agents) {
this.agents = agents;
this.capabilityMatrix = this.buildCapabilityMatrix();
}
buildCapabilityMatrix() {
const matrix = new Map();
for (const agent of this.agents) {
matrix.set(agent.id, {
skills: agent.getSkills(),
performance: agent.getPerformanceMetrics(),
availability: agent.isAvailable()
});
}
return matrix;
}
allocate(task) {
let bestAgent = null;
let bestScore = -Infinity;
for (const agent of this.agents) {
const capabilities = this.capabilityMatrix.get(agent.id);
if (!capabilities.availability) continue;
const score = this.calculateMatchScore(task, capabilities);
if (score > bestScore) {
bestScore = score;
bestAgent = agent;
}
}
return {
task: task,
assignedAgent: bestAgent?.id || null,
matchScore: bestScore,
timestamp: Date.now()
};
}
calculateMatchScore(task, capabilities) {
const requiredSkills = task.requiredSkills || [];
const agentSkills = capabilities.skills;
// Calculate skill match percentage
const matchedSkills = requiredSkills.filter(
skill => agentSkills.includes(skill)
).length;
const skillScore = matchedSkills / requiredSkills.length;
// Factor in agent performance
const performanceScore = capabilities.performance.averageQuality;
// Weighted combination
return skillScore * 0.7 + performanceScore * 0.3;
}
}
// 弘益人間: Matching tasks to expertise for optimal outcomes
Tasks are assigned to minimize imbalance in agent workloads.
class LoadBalancedAllocator {
constructor(agents) {
this.agents = agents;
this.workload = new Map();
// Initialize workload tracking
agents.forEach(agent => this.workload.set(agent.id, 0));
}
allocate(task) {
// Find agent with least current workload
let minWorkload = Infinity;
let leastLoadedAgent = null;
for (const agent of this.agents) {
const currentLoad = this.workload.get(agent.id);
if (agent.canPerform(task) && currentLoad < minWorkload) {
minWorkload = currentLoad;
leastLoadedAgent = agent;
}
}
if (leastLoadedAgent) {
// Update workload
const estimatedEffort = task.estimatedDuration || 1;
this.workload.set(
leastLoadedAgent.id,
this.workload.get(leastLoadedAgent.id) + estimatedEffort
);
}
return {
task: task,
assignedAgent: leastLoadedAgent?.id || null,
currentWorkload: minWorkload,
timestamp: Date.now()
};
}
// Called when agent completes a task
taskCompleted(agentId, task) {
const currentLoad = this.workload.get(agentId);
const effort = task.actualDuration || task.estimatedDuration || 1;
this.workload.set(agentId, Math.max(0, currentLoad - effort));
}
getWorkloadBalance() {
const loads = Array.from(this.workload.values());
const avg = loads.reduce((sum, load) => sum + load, 0) / loads.length;
const variance = loads.reduce(
(sum, load) => sum + Math.pow(load - avg, 2),
0
) / loads.length;
return {
average: avg,
variance: variance,
standardDeviation: Math.sqrt(variance)
};
}
}
// 弘益人間: Fair workload distribution benefiting all agents
Some tasks require multiple agents working together. Coalition formation determines which agents should form teams to complete complex tasks.
class CoalitionFormation {
constructor(agents, tasks) {
this.agents = agents;
this.tasks = tasks;
}
// Form coalitions for tasks requiring multiple agents
formCoalitions() {
const coalitions = [];
for (const task of this.tasks) {
if (task.requiredAgents > 1) {
const coalition = this.findBestCoalition(task);
if (coalition) {
coalitions.push({
task: task,
members: coalition.members,
value: coalition.value
});
}
} else {
// Single agent task
const agent = this.findBestAgent(task);
if (agent) {
coalitions.push({
task: task,
members: [agent],
value: this.calculateValue(task, [agent])
});
}
}
}
return coalitions;
}
findBestCoalition(task) {
const requiredSize = task.requiredAgents;
const candidates = this.generateCombinations(this.agents, requiredSize);
let bestCoalition = null;
let bestValue = -Infinity;
for (const coalition of candidates) {
// Check if coalition can perform task
if (!this.canPerformTask(coalition, task)) continue;
const value = this.calculateCoalitionValue(coalition, task);
if (value > bestValue) {
bestValue = value;
bestCoalition = coalition;
}
}
return bestCoalition ? {
members: bestCoalition,
value: bestValue
} : null;
}
canPerformTask(coalition, task) {
const combinedSkills = new Set();
coalition.forEach(agent => {
agent.getSkills().forEach(skill => combinedSkills.add(skill));
});
return task.requiredSkills.every(skill => combinedSkills.has(skill));
}
calculateCoalitionValue(coalition, task) {
// Value = task reward - coalition cost
const reward = task.reward || 100;
const cost = coalition.reduce(
(sum, agent) => sum + agent.getCost(task),
0
);
// Bonus for complementary skills
const synergyBonus = this.calculateSynergy(coalition, task);
return reward - cost + synergyBonus;
}
calculateSynergy(coalition, task) {
// Agents with complementary skills work better together
const skillCoverage = new Set();
coalition.forEach(agent => {
agent.getSkills().forEach(skill => skillCoverage.add(skill));
});
const coverageRatio = skillCoverage.size / coalition.length;
return coverageRatio * 20; // Synergy bonus
}
}
// 弘益人間: Collaborative teams for complex challenges
In dynamic environments, new tasks arrive continuously and agents' states change. Allocation must adapt in real-time.
class DynamicTaskAllocator {
constructor(agents) {
this.agents = agents;
this.taskQueue = [];
this.allocations = new Map();
this.running = false;
}
start() {
this.running = true;
this.allocationLoop();
}
stop() {
this.running = false;
}
addTask(task) {
task.arrivalTime = Date.now();
task.priority = task.priority || 0;
// Insert in priority order
const index = this.taskQueue.findIndex(t => t.priority < task.priority);
if (index === -1) {
this.taskQueue.push(task);
} else {
this.taskQueue.splice(index, 0, task);
}
}
async allocationLoop() {
while (this.running) {
if (this.taskQueue.length > 0) {
const task = this.taskQueue.shift();
await this.allocateTask(task);
}
// Check for task completions and reallocation needs
await this.checkCompletions();
await this.considerReallocation();
await this.sleep(100); // Check every 100ms
}
}
async allocateTask(task) {
// Find best available agent
const availableAgents = this.agents.filter(a => a.isIdle());
if (availableAgents.length === 0) {
// No agents available, put back in queue
this.taskQueue.unshift(task);
return;
}
const bestAgent = this.selectBestAgent(availableAgents, task);
// Assign task
this.allocations.set(task.id, {
task: task,
agent: bestAgent,
startTime: Date.now(),
status: 'in-progress'
});
await bestAgent.executeTask(task);
}
async considerReallocation() {
// Consider reallocating tasks if better options become available
for (const [taskId, allocation] of this.allocations) {
if (allocation.status !== 'in-progress') continue;
const currentAgent = allocation.agent;
const idleAgents = this.agents.filter(
a => a.isIdle() && a.id !== currentAgent.id
);
for (const agent of idleAgents) {
if (this.shouldReallocate(allocation, agent)) {
await this.reallocate(allocation, agent);
break;
}
}
}
}
shouldReallocate(allocation, newAgent) {
const task = allocation.task;
const currentAgent = allocation.agent;
// Only reallocate if significant benefit
const currentScore = this.scoreAssignment(currentAgent, task);
const newScore = this.scoreAssignment(newAgent, task);
return newScore > currentScore * 1.5; // 50% improvement threshold
}
async reallocate(allocation, newAgent) {
const task = allocation.task;
const oldAgent = allocation.agent;
// Preempt current execution
await oldAgent.cancelTask(task);
// Reassign to new agent
allocation.agent = newAgent;
allocation.startTime = Date.now();
await newAgent.executeTask(task);
}
}
// 弘益人間: Adaptive allocation for changing conditions
Planning involves determining sequences of actions that achieve goals. In multi-agent systems, agents must coordinate their plans.
A central planner creates a joint plan for all agents.
class CentralizedPlanner {
constructor(agents, goals) {
this.agents = agents;
this.goals = goals;
}
createPlan() {
// Generate global plan for all agents
const plan = {
agents: new Map(),
schedule: [],
dependencies: []
};
// Decompose goals into tasks
const tasks = this.decomposeGoals(this.goals);
// Allocate tasks to agents
for (const task of tasks) {
const agent = this.selectAgent(task);
if (!plan.agents.has(agent.id)) {
plan.agents.set(agent.id, []);
}
plan.agents.get(agent.id).push(task);
}
// Create schedule respecting dependencies
plan.schedule = this.createSchedule(tasks);
return plan;
}
decomposeGoals(goals) {
const tasks = [];
for (const goal of goals) {
const subtasks = this.decomposeGoal(goal);
tasks.push(...subtasks);
}
return tasks;
}
decomposeGoal(goal) {
// Hierarchical task decomposition
if (goal.isPrimitive) {
return [goal];
}
const subtasks = [];
for (const method of goal.methods) {
if (this.isApplicable(method, goal)) {
subtasks.push(...method.subtasks);
break;
}
}
// Recursively decompose
return subtasks.flatMap(task =>
task.isPrimitive ? [task] : this.decomposeGoal(task)
);
}
createSchedule(tasks) {
// Topological sort respecting dependencies
const schedule = [];
const completed = new Set();
const remaining = [...tasks];
while (remaining.length > 0) {
const ready = remaining.filter(task =>
task.dependencies.every(dep => completed.has(dep))
);
if (ready.length === 0) {
throw new Error('Circular dependencies detected');
}
// Schedule ready tasks
for (const task of ready) {
schedule.push({
task: task,
earliestStart: this.calculateEarliestStart(task, schedule)
});
completed.add(task.id);
}
// Remove from remaining
remaining.splice(0, remaining.length,
...remaining.filter(t => !completed.has(t.id))
);
}
return schedule;
}
}
// 弘益人間: Coordinated planning for shared goals
Each agent creates its own plan and coordinates with others to resolve conflicts and dependencies.
class DistributedPlanningAgent {
constructor(id, goals) {
this.id = id;
this.goals = goals;
this.plan = [];
this.commitments = [];
}
async createPlan() {
// Each agent plans independently
this.plan = this.generateLocalPlan(this.goals);
// Share plan with neighbors
await this.sharePlan();
// Coordinate and resolve conflicts
await this.coordinatePlans();
return this.plan;
}
generateLocalPlan(goals) {
// Use A* or similar planner
const plan = [];
for (const goal of goals) {
const actions = this.planForGoal(goal);
plan.push(...actions);
}
return this.optimizePlan(plan);
}
async sharePlan() {
for (const neighbor of this.neighbors) {
await neighbor.receivePlan({
agentId: this.id,
plan: this.plan,
resources: this.getRequiredResources()
});
}
}
async coordinatePlans() {
// Iteratively refine plan through negotiation
let conflicts = await this.detectConflicts();
while (conflicts.length > 0) {
for (const conflict of conflicts) {
await this.resolveConflict(conflict);
}
conflicts = await this.detectConflicts();
}
}
async resolveConflict(conflict) {
if (conflict.type === 'resource') {
// Negotiate resource usage time
await this.negotiateResourceSchedule(conflict);
} else if (conflict.type === 'ordering') {
// Determine action ordering
await this.negotiateOrdering(conflict);
}
}
}
// 弘益人間: Distributed planning empowering autonomous agents
Scheduling determines when tasks should be executed, considering deadlines, priorities, and resource availability.
class TaskScheduler {
constructor() {
this.schedule = [];
this.resources = new Map();
}
// Earliest Deadline First (EDF) scheduling
scheduleEDF(tasks) {
const sorted = tasks.sort((a, b) => a.deadline - b.deadline);
for (const task of sorted) {
const slot = this.findEarliestSlot(task);
if (slot) {
this.schedule.push({
task: task,
startTime: slot.start,
endTime: slot.end
});
}
}
return this.schedule;
}
// Priority-based scheduling
schedulePriority(tasks) {
const sorted = tasks.sort((a, b) => b.priority - a.priority);
for (const task of sorted) {
const slot = this.findEarliestSlot(task);
if (slot) {
this.schedule.push({
task: task,
startTime: slot.start,
endTime: slot.end
});
}
}
return this.schedule;
}
findEarliestSlot(task) {
const duration = task.duration;
let currentTime = Date.now();
while (currentTime < task.deadline) {
if (this.isSlotAvailable(currentTime, duration, task.resources)) {
return {
start: currentTime,
end: currentTime + duration
};
}
currentTime += 1000; // Check every second
}
return null; // No slot found before deadline
}
isSlotAvailable(startTime, duration, requiredResources) {
const endTime = startTime + duration;
// Check if required resources are available
for (const [resource, amount] of Object.entries(requiredResources)) {
if (!this.checkResourceAvailability(resource, amount, startTime, endTime)) {
return false;
}
}
return true;
}
}
// 弘益人間: Efficient scheduling maximizing collective productivity