Agent Skills: Emergent Role Assignment

**Category:** Phase 3 Core - Self-Organization

UncategorizedID: plurigrid/asi/emergent-role-assignment

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ies/music-topos/.codex/skills/emergent-role-assignment/SKILL.md

Emergent Role Assignment

Category: Phase 3 Core - Self-Organization Status: Skeleton Implementation Dependencies: sheaf-theoretic-coordination, chemical-organization-theory

Overview

Implements spontaneous role assignment in multi-agent systems through self-organization, dynamic hierarchy adaptation, and reward-based emergence without central coordination.

Capabilities

  • Spontaneous Hierarchy: Agents self-organize into hierarchical structures
  • Dynamic Role Adaptation: Roles change based on task demands
  • Reward-Based Emergence: Roles emerge from collective optimization
  • Stability Analysis: Verify organizational stability and convergence

Core Components

  1. Role Dynamics (role_dynamics.jl)

    • Role state representation
    • Transition dynamics between roles
    • Stability attractors
  2. Hierarchy Formation (hierarchy_formation.jl)

    • Emergent leadership via fitness
    • Span of control optimization
    • Dynamic reorganization triggers
  3. Reward Shaping (reward_shaping.jl)

    • Collective reward functions
    • Credit assignment without centralization
    • Multi-agent learning objectives
  4. Stability Verification (stability_verification.jl)

    • Lyapunov function construction
    • Convergence guarantees
    • Resilience to perturbations

Integration Points

  • Input from: sheaf-theoretic-coordination (consensus on roles)
  • Output to: chemical-organization-theory (roles as stable organizations)
  • Coordinates with: feedforward-learning-local (local learning signals)

Usage

using EmergentRoleAssignment

# Define multi-agent system
agents = [Agent(id=i, capabilities=rand(5)) for i in 1:20]
environment = GridWorld(10, 10)

# Initialize role assignment system
role_system = RoleSystem(
    n_roles=4,
    transition_rates=0.1,
    reward_fn=collective_foraging_reward
)

# Simulate emergence
trajectory = simulate_emergence(role_system, agents, environment, steps=1000)

# Analyze stability
stability = analyze_role_stability(trajectory)
hierarchy = extract_hierarchy(trajectory.final_state)

References

  • Bonabeau et al. "Self-Organization in Social Insects" (1997)
  • Wolpert & Tumer "Optimal Payoff Functions for Members of Collectives" (1999)
  • Tumer & Wolpert "A Survey of Collectives" (2004)

Implementation Status

  • [x] Basic role dynamics
  • [x] Simple hierarchy formation
  • [ ] Full reward shaping framework
  • [ ] Stability verification
  • [ ] Benchmark on multi-agent tasks