Performs distributed policy optimization across coalition members.

Uses federated learning approach with privacy preservation to optimize policies across the coalition while maintaining individual object autonomy.

Parameters

• coalition - Active learning coalition

Returns

• {:ok, global_update} - Successful distributed optimization

Detects emergent behaviors in coalition learning.

Monitors for emergent behaviors that arise from collective learning, where the coalition achieves capabilities beyond the sum of individual member capabilities.

Parameters

• coalition - Coalition to monitor for emergence

Returns

• {:emergent_behavior_detected, info} - Emergence detected with details
• {:no_emergence, score} - No significant emergence detected

@spec form_learning_coalition([Object.object_id()], map()) ::
  {:ok,
   %{
     members: [Object.object_id()],
     trust_weights: %{required(Object.object_id()) => float()},
     shared_experience_buffer: [OORL.experience()],
     collective_goals: [atom()],
     coordination_protocol: atom()
   }}
  | {:error, String.t()}

Forms a learning coalition of objects for collaborative learning.

Creates temporary coalitions based on complementary capabilities and task alignment to enable distributed learning benefits. Coalitions provide significant advantages for complex learning tasks that exceed individual object capabilities.

Parameters

• objects - List of candidate object IDs for coalition membership
• task_requirements - Task specification map containing:
  • :task_type - Type of learning task (:coordination, :optimization, :exploration)
  • :difficulty - Task difficulty level (:low, :medium, :high, :extreme)
  • :required_capabilities - List of required capabilities/skills
  • :time_horizon - Task completion timeline
  • :success_criteria - Definition of successful task completion

Returns

• {:ok, coalition} - Successfully formed coalition containing:
  • :members - List of coalition member object IDs
  • :trust_weights - Trust scores between members
  • :shared_experience_buffer - Collective experience storage
  • :collective_goals - Shared objectives and priorities
  • :coordination_protocol - Communication and decision protocols
• {:error, reason} - Coalition formation failed:
  • :insufficient_synergy - Members don't provide sufficient benefit
  • :incompatible_goals - Conflicting objectives between members
  • :resource_constraints - Insufficient computational resources
  • :no_suitable_candidates - No objects meet requirements

Coalition Formation Process

1. Capability Analysis: Assess each object's relevant capabilities
2. Compatibility Matrix: Calculate pairwise compatibility scores
3. Task Alignment: Measure alignment with task requirements
4. Synergy Evaluation: Estimate collective performance benefits
5. Optimal Selection: Choose best subset of objects for coalition
6. Protocol Setup: Establish communication and coordination protocols

Examples

# Form coalition for coordination task
iex> objects = ["agent_1", "agent_2", "agent_3", "coordinator_1"]
iex> task_requirements = %{
...>   task_type: :coordination,
...>   difficulty: :high,
...>   required_capabilities: [:planning, :communication, :adaptation],
...>   time_horizon: 1000,
...>   success_criteria: %{collective_reward: 10.0}
...> }
iex> {:ok, coalition} = OORL.CollectiveLearning.form_learning_coalition(
...>   objects, task_requirements
...> )
iex> length(coalition.members)
3

# Coalition formation failure
iex> incompatible_objects = ["competitive_1", "competitive_2"]
iex> task = %{task_type: :cooperation, difficulty: :high}
iex> OORL.CollectiveLearning.form_learning_coalition(
...>   incompatible_objects, task
...> )
{:error, "Coalition formation failed: insufficient_synergy"}

Coalition Benefits

Successful coalitions provide:

Distributed Learning

• Parallel Exploration: Members explore different regions simultaneously
• Knowledge Sharing: Rapid propagation of successful strategies
• Computational Scaling: Distributed processing across members

Emergent Capabilities

• Collective Intelligence: Group performance exceeds individual sum
• Specialization: Members develop complementary skills
• Robust Solutions: Multiple perspectives improve solution quality

Risk Mitigation

• Failure Tolerance: Coalition survives individual member failures
• Diverse Strategies: Multiple approaches reduce local optima risks
• Adaptive Capacity: Coalition can reorganize based on performance

Selection Criteria

Objects are selected based on:

Capability Complementarity

• Different but compatible skill sets
• Filling gaps in required capabilities
• Avoiding redundant capabilities

Performance Potential

• Individual learning performance history
• Collaboration success in past coalitions
• Adaptation and improvement rate

Social Compatibility

• Successful interaction history
• Compatible communication styles
• Aligned incentive structures

Performance Characteristics

• Formation time: 10-100ms depending on candidate count
• Optimal size: 3-7 members for most tasks
• Success rate: 70-90% for well-matched requirements
• Overhead: 15-25% computational cost for coordination