OORL.CollectiveLearning (object v0.1.2)
Collective learning implementation for OORL framework enabling emergent group intelligence through distributed coordination and knowledge sharing.
Provides mechanisms for:
- Coalition formation and management
- Distributed knowledge aggregation
- Emergent collective intelligence
- Byzantine fault tolerance in learning networks
Summary
Functions
Adds knowledge from an object to the collective learning system.
Detects and analyzes emergent collective intelligence phenomena.
Performs distributed policy optimization across coalition members.
Detects emergent behaviors and intelligence in the collective.
Forms a learning coalition from compatible objects based on learning objectives.
Manages coalition membership with dynamic joining and leaving.
Creates a new collective learning coalition.
Optimizes collective learning performance through meta-learning.
Performs distributed consensus on a proposition across coalition members.
Types
@type coalition_id() :: String.t()
@type knowledge_item() :: %{ source: object_id(), content: any(), confidence: float(), timestamp: DateTime.t(), validation_count: integer() }
@type object_id() :: String.t()
@type t() :: %OORL.CollectiveLearning{ byzantine_tolerance: float() | nil, coalition_id: coalition_id() | nil, collective_memory: map() | nil, communication_protocols: list() | nil, consensus_algorithm: atom() | nil, emergence_detector: pid() | nil, knowledge_graph: map() | nil, member_objects: MapSet.t() | nil, performance_metrics: map() | nil, trust_network: map() | nil }
Functions
Adds knowledge from an object to the collective learning system.
Parameters
collective: Current collective learning statesource_object_id: ID of the contributing objectknowledge: Knowledge item to addvalidation_options: Options for knowledge validation
Returns
Updated collective learning state
Detects and analyzes emergent collective intelligence phenomena.
Parameters
collective: Current collective learning stateobservation_window: Time window for analysis
Returns
{:ok, emergence_analysis} with detected emergent behaviors
Performs distributed policy optimization across coalition members.
Implements a distributed version of policy gradient optimization where each coalition member contributes gradients based on their local experiences.
Parameters
collective: Current collective learning state
Returns
Updated collective with optimized policies
Detects emergent behaviors and intelligence in the collective.
Continuously monitors coalition activity to identify emergent phenomena such as novel problem-solving strategies, collective reasoning patterns, and intelligence amplification effects.
Parameters
collective: Current collective learning state
Returns
{:ok, emergence_report} with detailed emergence analysis
Forms a learning coalition from compatible objects based on learning objectives.
Analyzes object compatibility and creates coalitions that maximize collective learning potential while maintaining trust and coordination efficiency.
Parameters
available_objects: List of object IDs available for coalition formationlearning_objective: The shared learning goal for the coalition
Returns
{:ok, collective} with formed coalition or {:error, reason}
Manages coalition membership with dynamic joining and leaving.
Parameters
collective: Current collective learning stateaction: :join or :leaveobject_id: Object to add or removecredentials: Trust credentials for joining objects
Returns
Updated collective with modified membership
Creates a new collective learning coalition.
Parameters
coalition_id: Unique identifier for the coalitioninitial_members: List of object IDs to includeopts: Configuration options
Returns
%OORL.CollectiveLearning{} struct
optimize_collective_performance(collective, performance_feedback, optimization_strategy \\ :adaptive)
Optimizes collective learning performance through meta-learning.
Parameters
collective: Current collective learning stateperformance_feedback: Recent performance metricsoptimization_strategy: Strategy for optimization
Returns
Optimized collective learning configuration
Performs distributed consensus on a proposition across coalition members.
Parameters
collective: Current collective learning stateproposition: Proposition to reach consensus ontimeout_ms: Maximum time to wait for consensus
Returns
{:ok, consensus_result} or {:error, reason}