defmodule Automata.AdaptiveLearning do
  @moduledoc """
  Main entry point for the Adaptive Learning systems.
  
  This module integrates all components of the Adaptive Learning framework:
  - Multi-Agent Reinforcement Learning
  - Collective Knowledge Evolution
  - Adaptive Strategy Formulation
  
  The Adaptive Learning framework provides mechanisms for agents to learn and adapt
  their behaviors, knowledge, and strategies based on experience and changing conditions.
  """
  
  alias Automata.AdaptiveLearning.MultiAgentRL
  alias Automata.AdaptiveLearning.CollectiveKnowledgeEvolution
  alias Automata.AdaptiveLearning.AdaptiveStrategyFormulation
  alias Automata.CollectiveIntelligence.KnowledgeSynthesis.KnowledgeSystem
  
  @doc """
  Starts the Adaptive Learning system.
  """
  def start_link(opts \\ []) do
    # Start the adaptive learning supervisor
    {:ok, supervisor} = Automata.AdaptiveLearning.Supervisor.start_link(opts)
    
    # Return a handle to the Adaptive Learning system
    {:ok, %{
      supervisor: supervisor,
      modules: [:multi_agent_rl, :collective_knowledge_evolution, :adaptive_strategy_formulation]
    }}
  end
  
  # Multi-Agent RL API
  
  @doc """
  Registers a new learning agent.
  """
  def register_agent(agent_id, algorithm \\ :q_learning, params \\ %{}) do
    MultiAgentRL.register_agent(agent_id, algorithm, params)
  end
  
  @doc """
  Records an experience tuple for an agent.
  """
  def record_experience(agent_id, experience) do
    MultiAgentRL.record_experience(agent_id, experience)
  end
  
  @doc """
  Batch records multiple experiences for an agent.
  """
  def record_experiences(agent_id, experiences) when is_list(experiences) do
    MultiAgentRL.record_experiences(agent_id, experiences)
  end
  
  @doc """
  Gets the best action for a state according to the agent's policy.
  """
  def get_action(agent_id, state, epsilon \\ 0.1) do
    MultiAgentRL.get_action(agent_id, state, epsilon)
  end
  
  @doc """
  Updates an agent's value function/policy based on collected experiences.
  """
  def update_agent(agent_id, params \\ %{}) do
    MultiAgentRL.update(agent_id, params)
  end
  
  @doc """
  Triggers synchronization of experience and policies among agents.
  """
  def synchronize_agents(group_id \\ :all) do
    MultiAgentRL.synchronize(group_id)
  end
  
  @doc """
  Retrieves metrics and performance statistics for an agent.
  """
  def get_agent_metrics(agent_id) do
    MultiAgentRL.get_agent_metrics(agent_id)
  end
  
  @doc """
  Gets the current policy for an agent.
  """
  def get_policy(agent_id) do
    MultiAgentRL.get_policy(agent_id)
  end
  
  @doc """
  Gets information about all registered agents.
  """
  def list_agents do
    MultiAgentRL.list_agents()
  end
  
  @doc """
  Creates a joint policy from multiple agents.
  """
  def create_joint_policy(agent_ids) when is_list(agent_ids) do
    MultiAgentRL.create_joint_policy(agent_ids)
  end
  
  # Collective Knowledge Evolution API
  
  @doc """
  Registers a new knowledge source.
  """
  def register_knowledge_source(source_id, type, params \\ %{}) do
    CollectiveKnowledgeEvolution.register_knowledge_source(source_id, type, params)
  end
  
  @doc """
  Adds a new observation to the knowledge base.
  """
  def add_observation(observation, source_id, metadata \\ %{}) do
    CollectiveKnowledgeEvolution.add_observation(observation, source_id, metadata)
  end
  
  @doc """
  Proposes a knowledge revision based on new evidence.
  """
  def propose_revision(knowledge_id, revision, evidence, source_id) do
    CollectiveKnowledgeEvolution.propose_revision(knowledge_id, revision, evidence, source_id)
  end
  
  @doc """
  Detects and adapts to concept drift.
  """
  def detect_concept_drift(concept_id, params \\ %{}) do
    CollectiveKnowledgeEvolution.detect_concept_drift(concept_id, params)
  end
  
  @doc """
  Evolves the ontology based on new evidence.
  """
  def evolve_ontology(params \\ %{}) do
    CollectiveKnowledgeEvolution.evolve_ontology(params)
  end
  
  @doc """
  Gets the current state of a knowledge element.
  """
  def get_knowledge_state(knowledge_id) do
    CollectiveKnowledgeEvolution.get_knowledge_state(knowledge_id)
  end
  
  @doc """
  Gets history of revisions for a knowledge element.
  """
  def get_revision_history(knowledge_id) do
    CollectiveKnowledgeEvolution.get_revision_history(knowledge_id)
  end
  
  @doc """
  Gets metrics about the knowledge evolution process.
  """
  def get_evolution_metrics do
    CollectiveKnowledgeEvolution.get_evolution_metrics()
  end
  
  @doc """
  Triggers a knowledge refinement cycle.
  """
  def refine_knowledge(options \\ %{}) do
    CollectiveKnowledgeEvolution.refine_knowledge(options)
  end
  
  @doc """
  Check consistency of the knowledge base and identify conflicts.
  """
  def check_knowledge_consistency do
    CollectiveKnowledgeEvolution.check_knowledge_consistency()
  end
  
  # Adaptive Strategy Formulation API
  
  @doc """
  Creates a new strategy.
  """
  def create_strategy(domain_id, name, description, components, params \\ %{}) do
    AdaptiveStrategyFormulation.create_strategy(domain_id, name, description, components, params)
  end
  
  @doc """
  Evaluates a strategy against criteria.
  """
  def evaluate_strategy(strategy_id, evaluation_criteria \\ %{}) do
    AdaptiveStrategyFormulation.evaluate_strategy(strategy_id, evaluation_criteria)
  end
  
  @doc """
  Adapts a strategy based on feedback or changing conditions.
  """
  def adapt_strategy(strategy_id, adaptation_params) do
    AdaptiveStrategyFormulation.adapt_strategy(strategy_id, adaptation_params)
  end
  
  @doc """
  Transfers a strategy to a new domain.
  """
  def transfer_strategy(strategy_id, target_domain_id, transfer_params \\ %{}) do
    AdaptiveStrategyFormulation.transfer_strategy(strategy_id, target_domain_id, transfer_params)
  end
  
  @doc """
  Gets details of a specific strategy.
  """
  def get_strategy(strategy_id) do
    AdaptiveStrategyFormulation.get_strategy(strategy_id)
  end
  
  @doc """
  Lists all strategies for a domain.
  """
  def list_domain_strategies(domain_id) do
    AdaptiveStrategyFormulation.list_domain_strategies(domain_id)
  end
  
  @doc """
  Lists all domains with strategies.
  """
  def list_strategy_domains do
    AdaptiveStrategyFormulation.list_domains()
  end
  
  @doc """
  Gets the evolution history of a strategy.
  """
  def get_strategy_history(strategy_id) do
    AdaptiveStrategyFormulation.get_strategy_history(strategy_id)
  end
  
  @doc """
  Deletes a strategy.
  """
  def delete_strategy(strategy_id) do
    AdaptiveStrategyFormulation.delete_strategy(strategy_id)
  end
  
  @doc """
  Combines multiple strategies into a composite strategy.
  """
  def combine_strategies(strategy_ids, name, description, combination_params \\ %{}) do
    AdaptiveStrategyFormulation.combine_strategies(strategy_ids, name, description, combination_params)
  end
  
  # Integrated operations
  
  @doc """
  Creates a learning agent with strategy support.
  
  This combines the Multi-Agent RL and Adaptive Strategy systems
  to create an agent that can both learn and use strategies.
  """
  def create_strategic_agent(agent_id, domain_id, algorithm, strategy_components, params \\ %{}) do
    # Register agent
    {:ok, agent_id} = register_agent(agent_id, algorithm, params)
    
    # Create initial strategy
    {:ok, strategy_id} = create_strategy(
      domain_id,
      "#{agent_id}_initial_strategy",
      "Initial strategy for agent #{agent_id}",
      strategy_components,
      params
    )
    
    # Link agent and strategy in knowledge system
    KnowledgeSystem.create_triple(
      agent_id,
      :uses_strategy,
      strategy_id,
      :system,
      %{confidence: 1.0}
    )
    
    {:ok, %{agent_id: agent_id, strategy_id: strategy_id}}
  end
  
  @doc """
  Learns a strategy from agent experiences.
  
  This combines Multi-Agent RL with Adaptive Strategy Formulation
  to extract a strategy from learned policies.
  """
  def learn_strategy_from_agent(agent_id, domain_id, name, description, params \\ %{}) do
    # Get agent's policy
    {:ok, policy} = get_policy(agent_id)
    
    # Extract strategy components from policy
    components = extract_strategy_components(policy, params)
    
    # Create strategy
    create_strategy(domain_id, name, description, components, params)
  end
  
  @doc """
  Evolves knowledge based on agent experiences.
  
  This combines Multi-Agent RL with Collective Knowledge Evolution
  to update knowledge based on agent learning.
  """
  def evolve_knowledge_from_agents(agent_ids, knowledge_source_id, params \\ %{}) do
    # Get agent metrics and policies
    agent_data = 
      Enum.map(agent_ids, fn id ->
        {:ok, metrics} = get_agent_metrics(id)
        {:ok, policy} = get_policy(id)
        
        %{id: id, metrics: metrics, policy: policy}
      end)
    
    # Create observations from agent data
    observations = 
      Enum.flat_map(agent_data, fn agent ->
        create_observations_from_agent(agent, params)
      end)
    
    # Add observations to knowledge evolution
    Enum.each(observations, fn obs ->
      add_observation(obs, knowledge_source_id, %{auto_process: true})
    end)
    
    # Trigger knowledge refinement
    refine_knowledge(%{refinement_type: :targeted, target_area: :agent_experiences})
  end
  
  # Private helper functions
  
  defp extract_strategy_components(policy, params) do
    # Extract strategic components from policy
    # This is a placeholder implementation
    
    # Extract key states and their actions
    threshold = Map.get(params, :importance_threshold, 0.5)
    
    # In a real implementation, would analyze the policy to identify important components
    # Here, just select some random policy entries to simulate
    policy
    |> Enum.take_random(5)
    |> Enum.map(fn {state, action} ->
      %{
        state: state,
        action: action,
        importance: :rand.uniform()
      }
    end)
    |> Enum.filter(fn component -> component.importance > threshold end)
  end
  
  defp create_observations_from_agent(agent, _params) do
    # Create knowledge observations from agent data
    # This is a placeholder implementation
    
    # Create observations about agent performance
    performance_obs = %{
      type: :fact,
      content: %{
        agent_id: agent.id,
        total_reward: agent.metrics.total_reward,
        total_experiences: agent.metrics.total_experiences,
        average_reward: agent.metrics.average_reward
      }
    }
    
    # Create observations about agent policy
    policy_states = Map.keys(agent.policy) |> Enum.take(3)
    
    policy_obs = 
      Enum.map(policy_states, fn state ->
        %{
          type: :relation,
          content: %{
            agent_id: agent.id,
            state: state,
            chosen_action: agent.policy[state]
          }
        }
      end)
    
    [performance_obs | policy_obs]
  end
end