defmodule Object.MetaDSL do
  @moduledoc """
  Self-Reflective Meta-DSL implementation based on AAOS specification.
  
  Provides primitives and constructs for objects to reason about and modify
  their own learning process, enabling "learning to learn" capabilities.
  
  Core constructs include:
  - DEFINE: Define new attributes, methods, or sub-objects
  - GOAL: Query or modify the object's goal function
  - BELIEF: Update or query the object's beliefs about the environment
  - INFER: Perform probabilistic inference using the world model
  - DECIDE: Make decisions based on current state and goals
  - LEARN: Update learning parameters or strategies
  - REFINE: Meta-learning to improve learning efficiency
  """

  defstruct [
    :constructs,
    :execution_context,
    :learning_parameters,
    :self_modification_history,
    :meta_knowledge_base,
    :adaptation_triggers
  ]

  @type t :: %__MODULE__{
    constructs: [atom()],
    execution_context: map(),
    learning_parameters: map(),
    self_modification_history: [modification_record()],
    meta_knowledge_base: map(),
    adaptation_triggers: [trigger()]
  }

  @type modification_record :: %{
    timestamp: DateTime.t(),
    construct: atom(),
    modification: any(),
    success: boolean(),
    impact_score: float()
  }

  @type trigger :: %{
    condition: function(),
    action: atom(),
    threshold: float(),
    active: boolean()
  }

  @doc """
  Creates a new Meta-DSL instance with default constructs and parameters.
  
  ## Parameters
  - `opts`: Optional configuration with `:constructs`, `:execution_context`, `:learning_parameters`
  
  ## Returns
  New Meta-DSL struct with initialized constructs
  
  ## Examples
      iex> Object.MetaDSL.new()
      %Object.MetaDSL{constructs: [:define, :goal, :belief, :infer, ...], ...}
  """
  def new(opts \\ []) do
    %__MODULE__{
      constructs: Keyword.get(opts, :constructs, default_constructs()),
      execution_context: Keyword.get(opts, :execution_context, %{}),
      learning_parameters: Keyword.get(opts, :learning_parameters, default_learning_params()),
      self_modification_history: [],
      meta_knowledge_base: initialize_meta_knowledge(),
      adaptation_triggers: initialize_adaptation_triggers()
    }
  end

  @doc """
  Executes a meta-DSL construct with the given arguments.
  
  ## Parameters
  - `meta_dsl`: Meta-DSL system struct
  - `construct`: Construct to execute (`:define`, `:goal`, `:belief`, `:infer`, `:decide`, `:learn`, `:refine`)
  - `object`: Object to apply construct to
  - `args`: Arguments for the construct
  
  ## Returns
  `{:ok, result, updated_meta_dsl}` on success, `{:error, reason}` on failure
  """
  def execute(%__MODULE__{} = meta_dsl, construct, object, args) do
    if construct in meta_dsl.constructs do
      case apply_construct(construct, meta_dsl, object, args) do
        {:ok, result, updated_meta_dsl} ->
          final_meta_dsl = record_modification(updated_meta_dsl, construct, args, true, calculate_impact(result))
          {:ok, result, final_meta_dsl}
        
        {:error, _reason} = error ->
          _final_meta_dsl = record_modification(meta_dsl, construct, args, false, 0.0)
          error
      end
    else
      {:error, {:unknown_construct, construct}}
    end
  end

  @doc """
  DEFINE construct: Defines new attributes, methods, or sub-objects.
  
  ## Parameters
  - `meta_dsl`: Meta-DSL system struct
  - `object`: Object to modify
  - `definition`: Definition tuple like `{:attribute, name, value}` or `{:method, name, impl}`
  
  ## Returns
  `{:ok, updated_object, updated_meta_dsl}` on success
  """
  def define(%__MODULE__{} = meta_dsl, object, definition) do
    case definition do
      {:attribute, name, initial_value} ->
        define_attribute(meta_dsl, object, name, initial_value)
      
      {:method, name, implementation} ->
        define_method(meta_dsl, object, name, implementation)
      
      {:sub_object, name, spec} ->
        define_sub_object(meta_dsl, object, name, spec)
      
      {:goal, goal_function} ->
        define_goal(meta_dsl, object, goal_function)
      
      _ ->
        {:error, {:invalid_definition, definition}}
    end
  end

  @doc """
  GOAL construct: Query or modify the object's goal function.
  
  ## Parameters
  - `meta_dsl`: Meta-DSL system struct
  - `object`: Object to operate on
  - `operation`: `:query`, `{:modify, new_goal}`, or `{:compose, goal_functions}`
  
  ## Returns
  `{:ok, result, updated_meta_dsl}` where result depends on operation
  """
  def goal(%__MODULE__{} = meta_dsl, object, operation) do
    case operation do
      :query ->
        {:ok, object.goal, meta_dsl}
      
      {:modify, new_goal} when is_function(new_goal) ->
        updated_object = %{object | goal: new_goal}
        updated_meta_dsl = update_meta_knowledge(meta_dsl, :goal_modification, new_goal)
        {:ok, updated_object, updated_meta_dsl}
      
      {:compose, goal_functions} when is_list(goal_functions) ->
        composed_goal = compose_goals(goal_functions)
        updated_object = %{object | goal: composed_goal}
        {:ok, updated_object, meta_dsl}
      
      _ ->
        {:error, {:invalid_goal_operation, operation}}
    end
  end

  @doc """
  BELIEF construct: Update or query the object's beliefs about the environment.
  
  ## Parameters
  - `meta_dsl`: Meta-DSL system struct
  - `object`: Object to operate on
  - `operation`: `:query`, `{:update, key, value}`, or `{:uncertainty, key, uncertainty}`
  
  ## Returns
  `{:ok, result, updated_meta_dsl}` with beliefs or updated object
  """
  def belief(%__MODULE__{} = meta_dsl, object, operation) do
    case operation do
      :query ->
        {:ok, object.world_model.beliefs, meta_dsl}
      
      {:update, belief_key, belief_value} ->
        updated_beliefs = Map.put(object.world_model.beliefs, belief_key, belief_value)
        updated_world_model = %{object.world_model | beliefs: updated_beliefs}
        updated_object = %{object | world_model: updated_world_model}
        {:ok, updated_object, meta_dsl}
      
      {:uncertainty, belief_key, uncertainty_value} ->
        updated_uncertainties = Map.put(object.world_model.uncertainties, belief_key, uncertainty_value)
        updated_world_model = %{object.world_model | uncertainties: updated_uncertainties}
        updated_object = %{object | world_model: updated_world_model}
        {:ok, updated_object, meta_dsl}
      
      _ ->
        {:error, {:invalid_belief_operation, operation}}
    end
  end

  @doc """
  INFER construct: Perform probabilistic inference using the world model.
  
  ## Parameters
  
  - `meta_dsl` - Meta-DSL system struct
  - `object` - Object to perform inference on
  - `inference_query` - Query specification:
    - `{:bayesian_update, evidence}` - Bayesian belief update
    - `{:predict, state, horizon}` - State prediction
    - `{:causal, cause, effect}` - Causal inference
  
  ## Returns
  
  `{:ok, inference_result, updated_meta_dsl}` with inference results
  """
  def infer(%__MODULE__{} = meta_dsl, object, inference_query) do
    case inference_query do
      {:bayesian_update, evidence} ->
        perform_bayesian_inference(meta_dsl, object, evidence)
      
      {:predict, state, horizon} ->
        perform_prediction(meta_dsl, object, state, horizon)
      
      {:causal, cause, effect} ->
        perform_causal_inference(meta_dsl, object, cause, effect)
      
      _ ->
        {:error, {:invalid_inference_query, inference_query}}
    end
  end

  @doc """
  DECIDE construct: Make decisions based on current state and goals.
  
  ## Parameters
  
  - `meta_dsl` - Meta-DSL system struct
  - `object` - Object making the decision
  - `decision_context` - Decision context:
    - `{:action_selection, available_actions}` - Choose optimal action
    - `{:resource_allocation, resources, tasks}` - Allocate resources
    - `{:coalition_formation, potential_partners}` - Form coalitions
  
  ## Returns
  
  `{:ok, decision_result, updated_meta_dsl}` with decision outcome
  """
  def decide(%__MODULE__{} = meta_dsl, object, decision_context) do
    case decision_context do
      {:action_selection, available_actions} ->
        select_optimal_action(meta_dsl, object, available_actions)
      
      {:resource_allocation, resources, tasks} ->
        allocate_resources(meta_dsl, object, resources, tasks)
      
      {:coalition_formation, potential_partners} ->
        decide_coalition_formation(meta_dsl, object, potential_partners)
      
      _ ->
        {:error, {:invalid_decision_context, decision_context}}
    end
  end

  @doc """
  LEARN construct: Update learning parameters or strategies.
  
  ## Parameters
  
  - `meta_dsl` - Meta-DSL system struct
  - `object` - Object updating learning
  - `learning_operation` - Learning operation:
    - `{:update_parameters, new_params}` - Update learning parameters
    - `{:adapt_strategy, performance_feedback}` - Adapt learning strategy
    - `{:transfer_knowledge, source_domain, target_domain}` - Transfer knowledge
  
  ## Returns
  
  `{:ok, learning_result, updated_meta_dsl}` with learning updates
  """
  def learn(%__MODULE__{} = meta_dsl, object, learning_operation) do
    case learning_operation do
      {:update_parameters, new_params} ->
        update_learning_parameters(meta_dsl, object, new_params)
      
      {:adapt_strategy, performance_feedback} ->
        adapt_learning_strategy(meta_dsl, object, performance_feedback)
      
      {:transfer_knowledge, source_domain, target_domain} ->
        transfer_knowledge(meta_dsl, object, source_domain, target_domain)
      
      _ ->
        {:error, {:invalid_learning_operation, learning_operation}}
    end
  end

  @doc """
  REFINE construct: Meta-learning to improve learning efficiency.
  
  ## Parameters
  
  - `meta_dsl` - Meta-DSL system struct
  - `object` - Object being refined
  - `refinement_target` - Target for refinement:
    - `:exploration_strategy` - Improve exploration approach
    - `:reward_function` - Refine reward function
    - `:world_model` - Improve world model accuracy
    - `:meta_parameters` - Adjust meta-learning parameters
  
  ## Returns
  
  `{:ok, refinement_result, updated_meta_dsl}` with refinement updates
  """
  def refine(%__MODULE__{} = meta_dsl, object, refinement_target) do
    case refinement_target do
      :exploration_strategy ->
        refine_exploration_strategy(meta_dsl, object)
      
      :reward_function ->
        refine_reward_function(meta_dsl, object)
      
      :world_model ->
        refine_world_model(meta_dsl, object)
      
      :meta_parameters ->
        refine_meta_parameters(meta_dsl, object)
      
      _ ->
        {:error, {:invalid_refinement_target, refinement_target}}
    end
  end

  @doc """
  Evaluates adaptation triggers and executes automatic adaptations.
  
  ## Parameters
  - `meta_dsl`: Meta-DSL system struct
  - `object`: Object to evaluate triggers for
  - `performance_metrics`: Current performance data
  
  ## Returns
  `{:ok, updated_object, updated_meta_dsl}` with any triggered adaptations applied
  """
  def evaluate_adaptation_triggers(%__MODULE__{} = meta_dsl, object, performance_metrics) do
    active_triggers = Enum.filter(meta_dsl.adaptation_triggers, & &1.active)
    
    triggered_adaptations = for trigger <- active_triggers do
      if trigger.condition.(performance_metrics) do
        {trigger.action, trigger.threshold}
      end
    end |> Enum.reject(&is_nil/1)
    
    if length(triggered_adaptations) > 0 do
      execute_automatic_adaptations(meta_dsl, object, triggered_adaptations)
    else
      {:ok, object, meta_dsl}
    end
  end

  # Private implementation functions

  defp apply_construct(construct, meta_dsl, object, args) do
    case construct do
      :define -> define(meta_dsl, object, args)
      :goal -> goal(meta_dsl, object, args)
      :belief -> belief(meta_dsl, object, args)
      :infer -> infer(meta_dsl, object, args)
      :decide -> decide(meta_dsl, object, args)
      :learn -> learn(meta_dsl, object, args)
      :refine -> refine(meta_dsl, object, args)
      _ -> {:error, {:unknown_construct, construct}}
    end
  end

  defp default_constructs do
    [:define, :goal, :belief, :infer, :decide, :learn, :refine]
  end

  defp default_learning_params do
    %{
      learning_rate: 0.01,
      exploration_rate: 0.1,
      discount_factor: 0.95,
      meta_learning_rate: 0.001,
      adaptation_threshold: 0.1
    }
  end

  defp initialize_meta_knowledge do
    %{
      successful_adaptations: [],
      failed_adaptations: [],
      performance_history: [],
      strategy_effectiveness: %{}
    }
  end

  defp initialize_adaptation_triggers do
    [
      %{
        condition: fn metrics -> Map.get(metrics, :performance_decline, 0) > 0.2 end,
        action: :adapt_learning_rate,
        threshold: 0.2,
        active: true
      },
      %{
        condition: fn metrics -> Map.get(metrics, :exploration_efficiency, 1.0) < 0.5 end,
        action: :refine_exploration,
        threshold: 0.5,
        active: true
      }
    ]
  end

  defp define_attribute(meta_dsl, object, name, value) do
    updated_state = Map.put(object.state, name, value)
    updated_object = %{object | state: updated_state}
    {:ok, updated_object, meta_dsl}
  end

  defp define_method(meta_dsl, object, name, _implementation) do
    updated_methods = [name | object.methods] |> Enum.uniq()
    updated_object = %{object | methods: updated_methods}
    {:ok, updated_object, meta_dsl}
  end

  defp define_sub_object(meta_dsl, object, name, spec) do
    sub_object = Object.new(spec)
    updated_state = Map.put(object.state, :"sub_object_#{name}", sub_object)
    updated_object = %{object | state: updated_state}
    {:ok, updated_object, meta_dsl}
  end

  defp define_goal(meta_dsl, object, goal_function) do
    updated_object = %{object | goal: goal_function}
    {:ok, updated_object, meta_dsl}
  end

  defp compose_goals(goal_functions) do
    fn state ->
      goal_functions
      |> Enum.map(& &1.(state))
      |> Enum.sum()
      |> Kernel./(length(goal_functions))
    end
  end

  defp perform_bayesian_inference(meta_dsl, object, evidence) do
    current_beliefs = object.world_model.beliefs
    
    # Simplified Bayesian update
    updated_beliefs = Map.merge(current_beliefs, evidence)
    updated_world_model = %{object.world_model | beliefs: updated_beliefs}
    updated_object = %{object | world_model: updated_world_model}
    
    {:ok, updated_object, meta_dsl}
  end

  defp perform_prediction(meta_dsl, _object, state, horizon) do
    # Simple prediction based on world model
    prediction = %{
      predicted_state: state,
      confidence: 0.8,
      horizon: horizon,
      timestamp: DateTime.utc_now()
    }
    
    {:ok, prediction, meta_dsl}
  end

  defp perform_causal_inference(meta_dsl, _object, cause, effect) do
    # Simplified causal inference
    causal_strength = :rand.uniform()
    
    result = %{
      cause: cause,
      effect: effect,
      causal_strength: causal_strength,
      confidence: 0.7
    }
    
    {:ok, result, meta_dsl}
  end

  defp select_optimal_action(meta_dsl, object, available_actions) do
    exploration_rate = meta_dsl.learning_parameters.exploration_rate
    
    action = if :rand.uniform() < exploration_rate do
      Enum.random(available_actions)
    else
      # Select action based on goal function
      action_values = for action <- available_actions do
        {action, evaluate_action_value(object, action)}
      end
      
      {best_action, _} = Enum.max_by(action_values, &elem(&1, 1))
      best_action
    end
    
    {:ok, action, meta_dsl}
  end

  defp allocate_resources(meta_dsl, _object, resources, tasks) do
    # Simple resource allocation based on task priorities
    allocation = Enum.zip(resources, tasks) |> Map.new()
    {:ok, allocation, meta_dsl}
  end

  defp decide_coalition_formation(meta_dsl, object, potential_partners) do
    # Decide based on object similarity and potential synergy
    decisions = for partner <- potential_partners do
      similarity = Object.similarity(object, partner)
      {partner.id, similarity > 0.6}
    end
    
    {:ok, decisions, meta_dsl}
  end

  defp update_learning_parameters(meta_dsl, object, new_params) do
    updated_params = Map.merge(meta_dsl.learning_parameters, new_params)
    updated_meta_dsl = %{meta_dsl | learning_parameters: updated_params}
    {:ok, object, updated_meta_dsl}
  end

  defp adapt_learning_strategy(meta_dsl, object, performance_feedback) do
    # Adapt learning strategy based on performance
    adaptation_rate = 0.1
    current_lr = meta_dsl.learning_parameters.learning_rate
    
    new_lr = if performance_feedback > 0 do
      current_lr * (1 + adaptation_rate)
    else
      current_lr * (1 - adaptation_rate)
    end
    
    updated_params = %{meta_dsl.learning_parameters | learning_rate: max(0.001, min(0.1, new_lr))}
    updated_meta_dsl = %{meta_dsl | learning_parameters: updated_params}
    
    {:ok, object, updated_meta_dsl}
  end

  defp transfer_knowledge(meta_dsl, object, _source_domain, _target_domain) do
    # Simplified knowledge transfer
    {:ok, object, meta_dsl}
  end

  defp refine_exploration_strategy(meta_dsl, object) do
    # Refine exploration based on performance history
    current_rate = meta_dsl.learning_parameters.exploration_rate
    new_rate = max(0.01, current_rate * 0.99)  # Gradual decay
    
    updated_params = %{meta_dsl.learning_parameters | exploration_rate: new_rate}
    updated_meta_dsl = %{meta_dsl | learning_parameters: updated_params}
    
    {:ok, object, updated_meta_dsl}
  end

  defp refine_reward_function(meta_dsl, object) do
    # Meta-learning to refine reward function
    {:ok, object, meta_dsl}
  end

  defp refine_world_model(meta_dsl, object) do
    # Refine world model based on prediction accuracy
    {:ok, object, meta_dsl}
  end

  defp refine_meta_parameters(meta_dsl, object) do
    # Meta-meta-learning: refine the meta-learning parameters
    {:ok, object, meta_dsl}
  end

  defp execute_automatic_adaptations(meta_dsl, object, adaptations) do
    {updated_object, updated_meta_dsl} = Enum.reduce(adaptations, {object, meta_dsl}, fn {action, _threshold}, {obj, m_dsl} ->
      case action do
        :adapt_learning_rate ->
          {:ok, obj, new_m_dsl} = adapt_learning_strategy(m_dsl, obj, -0.1)
          {obj, new_m_dsl}
        
        :refine_exploration ->
          {:ok, obj, new_m_dsl} = refine_exploration_strategy(m_dsl, obj)
          {obj, new_m_dsl}
        
        _ ->
          {obj, m_dsl}
      end
    end)
    
    {:ok, updated_object, updated_meta_dsl}
  end

  defp record_modification(meta_dsl, construct, args, success, impact) do
    modification = %{
      timestamp: DateTime.utc_now(),
      construct: construct,
      modification: args,
      success: success,
      impact_score: impact
    }
    
    updated_history = [modification | meta_dsl.self_modification_history]
    %{meta_dsl | self_modification_history: updated_history}
  end

  defp calculate_impact(result) do
    # Calculate impact score based on result
    case result do
      %{} when is_map(result) -> 0.5
      _ -> 0.3
    end
  end

  defp update_meta_knowledge(meta_dsl, knowledge_type, knowledge) do
    updated_knowledge = Map.put(meta_dsl.meta_knowledge_base, knowledge_type, knowledge)
    %{meta_dsl | meta_knowledge_base: updated_knowledge}
  end

  defp evaluate_action_value(object, action) do
    # Simplified action value evaluation based on object state
    base_value = :rand.uniform()
    goal_bonus = if action == :exploit, do: 0.2, else: 0.0
    
    # Factor in object's current goal evaluation 
    state_bonus = if Map.has_key?(object.state, :energy) and object.state.energy > 50, do: 0.1, else: 0.0
    
    base_value + goal_bonus + state_bonus
  end
end