object

defmodule Object.Subtypes do
  @moduledoc """
  Object subtypes implementation based on AAOS specification.
  Defines specialized object types: AI Agents, Human Clients, Sensor Objects,
  Actuator Objects, and Coordinator Objects.
  """


  # AI Agent Object
  defmodule AIAgent do
    @moduledoc """
    Autonomous AI Agent with advanced learning and reasoning capabilities.
    Implements full OORL capabilities including meta-learning and self-modification.
    """

    defstruct [
      :base_object,
      :intelligence_level,
      :learning_algorithms,
      :reasoning_engine,
      :autonomy_level,
      :specialization,
      :knowledge_base,
      :decision_tree,
      :performance_metrics
    ]

    @doc """
    Creates a new AI Agent with advanced learning and reasoning capabilities.
    
    ## Parameters
    
    - `opts` - Configuration options:
      - `:id` - Agent identifier (generates random if not provided)
      - `:intelligence_level` - Level of intelligence (`:basic`, `:intermediate`, `:advanced`)
      - `:specialization` - Agent specialization (`:general`, `:problem_solving`, etc.)
      - `:autonomy_level` - Degree of autonomy (`:low`, `:medium`, `:high`)
      - `:goal` - Custom goal function
    
    ## Returns
    
    AIAgent struct with initialized capabilities and performance metrics
    
    ## Examples
    
        iex> Object.Subtypes.AIAgent.new(intelligence_level: :advanced)
        %Object.Subtypes.AIAgent{intelligence_level: :advanced, ...}
    """
    def new(opts \\ []) do
      base = Object.new([
        id: Keyword.get(opts, :id, "ai_agent_#{:rand.uniform(10000)}"),
        methods: [:learn, :reason, :plan, :execute, :adapt, :self_modify],
        goal: Keyword.get(opts, :goal, &maximize_performance/1)
      ] ++ opts)

      %__MODULE__{
        base_object: base,
        intelligence_level: Keyword.get(opts, :intelligence_level, :advanced),
        learning_algorithms: [:q_learning, :policy_gradient, :meta_learning],
        reasoning_engine: :symbolic_neural_hybrid,
        autonomy_level: Keyword.get(opts, :autonomy_level, :high),
        specialization: Keyword.get(opts, :specialization, :general),
        knowledge_base: %{},
        decision_tree: %{},
        performance_metrics: %{accuracy: 0.0, efficiency: 0.0, adaptability: 0.0}
      }
    end

    @doc """
    Executes advanced multi-step reasoning process for complex problems.
    
    Performs systematic problem-solving using analyze, plan, execute, evaluate,
    and adapt phases. Updates performance metrics based on reasoning outcomes.
    
    ## Parameters
    
    - `agent` - AI Agent struct
    - `problem_context` - Context information about the problem to solve
    
    ## Returns
    
    Tuple with updated agent and list of reasoning step results
    
    ## Examples
    
        iex> AIAgent.execute_advanced_reasoning(agent, %{type: :optimization})
        {updated_agent, [{:analyze, :analysis_complete}, ...]}
    """
    def execute_advanced_reasoning(%__MODULE__{} = agent, problem_context) do
      # Multi-step reasoning process
      steps = [
        {:analyze, analyze_problem(agent, problem_context)},
        {:plan, generate_plan(agent, problem_context)},
        {:execute, execute_plan(agent, problem_context)},
        {:evaluate, evaluate_results(agent, problem_context)},
        {:adapt, adapt_strategy(agent, problem_context)}
      ]

      Enum.reduce(steps, {agent, []}, fn {step_type, step_fn}, {acc_agent, acc_results} ->
        result = step_fn
        updated_agent = update_performance_metrics(acc_agent, step_type, result)
        {updated_agent, [{step_type, result} | acc_results]}
      end)
    end

    @doc """
    Performs self-modification using meta-DSL constructs.
    
    Enables the AI agent to modify its own behavior and capabilities based
    on performance feedback and adaptation requirements.
    
    ## Parameters
    
    - `agent` - AI Agent struct
    - `modification_context` - Context for the modification including performance feedback
    
    ## Returns
    
    Updated AI Agent with modified capabilities and improved performance metrics
    """
    def self_modify(%__MODULE__{} = agent, modification_context) do
      # Implement meta-DSL self-modification
      modifications = Object.apply_meta_dsl(agent.base_object, :refine, modification_context)
      
      updated_base = Object.update_state(agent.base_object, modifications.state_updates || %{})
      
      %{agent |
        base_object: updated_base,
        performance_metrics: update_metrics_from_modification(agent.performance_metrics, modifications)
      }
    end

    defp maximize_performance(state) do
      # Goal function that balances multiple performance aspects
      accuracy = Map.get(state, :accuracy, 0.0)
      efficiency = Map.get(state, :efficiency, 0.0)
      adaptability = Map.get(state, :adaptability, 0.0)
      
      0.4 * accuracy + 0.3 * efficiency + 0.3 * adaptability
    end

    defp analyze_problem(_agent, _context), do: :analysis_complete
    defp generate_plan(_agent, _context), do: :plan_generated
    defp execute_plan(_agent, _context), do: :plan_executed
    defp evaluate_results(_agent, _context), do: :results_evaluated
    defp adapt_strategy(_agent, _context), do: :strategy_adapted

    defp update_performance_metrics(agent, step_type, result) do
      # Update metrics based on step performance
      current_metrics = agent.performance_metrics
      improvement = if result == :success, do: 0.01, else: -0.005
      
      updated_metrics = case step_type do
        :analyze -> %{current_metrics | accuracy: current_metrics.accuracy + improvement}
        :execute -> %{current_metrics | efficiency: current_metrics.efficiency + improvement}
        :adapt -> %{current_metrics | adaptability: current_metrics.adaptability + improvement}
        _ -> current_metrics
      end

      %{agent | performance_metrics: updated_metrics}
    end

    defp update_metrics_from_modification(metrics, _modifications) do
      # Self-modification improves adaptability
      %{metrics | adaptability: min(1.0, metrics.adaptability + 0.05)}
    end
  end

  # Human Client Object
  defmodule HumanClient do
    @moduledoc """
    Human client interface with natural language processing and preference learning.
    Handles human-AI interaction patterns and learns user preferences.
    """

    defstruct [
      :base_object,
      :user_profile,
      :communication_style,
      :preference_model,
      :interaction_history,
      :trust_level,
      :expertise_domain,
      :response_patterns
    ]

    @doc """
    Creates a new Human Client interface for human-AI interaction.
    
    ## Parameters
    
    - `opts` - Configuration options:
      - `:id` - Client identifier
      - `:user_profile` - User profile information
      - `:communication_style` - Preferred communication style
      - `:expertise_domain` - Domain of expertise
      - `:goal` - Custom goal function for satisfaction
    
    ## Returns
    
    HumanClient struct initialized with preference learning capabilities
    """
    def new(opts \\ []) do
      base = Object.new([
        id: Keyword.get(opts, :id, "human_client_#{:rand.uniform(10000)}"),
        methods: [:communicate, :express_preference, :provide_feedback, :request_service],
        goal: Keyword.get(opts, :goal, &maximize_satisfaction/1)
      ] ++ opts)

      %__MODULE__{
        base_object: base,
        user_profile: Keyword.get(opts, :user_profile, %{}),
        communication_style: Keyword.get(opts, :communication_style, :natural_language),
        preference_model: %{},
        interaction_history: [],
        trust_level: 0.5,
        expertise_domain: Keyword.get(opts, :expertise_domain, :general),
        response_patterns: %{}
      }
    end

    @doc """
    Processes natural language input using NLP pipeline.
    
    Analyzes input text for intent, extracts preferences, and performs
    emotional analysis. Updates the client's interaction history and
    preference model based on the processed input.
    
    ## Parameters
    
    - `client` - HumanClient struct
    - `input_text` - Natural language input to process
    
    ## Returns
    
    Tuple with parsed intent and updated client with learned preferences
    """
    def process_natural_language(%__MODULE__{} = client, input_text) do
      # NLP processing pipeline
      parsed_intent = parse_intent(input_text)
      extracted_preferences = extract_preferences(input_text, client.preference_model)
      emotional_context = analyze_emotion(input_text)

      updated_client = %{client |
        preference_model: Map.merge(client.preference_model, extracted_preferences),
        interaction_history: [%{
          timestamp: DateTime.utc_now(),
          input: input_text,
          intent: parsed_intent,
          emotion: emotional_context
        } | client.interaction_history]
      }

      {parsed_intent, updated_client}
    end

    @doc """
    Updates the client's trust level based on interaction outcomes.
    
    Adjusts trust level positively for positive outcomes and negatively
    for negative outcomes, maintaining bounds between 0.0 and 1.0.
    
    ## Parameters
    
    - `client` - HumanClient struct
    - `interaction_outcome` - Outcome (`:positive`, `:neutral`, `:negative`)
    
    ## Returns
    
    Updated HumanClient with adjusted trust level
    """
    def update_trust(%__MODULE__{} = client, interaction_outcome) do
      adjustment = case interaction_outcome do
        :positive -> 0.1
        :neutral -> 0.0
        :negative -> -0.15
      end

      new_trust = max(0.0, min(1.0, client.trust_level + adjustment))
      %{client | trust_level: new_trust}
    end

    @doc """
    Provides feedback on a service and updates preference model.
    
    Records feedback with rating and comments, then updates the preference
    model to learn from the user's feedback patterns.
    
    ## Parameters
    
    - `client` - HumanClient struct
    - `service_id` - Identifier of the service being rated
    - `rating` - Numerical rating (typically 1-5)
    - `comments` - Optional textual feedback (default: "")
    
    ## Returns
    
    Updated HumanClient with feedback recorded and preferences updated
    """
    def provide_feedback(%__MODULE__{} = client, service_id, rating, comments \\ "") do
      feedback = %{
        service_id: service_id,
        rating: rating,
        comments: comments,
        timestamp: DateTime.utc_now(),
        trust_context: client.trust_level
      }

      # Update preference model based on feedback
      updated_preferences = learn_from_feedback(client.preference_model, feedback)

      %{client |
        preference_model: updated_preferences,
        interaction_history: [feedback | client.interaction_history]
      }
    end

    defp maximize_satisfaction(state) do
      # Human satisfaction goal function
      trust = Map.get(state, :trust_level, 0.5)
      response_quality = Map.get(state, :response_quality, 0.5)
      ease_of_use = Map.get(state, :ease_of_use, 0.5)
      
      0.4 * trust + 0.3 * response_quality + 0.3 * ease_of_use
    end

    defp parse_intent(_text) do
      # Simplified intent parsing
      [:request_info, :make_complaint, :express_satisfaction, :ask_question]
      |> Enum.random()
    end

    defp extract_preferences(_text, current_preferences) do
      # Extract user preferences from text
      Map.merge(current_preferences, %{
        style: :conversational,
        detail_level: :medium,
        response_speed: :fast
      })
    end

    defp analyze_emotion(_text) do
      # Simple emotion analysis
      [:positive, :neutral, :negative, :excited, :frustrated]
      |> Enum.random()
    end

    defp learn_from_feedback(preferences, feedback) do
      # Update preferences based on user feedback
      case feedback.rating do
        rating when rating >= 4 -> 
          Map.put(preferences, :successful_pattern, feedback.service_id)
        rating when rating <= 2 ->
          Map.put(preferences, :avoid_pattern, feedback.service_id)
        _ ->
          preferences
      end
    end
  end

  # Sensor Object
  defmodule SensorObject do
    @moduledoc """
    Specialized object for environmental sensing and data collection.
    """

    defstruct [
      :base_object,
      :sensor_type,
      :measurement_range,
      :accuracy,
      :sampling_rate,
      :calibration_status,
      :data_buffer,
      :noise_model
    ]

    @doc """
    Creates a new Sensor Object for environmental monitoring.
    
    ## Parameters
    
    - `opts` - Configuration options:
      - `:id` - Sensor identifier
      - `:sensor_type` - Type of sensor (`:temperature`, `:humidity`, `:pressure`, etc.)
      - `:measurement_range` - Valid measurement range as tuple
      - `:accuracy` - Measurement accuracy (0.0-1.0)
      - `:sampling_rate` - Sampling frequency in Hz
    
    ## Returns
    
    SensorObject struct configured for data collection and calibration
    """
    def new(opts \\ []) do
      base = Object.new([
        id: Keyword.get(opts, :id, "sensor_#{:rand.uniform(10000)}"),
        methods: [:sense, :calibrate, :filter_noise, :transmit_data],
        goal: Keyword.get(opts, :goal, &maximize_data_quality/1)
      ] ++ opts)

      %__MODULE__{
        base_object: base,
        sensor_type: Keyword.get(opts, :sensor_type, :generic),
        measurement_range: Keyword.get(opts, :measurement_range, {0.0, 100.0}),
        accuracy: Keyword.get(opts, :accuracy, 0.95),
        sampling_rate: Keyword.get(opts, :sampling_rate, 1.0), # Hz
        calibration_status: :calibrated,
        data_buffer: [],
        noise_model: %{mean: 0.0, std: 0.1}
      }
    end

    @doc """
    Performs environmental sensing with noise modeling.
    
    Takes measurements from the environment, applies sensor noise model,
    and updates the internal data buffer with timestamped measurements.
    
    ## Parameters
    
    - `sensor` - SensorObject struct
    - `environment_state` - Current environmental conditions
    
    ## Returns
    
    Updated SensorObject with new measurement in data buffer
    """
    def sense(%__MODULE__{} = sensor, environment_state) do
      # Simulate sensing with noise
      raw_value = extract_sensor_value(environment_state, sensor.sensor_type)
      noisy_value = add_sensor_noise(raw_value, sensor.noise_model)
      
      measurement = %{
        value: noisy_value,
        timestamp: DateTime.utc_now(),
        sensor_id: sensor.base_object.id,
        confidence: sensor.accuracy
      }

      updated_buffer = [measurement | Enum.take(sensor.data_buffer, 99)]
      
      %{sensor |
        data_buffer: updated_buffer,
        base_object: Object.update_state(sensor.base_object, %{last_reading: noisy_value})
      }
    end

    @doc """
    Calibrates the sensor against reference values.
    
    Compares sensor readings with known reference values to calculate
    calibration error and adjust accuracy. Updates calibration status.
    
    ## Parameters
    
    - `sensor` - SensorObject struct
    - `reference_values` - Known reference values for calibration
    
    ## Returns
    
    Updated SensorObject with adjusted accuracy and calibration status
    """
    def calibrate(%__MODULE__{} = sensor, reference_values) do
      # Perform sensor calibration
      calibration_error = calculate_calibration_error(sensor.data_buffer, reference_values)
      
      updated_accuracy = max(0.5, min(1.0, sensor.accuracy - calibration_error * 0.1))
      
      %{sensor |
        accuracy: updated_accuracy,
        calibration_status: if(calibration_error < 0.1, do: :calibrated, else: :needs_calibration)
      }
    end

    defp maximize_data_quality(state) do
      # Sensor goal: high accuracy, low noise, good calibration
      accuracy = Map.get(state, :accuracy, 0.0)
      data_freshness = Map.get(state, :data_freshness, 0.0)
      calibration_score = Map.get(state, :calibration_score, 0.0)
      
      0.5 * accuracy + 0.3 * data_freshness + 0.2 * calibration_score
    end

    defp extract_sensor_value(environment_state, sensor_type) do
      case sensor_type do
        :temperature -> Map.get(environment_state, :temperature, 20.0)
        :humidity -> Map.get(environment_state, :humidity, 50.0)
        :pressure -> Map.get(environment_state, :pressure, 1013.25)
        _ -> :rand.uniform() * 100
      end
    end

    defp add_sensor_noise(value, noise_model) do
      noise = :rand.normal() * noise_model.std + noise_model.mean
      value + noise
    end

    defp calculate_calibration_error(_data_buffer, _reference_values) do
      # Simplified calibration error calculation
      :rand.uniform() * 0.2
    end
  end

  # Actuator Object
  defmodule ActuatorObject do
    @moduledoc """
    Object for environmental manipulation and action execution.
    """

    defstruct [
      :base_object,
      :actuator_type,
      :action_range,
      :precision,
      :response_time,
      :energy_consumption,
      :wear_level,
      :action_queue
    ]

    @doc """
    Creates a new Actuator Object for environmental manipulation.
    
    ## Parameters
    
    - `opts` - Configuration options:
      - `:id` - Actuator identifier
      - `:actuator_type` - Type of actuator (`:motor`, `:hydraulic`, `:pneumatic`)
      - `:action_range` - Valid action range as tuple
      - `:precision` - Action precision (0.0-1.0)
      - `:response_time` - Response time in seconds
    
    ## Returns
    
    ActuatorObject struct configured for action execution and queuing
    """
    def new(opts \\ []) do
      base = Object.new([
        id: Keyword.get(opts, :id, "actuator_#{:rand.uniform(10000)}"),
        methods: [:execute_action, :queue_action, :calibrate_motion, :monitor_wear],
        goal: Keyword.get(opts, :goal, &maximize_execution_efficiency/1)
      ] ++ opts)

      %__MODULE__{
        base_object: base,
        actuator_type: Keyword.get(opts, :actuator_type, :generic),
        action_range: Keyword.get(opts, :action_range, {-100.0, 100.0}),
        precision: Keyword.get(opts, :precision, 0.9),
        response_time: Keyword.get(opts, :response_time, 0.1), # seconds
        energy_consumption: 0.0,
        wear_level: 0.0,
        action_queue: []
      }
    end

    @doc """
    Executes a physical action and updates actuator state.
    
    Performs the specified action, calculates energy consumption and wear,
    then updates the actuator's internal state and metrics.
    
    ## Parameters
    
    - `actuator` - ActuatorObject struct
    - `action_command` - Command specifying the action to execute
    
    ## Returns
    
    Tuple with execution result and updated actuator with wear/energy updates
    """
    def execute_action(%__MODULE__{} = actuator, action_command) do
      # Execute physical action
      execution_result = perform_action(actuator, action_command)
      
      # Update wear and energy consumption
      energy_cost = calculate_energy_cost(action_command, actuator.actuator_type)
      wear_increase = calculate_wear_increase(action_command, actuator.precision)
      
      updated_actuator = %{actuator |
        energy_consumption: actuator.energy_consumption + energy_cost,
        wear_level: min(1.0, actuator.wear_level + wear_increase),
        base_object: Object.update_state(actuator.base_object, %{
          last_action: action_command,
          execution_result: execution_result
        })
      }

      {execution_result, updated_actuator}
    end

    @doc """
    Queues an action for later execution with priority ordering.
    
    Adds an action to the execution queue, sorted by priority and timestamp.
    Higher priority actions are executed first.
    
    ## Parameters
    
    - `actuator` - ActuatorObject struct
    - `action_command` - Command to queue for execution
    - `priority` - Priority level (`:critical`, `:high`, `:normal`, `:low`)
    
    ## Returns
    
    Updated ActuatorObject with action added to sorted queue
    """
    def queue_action(%__MODULE__{} = actuator, action_command, priority \\ :normal) do
      queued_action = %{
        command: action_command,
        priority: priority,
        queued_at: DateTime.utc_now()
      }

      updated_queue = [queued_action | actuator.action_queue]
                     |> Enum.sort_by(fn action -> 
                       {priority_to_number(action.priority), action.queued_at}
                     end)

      %{actuator | action_queue: updated_queue}
    end

    defp maximize_execution_efficiency(state) do
      # Actuator goal: high precision, low energy, minimal wear
      precision = Map.get(state, :precision, 0.0)
      energy_efficiency = 1.0 - Map.get(state, :energy_consumption, 0.0) / 100.0
      durability = 1.0 - Map.get(state, :wear_level, 0.0)
      
      0.4 * precision + 0.3 * energy_efficiency + 0.3 * durability
    end

    defp perform_action(_actuator, action_command) do
      # Simulate action execution
      success_probability = 0.9
      if :rand.uniform() < success_probability do
        %{status: :success, actual_result: action_command.target_value}
      else
        %{status: :partial_success, actual_result: action_command.target_value * 0.8}
      end
    end

    defp calculate_energy_cost(action_command, actuator_type) do
      base_cost = case actuator_type do
        :motor -> 5.0
        :hydraulic -> 8.0
        :pneumatic -> 3.0
        _ -> 4.0
      end

      magnitude = abs(Map.get(action_command, :magnitude, 1.0))
      base_cost * magnitude
    end

    defp calculate_wear_increase(action_command, precision) do
      base_wear = 0.001
      stress_factor = abs(Map.get(action_command, :magnitude, 1.0))
      precision_factor = 1.0 / max(0.1, precision)
      
      base_wear * stress_factor * precision_factor
    end

    defp priority_to_number(:critical), do: 0
    defp priority_to_number(:high), do: 1
    defp priority_to_number(:normal), do: 2
    defp priority_to_number(:low), do: 3
  end

  # Coordinator Object
  defmodule CoordinatorObject do
    @moduledoc """
    Coordination and orchestration object for multi-agent systems.
    """

    defstruct [
      :base_object,
      :managed_objects,
      :coordination_strategy,
      :conflict_resolution,
      :resource_allocation,
      :performance_monitoring,
      :coordination_history
    ]

    @doc """
    Creates a new Coordinator Object for multi-agent orchestration.
    
    ## Parameters
    
    - `opts` - Configuration options:
      - `:id` - Coordinator identifier
      - `:strategy` - Coordination strategy (`:consensus`, `:delegation`, `:hierarchy`)
      - `:goal` - Custom goal function for system optimization
    
    ## Returns
    
    CoordinatorObject struct configured for multi-agent coordination
    """
    def new(opts \\ []) do
      base = Object.new([
        id: Keyword.get(opts, :id, "coordinator_#{:rand.uniform(10000)}"),
        methods: [:coordinate, :resolve_conflicts, :allocate_resources, :monitor_performance],
        goal: Keyword.get(opts, :goal, &maximize_system_performance/1)
      ] ++ opts)

      %__MODULE__{
        base_object: base,
        managed_objects: [],
        coordination_strategy: Keyword.get(opts, :strategy, :consensus),
        conflict_resolution: :negotiation,
        resource_allocation: %{},
        performance_monitoring: %{},
        coordination_history: []
      }
    end

    @doc """
    Adds an object to the coordinator's management scope.
    
    ## Parameters
    
    - `coordinator` - CoordinatorObject struct
    - `object_id` - ID of the object to manage
    
    ## Returns
    
    Updated CoordinatorObject with the object added to managed list
    """
    def add_managed_object(%__MODULE__{} = coordinator, object_id) do
      updated_objects = [object_id | coordinator.managed_objects] |> Enum.uniq()
      %{coordinator | managed_objects: updated_objects}
    end

    @doc """
    Coordinates managed objects to complete a task.
    
    Generates and executes a coordination plan for the specified task,
    then records the coordination history and results.
    
    ## Parameters
    
    - `coordinator` - CoordinatorObject struct
    - `coordination_task` - Task specification for coordination
    
    ## Returns
    
    Updated CoordinatorObject with coordination history updated
    """
    def coordinate_objects(%__MODULE__{} = coordinator, coordination_task) do
      # Implement coordination algorithm
      coordination_plan = generate_coordination_plan(coordinator, coordination_task)
      execution_results = execute_coordination_plan(coordinator, coordination_plan)
      
      coordination_record = %{
        task: coordination_task,
        plan: coordination_plan,
        results: execution_results,
        timestamp: DateTime.utc_now()
      }

      updated_history = [coordination_record | coordinator.coordination_history]
      
      %{coordinator |
        coordination_history: updated_history,
        base_object: Object.update_state(coordinator.base_object, %{
          last_coordination: coordination_record
        })
      }
    end

    @doc """
    Resolves conflicts between managed objects.
    
    Uses the configured conflict resolution strategy to resolve disputes
    between objects under management.
    
    ## Parameters
    
    - `coordinator` - CoordinatorObject struct
    - `conflict_context` - Information about the conflict to resolve
    
    ## Returns
    
    Tuple with resolution result and updated coordinator
    """
    def resolve_conflict(%__MODULE__{} = coordinator, conflict_context) do
      # Conflict resolution based on strategy
      resolution = case coordinator.conflict_resolution do
        :negotiation -> negotiate_resolution(conflict_context)
        :arbitration -> arbitrate_resolution(conflict_context)
        :voting -> vote_resolution(conflict_context)
        _ -> default_resolution(conflict_context)
      end

      updated_coordinator = Object.update_state(coordinator.base_object, %{
        last_conflict_resolution: resolution
      })

      {resolution, %{coordinator | base_object: updated_coordinator}}
    end

    defp maximize_system_performance(state) do
      # Coordinator goal: optimize overall system performance
      coordination_efficiency = Map.get(state, :coordination_efficiency, 0.0)
      conflict_resolution_rate = Map.get(state, :conflict_resolution_rate, 0.0)
      resource_utilization = Map.get(state, :resource_utilization, 0.0)
      
      0.4 * coordination_efficiency + 0.3 * conflict_resolution_rate + 0.3 * resource_utilization
    end

    defp generate_coordination_plan(_coordinator, _task) do
      # Generate coordination plan
      %{
        strategy: :parallel_execution,
        object_assignments: %{},
        dependencies: [],
        timeline: []
      }
    end

    defp execute_coordination_plan(_coordinator, _plan) do
      # Execute the coordination plan
      %{
        success_rate: 0.85,
        completed_tasks: 5,
        total_tasks: 6,
        execution_time: 120
      }
    end

    defp negotiate_resolution(_context), do: %{method: :negotiation, outcome: :compromise}
    defp arbitrate_resolution(_context), do: %{method: :arbitration, outcome: :decision}
    defp vote_resolution(_context), do: %{method: :voting, outcome: :majority}
    defp default_resolution(_context), do: %{method: :default, outcome: :random}
  end
end