defmodule Automata.DistributedCognition.EmergentSpecialization.CapabilityProfiling do
  @moduledoc """
  Provides mechanisms for discovering, profiling, and tracking agent capabilities within
  a distributed system.
  
  This module enables automatic discovery of agent capabilities, performance monitoring,
  dynamic capability evaluation, and comparative profiling across agents.
  """
  
  alias Automata.DistributedCognition.BeliefArchitecture.DecentralizedBeliefSystem
  
  defmodule CapabilityProfile do
    @moduledoc """
    Represents an agent's capability profile, including capabilities, performance metrics,
    and historical performance data.
    """
    
    @type capability_id :: atom() | String.t()
    @type performance_metric :: %{
      efficiency: float(),
      quality: float(),
      reliability: float(),
      latency: float()
    }
    
    @type t :: %__MODULE__{
      agent_id: term(),
      capabilities: %{capability_id => performance_metric},
      capability_history: %{capability_id => list(performance_metric)},
      specializations: list(capability_id),
      profile_updated_at: DateTime.t()
    }
    
    defstruct [
      :agent_id,
      :capabilities,
      :capability_history,
      :specializations,
      :profile_updated_at
    ]
    
    @doc """
    Creates a new capability profile for an agent.
    """
    def new(agent_id, initial_capabilities \\ %{}) do
      now = DateTime.utc_now()
      
      capability_history = Map.new(initial_capabilities, fn {cap_id, metrics} ->
        {cap_id, [metrics]}
      end)
      
      %__MODULE__{
        agent_id: agent_id,
        capabilities: initial_capabilities,
        capability_history: capability_history,
        specializations: [],
        profile_updated_at: now
      }
    end
    
    @doc """
    Updates a capability's performance metrics in the profile.
    """
    def update_capability(profile, capability_id, metrics) do
      # Get current metrics for the capability
      current_metrics = Map.get(profile.capabilities, capability_id, %{
        efficiency: 0.0,
        quality: 0.0,
        reliability: 0.0,
        latency: 0.0
      })
      
      # Merge the new metrics with the current ones
      updated_metrics = Map.merge(current_metrics, metrics)
      
      # Get current history for the capability
      history = Map.get(profile.capability_history, capability_id, [])
      
      # Update the profile
      %{profile |
        capabilities: Map.put(profile.capabilities, capability_id, updated_metrics),
        capability_history: Map.put(profile.capability_history, capability_id, [updated_metrics | history]),
        profile_updated_at: DateTime.utc_now()
      }
    end
    
    @doc """
    Identifies specializations based on capability performance.
    """
    def identify_specializations(profile, threshold \\ 0.8) do
      # Find capabilities with high performance metrics
      specializations = profile.capabilities
      |> Enum.filter(fn {_cap_id, metrics} ->
        calculate_overall_performance(metrics) >= threshold
      end)
      |> Enum.map(fn {cap_id, _metrics} -> cap_id end)
      
      %{profile | specializations: specializations, profile_updated_at: DateTime.utc_now()}
    end
    
    @doc """
    Calculates the overall performance of an agent for a specific capability.
    """
    def capability_performance(profile, capability_id) do
      case Map.fetch(profile.capabilities, capability_id) do
        {:ok, metrics} ->
          calculate_overall_performance(metrics)
          
        :error ->
          0.0
      end
    end
    
    @doc """
    Gets a time series of performance for a capability.
    """
    def capability_history(profile, capability_id, limit \\ 10) do
      history = Map.get(profile.capability_history, capability_id, [])
      
      # Calculate overall performance for each historical entry
      history
      |> Enum.take(limit)
      |> Enum.map(&calculate_overall_performance/1)
    end
    
    # Private functions
    
    defp calculate_overall_performance(metrics) do
      # Calculate a weighted average of the metrics
      (metrics.efficiency * 0.3) +
      (metrics.quality * 0.3) +
      (metrics.reliability * 0.3) +
      ((1.0 - metrics.latency) * 0.1)  # Lower latency is better
    end
  end
  
  defmodule CapabilityDiscovery do
    @moduledoc """
    Provides mechanisms for discovering and categorizing agent capabilities.
    
    This module enables automatic identification of capabilities through observation,
    self-reporting, and testing.
    """
    
    @doc """
    Discovers capabilities of an agent through observation of its behavior.
    """
    def discover_through_observation(agent_id, interaction_history, context) do
      # Extract patterns from interaction history
      patterns = extract_behavioral_patterns(interaction_history)
      
      # Map patterns to capabilities
      capabilities = map_patterns_to_capabilities(patterns, context)
      
      # Create capability metrics based on observed performance
      capability_metrics = create_capability_metrics(capabilities, interaction_history)
      
      {:ok, capability_metrics}
    end
    
    @doc """
    Discovers capabilities through self-reporting by the agent.
    """
    def discover_through_self_reporting(agent_id) do
      # Request capability report from agent
      # In a real implementation, this would communicate with the agent
      capability_report = request_capability_report(agent_id)
      
      # Validate the self-reported capabilities
      {:ok, validated_capabilities} = validate_self_reported_capabilities(capability_report)
      
      {:ok, validated_capabilities}
    end
    
    @doc """
    Discovers capabilities through targeted testing.
    """
    def discover_through_testing(agent_id, test_suite) do
      # Run test suite on agent
      test_results = run_capability_tests(agent_id, test_suite)
      
      # Analyze test results to identify capabilities
      capabilities = analyze_test_results(test_results)
      
      {:ok, capabilities}
    end
    
    @doc """
    Combines discovery methods to create a comprehensive capability profile.
    """
    def comprehensive_discovery(agent_id, options \\ []) do
      # Use multiple discovery methods
      discovery_results = []
      
      # Observation-based discovery
      if options[:use_observation] do
        interaction_history = get_interaction_history(agent_id)
        context = options[:context] || %{}
        
        {:ok, observation_results} = discover_through_observation(
          agent_id, 
          interaction_history, 
          context
        )
        
        discovery_results = [observation_results | discovery_results]
      end
      
      # Self-reporting discovery
      if options[:use_self_reporting] do
        {:ok, self_report_results} = discover_through_self_reporting(agent_id)
        discovery_results = [self_report_results | discovery_results]
      end
      
      # Testing-based discovery
      if options[:use_testing] do
        test_suite = options[:test_suite] || default_test_suite()
        
        {:ok, testing_results} = discover_through_testing(agent_id, test_suite)
        discovery_results = [testing_results | discovery_results]
      end
      
      # Combine and reconcile results from different methods
      combined_capabilities = combine_discovery_results(discovery_results)
      
      {:ok, combined_capabilities}
    end
    
    # Private functions
    
    defp extract_behavioral_patterns(interaction_history) do
      # In a real implementation, this would analyze the interaction history
      # to identify patterns that indicate capabilities
      
      # For now, return placeholder patterns
      [:pattern1, :pattern2, :pattern3]
    end
    
    defp map_patterns_to_capabilities(patterns, context) do
      # In a real implementation, this would map identified patterns to
      # known capability types based on context
      
      # For now, return placeholder capabilities
      %{
        computation: 0.8,
        communication: 0.6,
        problem_solving: 0.7
      }
    end
    
    defp create_capability_metrics(capabilities, interaction_history) do
      # In a real implementation, this would calculate performance metrics
      # for each capability based on the interaction history
      
      # For now, return placeholder metrics
      Map.new(capabilities, fn {capability, score} ->
        {capability, %{
          efficiency: score,
          quality: score - 0.1,
          reliability: score + 0.1,
          latency: 1.0 - score
        }}
      end)
    end
    
    defp request_capability_report(agent_id) do
      # In a real implementation, this would request a capability report from the agent
      
      # For now, return placeholder capabilities
      %{
        computation: %{
          efficiency: 0.9,
          quality: 0.8,
          reliability: 0.7,
          latency: 0.2
        },
        planning: %{
          efficiency: 0.7,
          quality: 0.7,
          reliability: 0.8,
          latency: 0.3
        }
      }
    end
    
    defp validate_self_reported_capabilities(capability_report) do
      # In a real implementation, this would validate self-reported capabilities
      # against known benchmarks or other metrics
      
      # For now, just return the report
      {:ok, capability_report}
    end
    
    defp run_capability_tests(agent_id, test_suite) do
      # In a real implementation, this would run tests to evaluate the agent's capabilities
      
      # For now, return placeholder test results
      Enum.map(test_suite, fn {capability, test} ->
        {capability, 0.7 + :rand.uniform() * 0.3}
      end)
    end
    
    defp analyze_test_results(test_results) do
      # In a real implementation, this would analyze test results to identify capabilities
      
      # For now, convert test results to capability metrics
      Map.new(test_results, fn {capability, score} ->
        {capability, %{
          efficiency: score,
          quality: score - 0.1,
          reliability: score + 0.1,
          latency: 1.0 - score
        }}
      end)
    end
    
    defp get_interaction_history(agent_id) do
      # In a real implementation, this would retrieve the agent's interaction history
      
      # For now, return placeholder history
      [:interaction1, :interaction2, :interaction3]
    end
    
    defp combine_discovery_results(discovery_results) do
      # Combine all capabilities discovered through different methods
      Enum.reduce(discovery_results, %{}, fn result, acc ->
        Map.merge(acc, result, fn _k, v1, v2 ->
          # For each capability found in multiple results, merge the metrics
          %{
            efficiency: max(v1.efficiency, v2.efficiency),
            quality: max(v1.quality, v2.quality),
            reliability: max(v1.reliability, v2.reliability),
            latency: min(v1.latency, v2.latency)  # Lower latency is better
          }
        end)
      end)
    end
    
    defp default_test_suite do
      # In a real implementation, this would provide a default suite of tests
      # for various capabilities
      
      # For now, return placeholder tests
      [
        {:computation, :computation_test},
        {:planning, :planning_test},
        {:learning, :learning_test},
        {:communication, :communication_test}
      ]
    end
  end
  
  defmodule PerformanceMonitoring do
    @moduledoc """
    Provides mechanisms for monitoring agent performance across different capabilities.
    
    This module enables tracking of performance metrics, analysis of performance trends,
    and detection of performance changes over time.
    """
    
    @doc """
    Records a performance event for an agent's capability.
    """
    def record_performance_event(agent_id, capability_id, event_data) do
      # Extract performance metrics from event data
      metrics = extract_performance_metrics(capability_id, event_data)
      
      # Get the agent's profile
      with {:ok, profile} <- get_capability_profile(agent_id) do
        # Update the profile with new metrics
        updated_profile = CapabilityProfile.update_capability(profile, capability_id, metrics)
        
        # Store the updated profile
        store_capability_profile(agent_id, updated_profile)
        
        # Publish performance event
        publish_performance_event(agent_id, capability_id, metrics)
        
        {:ok, updated_profile}
      end
    end
    
    @doc """
    Analyzes performance trends for an agent's capability.
    """
    def analyze_performance_trend(agent_id, capability_id, window_size \\ 10) do
      with {:ok, profile} <- get_capability_profile(agent_id) do
        # Get historical performance data
        history = CapabilityProfile.capability_history(profile, capability_id, window_size)
        
        # Calculate trend
        trend = calculate_performance_trend(history)
        
        # Detect anomalies
        anomalies = detect_performance_anomalies(history)
        
        {:ok, %{trend: trend, anomalies: anomalies}}
      end
    end
    
    @doc """
    Compares performance across multiple agents for a specific capability.
    """
    def compare_agent_performance(agent_ids, capability_id) do
      # Get performance metrics for all agents
      agent_metrics = Enum.map(agent_ids, fn agent_id ->
        case get_capability_profile(agent_id) do
          {:ok, profile} ->
            performance = CapabilityProfile.capability_performance(profile, capability_id)
            {agent_id, performance}
            
          _ ->
            {agent_id, 0.0}
        end
      end)
      
      # Rank agents by performance
      ranked_agents = Enum.sort_by(agent_metrics, fn {_agent_id, performance} -> performance end, :desc)
      
      # Calculate comparative statistics
      statistics = calculate_comparative_statistics(agent_metrics)
      
      {:ok, %{ranked_agents: ranked_agents, statistics: statistics}}
    end
    
    @doc """
    Detects significant changes in agent performance.
    """
    def detect_performance_changes(agent_id, capability_id, threshold \\ 0.2) do
      with {:ok, profile} <- get_capability_profile(agent_id) do
        # Get historical performance data
        history = CapabilityProfile.capability_history(profile, capability_id, 5)
        
        # Detect significant changes
        if length(history) < 2 do
          {:ok, :insufficient_data}
        else
          [current | previous] = history
          change = current - hd(previous)
          
          if abs(change) >= threshold do
            {:ok, %{change: change, is_significant: true}}
          else
            {:ok, %{change: change, is_significant: false}}
          end
        end
      end
    end
    
    # Private functions
    
    defp extract_performance_metrics(capability_id, event_data) do
      # In a real implementation, this would extract metrics from event data
      # based on the capability type
      
      # For now, return placeholder metrics
      %{
        efficiency: Map.get(event_data, :efficiency, 0.7),
        quality: Map.get(event_data, :quality, 0.7),
        reliability: Map.get(event_data, :reliability, 0.8),
        latency: Map.get(event_data, :latency, 0.3)
      }
    end
    
    defp get_capability_profile(agent_id) do
      # In a real implementation, this would retrieve the agent's capability profile
      # from a data store
      
      # For now, create a placeholder profile
      profile = CapabilityProfile.new(agent_id, %{
        computation: %{
          efficiency: 0.8,
          quality: 0.7,
          reliability: 0.9,
          latency: 0.2
        }
      })
      
      {:ok, profile}
    end
    
    defp store_capability_profile(agent_id, profile) do
      # In a real implementation, this would store the agent's capability profile
      # in a data store
      
      # For now, do nothing
      :ok
    end
    
    defp publish_performance_event(agent_id, capability_id, metrics) do
      # In a real implementation, this would publish a performance event
      # to notify interested parties
      
      # For now, do nothing
      :ok
    end
    
    defp calculate_performance_trend(history) do
      # In a real implementation, this would calculate the trend in performance
      # over time using statistical methods
      
      # For now, calculate a simple linear trend
      if length(history) < 2 do
        0.0
      else
        # Calculate the average change between consecutive points
        history
        |> Enum.chunk_every(2, 1, :discard)
        |> Enum.map(fn [a, b] -> b - a end)
        |> Enum.sum()
        |> Kernel./(length(history) - 1)
      end
    end
    
    defp detect_performance_anomalies(history) do
      # In a real implementation, this would detect anomalies in performance
      # using statistical methods
      
      # For now, use a simple threshold-based approach
      if length(history) < 3 do
        []
      else
        # Calculate mean and standard deviation
        mean = Enum.sum(history) / length(history)
        
        variance = history
        |> Enum.map(fn x -> :math.pow(x - mean, 2) end)
        |> Enum.sum()
        |> Kernel./(length(history))
        
        std_dev = :math.sqrt(variance)
        
        # Identify points more than 2 standard deviations from the mean
        history
        |> Enum.with_index()
        |> Enum.filter(fn {value, _idx} -> abs(value - mean) > 2 * std_dev end)
        |> Enum.map(fn {_value, idx} -> idx end)
      end
    end
    
    defp calculate_comparative_statistics(agent_metrics) do
      performances = Enum.map(agent_metrics, fn {_agent_id, performance} -> performance end)
      
      # Calculate basic statistics
      count = length(performances)
      
      if count > 0 do
        sum = Enum.sum(performances)
        mean = sum / count
        min = Enum.min(performances)
        max = Enum.max(performances)
        
        # Calculate standard deviation
        variance = performances
        |> Enum.map(fn x -> :math.pow(x - mean, 2) end)
        |> Enum.sum()
        |> Kernel./(count)
        
        std_dev = :math.sqrt(variance)
        
        %{
          count: count,
          mean: mean,
          min: min,
          max: max,
          std_dev: std_dev
        }
      else
        %{
          count: 0,
          mean: 0.0,
          min: 0.0,
          max: 0.0,
          std_dev: 0.0
        }
      end
    end
  end
  
  defmodule ComparativeEvaluation do
    @moduledoc """
    Provides mechanisms for evaluating and comparing agent capabilities.
    
    This module enables relative capability assessment, competitive and cooperative
    evaluation, and identification of comparative advantages.
    """
    
    @doc """
    Evaluates relative capabilities among a group of agents.
    """
    def evaluate_relative_capabilities(agent_ids, capabilities) do
      # Create a matrix of agent-capability performance
      capability_matrix = Enum.map(agent_ids, fn agent_id ->
        {agent_id, get_agent_capabilities(agent_id, capabilities)}
      end)
      |> Enum.into(%{})
      
      # For each capability, rank the agents
      capability_rankings = Enum.map(capabilities, fn capability ->
        agents_ranked = agent_ids
        |> Enum.map(fn agent_id -> 
          {agent_id, get_capability_score(capability_matrix, agent_id, capability)}
        end)
        |> Enum.sort_by(fn {_agent_id, score} -> score end, :desc)
        
        {capability, agents_ranked}
      end)
      |> Enum.into(%{})
      
      # For each agent, identify their best capabilities
      agent_strengths = Enum.map(agent_ids, fn agent_id ->
        agent_capabilities = capability_matrix[agent_id]
        
        strengths = agent_capabilities
        |> Enum.sort_by(fn {_capability, score} -> score end, :desc)
        |> Enum.take(3)  # Take top 3 capabilities
        
        {agent_id, strengths}
      end)
      |> Enum.into(%{})
      
      {:ok, %{
        capability_rankings: capability_rankings,
        agent_strengths: agent_strengths
      }}
    end
    
    @doc """
    Evaluates agents in competitive scenarios.
    """
    def competitive_evaluation(agent_ids, scenario, options \\ []) do
      # Run the competitive scenario
      results = run_competitive_scenario(agent_ids, scenario, options)
      
      # Analyze the results
      analysis = analyze_competitive_results(results)
      
      {:ok, analysis}
    end
    
    @doc """
    Evaluates agents in cooperative scenarios.
    """
    def cooperative_evaluation(agent_ids, scenario, options \\ []) do
      # Run the cooperative scenario
      results = run_cooperative_scenario(agent_ids, scenario, options)
      
      # Analyze the results
      analysis = analyze_cooperative_results(results)
      
      {:ok, analysis}
    end
    
    @doc """
    Identifies comparative advantages and disadvantages for an agent.
    """
    def identify_comparative_advantages(agent_id, comparison_group) do
      # Get capability profiles for the agent and comparison group
      {:ok, agent_profile} = get_capability_profile(agent_id)
      
      comparison_profiles = Enum.map(comparison_group, fn comp_agent_id ->
        {:ok, profile} = get_capability_profile(comp_agent_id)
        profile
      end)
      
      # Calculate average performance for each capability in the comparison group
      comparison_averages = calculate_comparison_averages(comparison_profiles)
      
      # Identify advantages (capabilities where agent performs better than average)
      advantages = Enum.filter(agent_profile.capabilities, fn {capability, metrics} ->
        agent_performance = CapabilityProfile.calculate_overall_performance(metrics)
        comp_performance = Map.get(comparison_averages, capability, 0.0)
        
        agent_performance > comp_performance * 1.2  # 20% better than average
      end)
      |> Enum.map(fn {capability, _metrics} -> capability end)
      
      # Identify disadvantages (capabilities where agent performs worse than average)
      disadvantages = Enum.filter(agent_profile.capabilities, fn {capability, metrics} ->
        agent_performance = CapabilityProfile.calculate_overall_performance(metrics)
        comp_performance = Map.get(comparison_averages, capability, 0.0)
        
        agent_performance < comp_performance * 0.8  # 20% worse than average
      end)
      |> Enum.map(fn {capability, _metrics} -> capability end)
      
      {:ok, %{advantages: advantages, disadvantages: disadvantages}}
    end
    
    # Private functions
    
    defp get_agent_capabilities(agent_id, capabilities) do
      # In a real implementation, this would retrieve the agent's capabilities
      # from its profile
      
      # For now, generate random scores for the specified capabilities
      Enum.map(capabilities, fn capability ->
        {capability, 0.5 + :rand.uniform() * 0.5}
      end)
      |> Enum.into(%{})
    end
    
    defp get_capability_score(capability_matrix, agent_id, capability) do
      # Get the agent's score for the capability
      agent_capabilities = Map.get(capability_matrix, agent_id, %{})
      Map.get(agent_capabilities, capability, 0.0)
    end
    
    defp run_competitive_scenario(agent_ids, scenario, options) do
      # In a real implementation, this would run a competitive scenario
      # and return the results
      
      # For now, generate random results
      Enum.map(agent_ids, fn agent_id ->
        {agent_id, :rand.uniform()}
      end)
      |> Enum.sort_by(fn {_agent_id, score} -> score end, :desc)
    end
    
    defp analyze_competitive_results(results) do
      # In a real implementation, this would analyze the results of a competitive
      # scenario to identify strengths, weaknesses, etc.
      
      # For now, return the rankings
      %{
        rankings: results,
        winner: elem(hd(results), 0),
        loser: elem(List.last(results), 0)
      }
    end
    
    defp run_cooperative_scenario(agent_ids, scenario, options) do
      # In a real implementation, this would run a cooperative scenario
      # and return the results
      
      # For now, generate random results
      group_score = :rand.uniform()
      
      agent_contributions = Enum.map(agent_ids, fn agent_id ->
        contribution = :rand.uniform()
        {agent_id, contribution}
      end)
      
      %{
        group_score: group_score,
        agent_contributions: agent_contributions
      }
    end
    
    defp analyze_cooperative_results(results) do
      # In a real implementation, this would analyze the results of a cooperative
      # scenario to identify synergies, complementary capabilities, etc.
      
      # For now, identify the top contributors
      sorted_contributions = Enum.sort_by(
        results.agent_contributions,
        fn {_agent_id, contribution} -> contribution end,
        :desc
      )
      
      top_contributors = Enum.take(sorted_contributions, 2)
      
      %{
        group_score: results.group_score,
        top_contributors: top_contributors
      }
    end
    
    defp get_capability_profile(agent_id) do
      # In a real implementation, this would retrieve the agent's capability profile
      # from a data store
      
      # For now, create a placeholder profile with random capabilities
      capabilities = [:computation, :planning, :learning, :communication]
      |> Enum.map(fn capability ->
        {capability, %{
          efficiency: 0.5 + :rand.uniform() * 0.5,
          quality: 0.5 + :rand.uniform() * 0.5,
          reliability: 0.5 + :rand.uniform() * 0.5,
          latency: :rand.uniform() * 0.5
        }}
      end)
      |> Enum.into(%{})
      
      profile = CapabilityProfile.new(agent_id, capabilities)
      
      {:ok, profile}
    end
    
    defp calculate_comparison_averages(profiles) do
      # Collect all capabilities
      all_capabilities = profiles
      |> Enum.flat_map(fn profile -> Map.keys(profile.capabilities) end)
      |> Enum.uniq()
      
      # Calculate average for each capability
      Enum.map(all_capabilities, fn capability ->
        # Get performance for this capability from all profiles that have it
        performances = profiles
        |> Enum.filter(fn profile -> Map.has_key?(profile.capabilities, capability) end)
        |> Enum.map(fn profile ->
          metrics = profile.capabilities[capability]
          CapabilityProfile.calculate_overall_performance(metrics)
        end)
        
        # Calculate average
        avg = if Enum.empty?(performances), do: 0.0, else: Enum.sum(performances) / length(performances)
        
        {capability, avg}
      end)
      |> Enum.into(%{})
    end
  end
end