object

defmodule Object.InteractionPatterns do
  @moduledoc """
  Defines and manages interaction patterns between Objects for self-organization.
  
  This module implements various interaction patterns that enable emergent behaviors:
  1. Peer-to-peer negotiation and consensus building
  2. Hierarchical coordination and delegation
  3. Swarm intelligence and collective decision making
  4. Market-based resource allocation
  5. Gossip protocols for information dissemination
  6. Adaptive coalition formation
  
  Each pattern is implemented as a composable interaction protocol that Objects
  can dynamically adopt based on their context and objectives.
  """
  
  alias Object.LLMIntegration
  
  
  @doc """
  Initiates a specific interaction pattern between objects.
  """
  def initiate_pattern(pattern, initiator_object, target_objects, context \\ %{}) do
    case pattern do
      :peer_negotiation ->
        peer_negotiation(initiator_object, target_objects, context)
      
      :hierarchical_delegation ->
        hierarchical_delegation(initiator_object, target_objects, context)
      
      :swarm_consensus ->
        swarm_consensus(initiator_object, target_objects, context)
      
      :market_auction ->
        market_auction(initiator_object, target_objects, context)
      
      :gossip_propagation ->
        gossip_propagation(initiator_object, target_objects, context)
      
      :coalition_formation ->
        coalition_formation(initiator_object, target_objects, context)
      
      :collaborative_learning ->
        collaborative_learning(initiator_object, target_objects, context)
      
      :adaptive_routing ->
        adaptive_routing(initiator_object, target_objects, context)
      
      _ ->
        {:error, {:unknown_pattern, pattern}}
    end
  end
  
  @doc """
  Peer negotiation pattern for bilateral or multilateral agreements.
  """
  def peer_negotiation(initiator, targets, context) do
    negotiation_session = %{
      id: generate_session_id(),
      participants: [initiator | targets],
      context: context,
      rounds: [],
      status: :active,
      started_at: DateTime.utc_now()
    }
    
    # Use LLM to generate initial proposal
    initial_proposal = generate_negotiation_proposal(initiator, context)
    
    # Conduct negotiation rounds
    final_result = conduct_negotiation_rounds(negotiation_session, initial_proposal)
    
    {:ok, final_result}
  end
  
  @doc """
  Hierarchical delegation pattern for top-down task distribution.
  """
  def hierarchical_delegation(coordinator, subordinates, context) do
    # Analyze task complexity and requirements
    task_analysis = analyze_delegation_requirements(coordinator, context)
    
    # Use LLM to create optimal delegation strategy
    delegation_strategy = create_delegation_strategy(coordinator, subordinates, task_analysis)
    
    # Execute delegation with monitoring
    delegation_result = execute_hierarchical_delegation(
      coordinator,
      subordinates,
      delegation_strategy
    )
    
    {:ok, delegation_result}
  end
  
  @doc """
  Swarm consensus pattern for collective decision making.
  """
  def swarm_consensus(initiator, swarm_members, context) do
    consensus_process = %{
      id: generate_session_id(),
      coordinator: initiator,
      participants: swarm_members,
      decision_context: context,
      voting_rounds: [],
      consensus_threshold: Map.get(context, :threshold, 0.7),
      status: :gathering_input
    }
    
    # Gather individual perspectives
    individual_inputs = gather_swarm_inputs(swarm_members, context)
    
    # Use collective intelligence to reach consensus
    consensus_result = reach_swarm_consensus(consensus_process, individual_inputs)
    
    {:ok, consensus_result}
  end
  
  @doc """
  Market auction pattern for resource allocation through bidding.
  """
  def market_auction(auctioneer, bidders, context) do
    auction = %{
      id: generate_session_id(),
      auctioneer: auctioneer,
      bidders: bidders,
      resource: Map.get(context, :resource),
      auction_type: Map.get(context, :type, :sealed_bid),
      deadline: Map.get(context, :deadline, DateTime.add(DateTime.utc_now(), 300, :second)),
      bids: [],
      status: :open
    }
    
    # Conduct auction process
    auction_result = conduct_market_auction(auction)
    
    {:ok, auction_result}
  end
  
  @doc """
  Gossip propagation pattern for distributed information sharing.
  """
  def gossip_propagation(initiator, network_nodes, context) do
    gossip_message = %{
      id: generate_session_id(),
      originator: initiator.id,
      content: Map.get(context, :message),
      metadata: Map.get(context, :metadata, %{}),
      ttl: Map.get(context, :ttl, 10),
      propagation_factor: Map.get(context, :propagation_factor, 3),
      timestamp: DateTime.utc_now()
    }
    
    # Start gossip propagation
    propagation_result = propagate_gossip_message(gossip_message, network_nodes)
    
    {:ok, propagation_result}
  end
  
  @doc """
  Coalition formation pattern for dynamic team assembly.
  """
  def coalition_formation(initiator, potential_partners, context) do
    coalition_request = %{
      id: generate_session_id(),
      initiator: initiator,
      objective: Map.get(context, :objective),
      required_capabilities: Map.get(context, :capabilities, []),
      duration: Map.get(context, :duration, :indefinite),
      benefits: Map.get(context, :benefits, %{}),
      constraints: Map.get(context, :constraints, [])
    }
    
    # Use LLM to evaluate potential coalitions
    coalition_analysis = analyze_coalition_potential(initiator, potential_partners, coalition_request)
    
    # Form optimal coalition
    formation_result = form_optimal_coalition(coalition_request, coalition_analysis)
    
    {:ok, formation_result}
  end
  
  @doc """
  Collaborative learning pattern for knowledge sharing and joint improvement.
  """
  def collaborative_learning(learner, teachers_peers, context) do
    learning_session = %{
      id: generate_session_id(),
      primary_learner: learner,
      knowledge_sources: teachers_peers,
      learning_objective: Map.get(context, :objective),
      knowledge_domain: Map.get(context, :domain),
      learning_strategy: Map.get(context, :strategy, :peer_to_peer),
      session_duration: Map.get(context, :duration, 3600) # 1 hour default
    }
    
    # Coordinate collaborative learning
    learning_result = coordinate_collaborative_learning(learning_session)
    
    {:ok, learning_result}
  end
  
  @doc """
  Adaptive routing pattern for dynamic message and task routing.
  """
  def adaptive_routing(router, destination_candidates, context) do
    routing_request = %{
      id: generate_session_id(),
      router: router,
      payload: Map.get(context, :payload),
      destination_candidates: destination_candidates,
      routing_criteria: Map.get(context, :criteria, [:latency, :capacity, :reliability]),
      fallback_strategy: Map.get(context, :fallback, :random)
    }
    
    # Use intelligent routing decision
    routing_result = make_adaptive_routing_decision(routing_request)
    
    {:ok, routing_result}
  end
  
  # Private implementation functions
  
  defp generate_session_id do
    :crypto.strong_rand_bytes(8) |> Base.encode16() |> String.downcase()
  end
  
  defp generate_negotiation_proposal(initiator, context) do
    case LLMIntegration.reason_about_goal(
      initiator,
      "Generate an initial negotiation proposal",
      %{
        context: context,
        initiator_capabilities: initiator.methods,
        initiator_resources: Map.get(initiator.state, :resources, %{})
      }
    ) do
      {:ok, reasoning_result, _} ->
        %{
          proposer: initiator.id,
          terms: extract_proposal_terms(reasoning_result),
          rationale: reasoning_result.reasoning_chain,
          timestamp: DateTime.utc_now()
        }
      
      _ ->
        %{
          proposer: initiator.id,
          terms: %{offer: "default_offer"},
          rationale: "fallback proposal",
          timestamp: DateTime.utc_now()
        }
    end
  end
  
  defp conduct_negotiation_rounds(session, initial_proposal) do
    # Simulate negotiation rounds with participants
    rounds = [
      %{
        round: 1,
        proposals: [initial_proposal],
        responses: simulate_negotiation_responses(session.participants, initial_proposal),
        timestamp: DateTime.utc_now()
      }
    ]
    
    # Determine outcome
    %{
      session_id: session.id,
      outcome: :agreement_reached,
      final_terms: merge_negotiation_terms(rounds),
      rounds: rounds,
      completed_at: DateTime.utc_now()
    }
  end
  
  defp analyze_delegation_requirements(coordinator, context) do
    case LLMIntegration.reason_about_goal(
      coordinator,
      "Analyze task delegation requirements",
      context
    ) do
      {:ok, reasoning_result, _} ->
        %{
          task_complexity: extract_complexity_score(reasoning_result),
          required_skills: extract_required_skills(reasoning_result),
          time_constraints: extract_time_constraints(reasoning_result),
          resource_needs: extract_resource_needs(reasoning_result)
        }
      
      _ ->
        %{task_complexity: :medium, required_skills: [], time_constraints: nil, resource_needs: %{}}
    end
  end
  
  defp create_delegation_strategy(coordinator, subordinates, _task_analysis) do
    # Use LLM to match tasks with optimal subordinates
    case LLMIntegration.collaborative_reasoning(
      [coordinator | subordinates],
      "Create optimal task delegation strategy",
      :consensus
    ) do
      {:ok, collaboration_result} ->
        %{
          assignments: parse_task_assignments(collaboration_result),
          coordination_plan: collaboration_result.coordination_plan,
          monitoring_strategy: extract_monitoring_strategy(collaboration_result)
        }
      
      _ ->
        %{assignments: [], coordination_plan: "fallback plan", monitoring_strategy: :periodic_check}
    end
  end
  
  defp execute_hierarchical_delegation(coordinator, _subordinates, strategy) do
    # Send delegation messages
    delegation_messages = for {subordinate, assignment} <- strategy.assignments do
      message = %{
        id: generate_session_id(),
        from: coordinator.id,
        to: subordinate.id,
        type: :task_delegation,
        content: assignment,
        deadline: assignment.deadline,
        priority: assignment.priority
      }
      
      Object.send_message(coordinator, subordinate.id, :task_delegation, assignment)
      message
    end
    
    %{
      delegation_completed: true,
      messages_sent: length(delegation_messages),
      monitoring_started: true,
      strategy_used: strategy
    }
  end
  
  defp gather_swarm_inputs(swarm_members, context) do
    for member <- swarm_members do
      case LLMIntegration.generate_response(member, %{
        content: "What is your perspective on: #{inspect(context)}",
        sender: "swarm_coordinator"
      }) do
        {:ok, response, _} ->
          %{member_id: member.id, input: response.content, confidence: response.confidence}
        
        _ ->
          %{member_id: member.id, input: "no input", confidence: 0.0}
      end
    end
  end
  
  defp reach_swarm_consensus(process, individual_inputs) do
    # Aggregate inputs and find consensus
    consensus_score = calculate_consensus_score(individual_inputs)
    
    if consensus_score >= process.consensus_threshold do
      %{
        consensus_reached: true,
        consensus_score: consensus_score,
        agreed_decision: synthesize_consensus_decision(individual_inputs),
        participants: length(individual_inputs)
      }
    else
      %{
        consensus_reached: false,
        consensus_score: consensus_score,
        additional_rounds_needed: true,
        participants: length(individual_inputs)
      }
    end
  end
  
  defp conduct_market_auction(auction) do
    # Collect bids from participants
    bids = collect_auction_bids(auction)
    
    # Determine winner based on auction type
    winner = determine_auction_winner(auction.auction_type, bids)
    
    %{
      auction_id: auction.id,
      winner: winner,
      winning_bid: find_winning_bid(bids, winner),
      total_bids: length(bids),
      completed_at: DateTime.utc_now()
    }
  end
  
  defp propagate_gossip_message(message, network_nodes) do
    # Simulate gossip propagation through network
    propagation_hops = simulate_gossip_hops(message, network_nodes)
    
    %{
      message_id: message.id,
      total_nodes_reached: length(propagation_hops),
      propagation_time: calculate_propagation_time(propagation_hops),
      coverage_percentage: calculate_coverage(propagation_hops, network_nodes)
    }
  end
  
  defp analyze_coalition_potential(initiator, partners, _request) do
    case LLMIntegration.collaborative_reasoning(
      [initiator | partners],
      "Analyze potential for coalition formation",
      :negotiation
    ) do
      {:ok, analysis} ->
        %{
          viability_score: extract_viability_score(analysis),
          optimal_members: extract_optimal_members(analysis, partners),
          expected_benefits: analysis.synthesis,
          coordination_requirements: analysis.coordination_plan
        }
      
      _ ->
        %{viability_score: 0.5, optimal_members: partners, expected_benefits: "unknown", coordination_requirements: "minimal"}
    end
  end
  
  defp form_optimal_coalition(request, analysis) do
    if analysis.viability_score > 0.6 do
      %{
        coalition_formed: true,
        members: analysis.optimal_members,
        coordinator: request.initiator,
        charter: create_coalition_charter(request, analysis),
        formation_timestamp: DateTime.utc_now()
      }
    else
      %{
        coalition_formed: false,
        reason: "insufficient viability",
        viability_score: analysis.viability_score
      }
    end
  end
  
  defp coordinate_collaborative_learning(session) do
    # Set up learning coordination
    learning_phases = [
      :knowledge_sharing,
      :collaborative_practice,
      :peer_feedback,
      :consolidation
    ]
    
    phase_results = for phase <- learning_phases do
      execute_learning_phase(session, phase)
    end
    
    %{
      session_id: session.id,
      learning_outcomes: synthesize_learning_outcomes(phase_results),
      knowledge_gained: measure_knowledge_improvement(session),
      participants: length(session.knowledge_sources) + 1
    }
  end
  
  defp make_adaptive_routing_decision(request) do
    # Evaluate routing options
    routing_scores = for candidate <- request.destination_candidates do
      score = calculate_routing_score(candidate, request.routing_criteria)
      {candidate, score}
    end
    
    # Select best option
    {optimal_destination, best_score} = Enum.max_by(routing_scores, &elem(&1, 1))
    
    %{
      selected_destination: optimal_destination,
      routing_score: best_score,
      decision_rationale: "optimized for #{inspect(request.routing_criteria)}",
      timestamp: DateTime.utc_now()
    }
  end
  
  # Simplified helper functions for demonstration
  defp extract_proposal_terms(_reasoning), do: %{offer: "collaborative partnership"}
  defp simulate_negotiation_responses(_participants, _proposal), do: [%{response: "acceptable", from: "participant_1"}]
  defp merge_negotiation_terms(_rounds), do: %{final_agreement: "mutual cooperation"}
  defp extract_complexity_score(_reasoning), do: :medium
  defp extract_required_skills(_reasoning), do: [:communication, :analysis]
  defp extract_time_constraints(_reasoning), do: %{deadline: DateTime.add(DateTime.utc_now(), 3600, :second)}
  defp extract_resource_needs(_reasoning), do: %{cpu: 0.5, memory: 1024}
  defp parse_task_assignments(_collaboration), do: []
  defp extract_monitoring_strategy(_collaboration), do: :periodic_status
  defp calculate_consensus_score(inputs), do: length(inputs) / max(1, length(inputs))
  defp synthesize_consensus_decision(_inputs), do: "consensus decision reached"
  defp collect_auction_bids(auction), do: Enum.map(auction.bidders, fn bidder -> %{bidder: bidder.id, amount: :rand.uniform(100)} end)
  defp determine_auction_winner(_type, bids), do: Enum.max_by(bids, & &1.amount).bidder
  defp find_winning_bid(bids, winner), do: Enum.find(bids, & &1.bidder == winner)
  defp simulate_gossip_hops(_message, nodes), do: Enum.take(nodes, 3)
  defp calculate_propagation_time(_hops), do: 150 # milliseconds
  defp calculate_coverage(hops, total_nodes), do: length(hops) / max(1, length(total_nodes))
  defp extract_viability_score(_analysis), do: 0.8
  defp extract_optimal_members(_analysis, partners), do: Enum.take(partners, 3)
  defp create_coalition_charter(_request, _analysis), do: %{purpose: "collaborative goal achievement"}
  defp execute_learning_phase(_session, phase), do: %{phase: phase, outcome: "successful"}
  defp synthesize_learning_outcomes(results), do: %{phases_completed: length(results)}
  defp measure_knowledge_improvement(_session), do: %{improvement_score: 0.7}
  defp calculate_routing_score(_candidate, _criteria), do: :rand.uniform()
end