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Meta-Schema Evolution: Objects That Modify Themselves

notebooks/meta_schema_evolution.livemd

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Arthur M. Collé
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Meta-Schema Evolution: Objects That Modify Themselves

Mix.install([
  {:kino, "~> 0.12.0"},
  {:jason, "~> 1.4"}
])

Understanding Meta-Schemas

A meta-schema is a schema that describes how schemas can be modified. It’s like DNA that can rewrite itself based on environmental pressures and performance feedback.

In AAOS, objects don’t just follow fixed programming - they can:

  • Analyze their own behavior patterns
  • Identify inefficiencies in their current schema
  • Propose modifications to improve performance
  • Test changes safely before committing
  • Roll back if changes prove harmful

Let’s see this in action!

Step 1: Basic Schema Structure

defmodule Schema do
  defstruct [
    :id,
    :version,
    :attributes,
    :behaviors,
    :constraints,
    :evolution_history,
    :performance_metrics
  ]
  
  def new(id, attributes \\ %{}, behaviors \\ %{}) do
    %__MODULE__{
      id: id,
      version: "1.0.0",
      attributes: attributes,
      behaviors: behaviors,
      constraints: %{
        max_evolution_rate: 0.2,
        core_attributes_immutable: true,
        rollback_generations: 5
      },
      evolution_history: [],
      performance_metrics: %{
        effectiveness: 0.5,
        efficiency: 0.5,
        adaptability: 0.5
      }
    }
  end
  
  def evolve_schema(schema, performance_data, environmental_pressures) do
    # Analyze current performance vs. desired performance
    performance_gap = analyze_performance_gap(schema.performance_metrics, performance_data)
    
    # Generate potential modifications
    proposed_changes = generate_schema_modifications(schema, performance_gap, environmental_pressures)
    
    # Evaluate safety and compatibility of changes
    safe_changes = filter_safe_changes(proposed_changes, schema.constraints)
    
    if length(safe_changes) > 0 do
      # Apply the most promising change
      best_change = Enum.max_by(safe_changes, & &1.expected_improvement)
      apply_schema_change(schema, best_change)
    else
      {:no_evolution, "No safe beneficial changes identified"}
    end
  end
  
  defp analyze_performance_gap(current_metrics, target_performance) do
    %{
      effectiveness_gap: target_performance.effectiveness - current_metrics.effectiveness,
      efficiency_gap: target_performance.efficiency - current_metrics.efficiency,
      adaptability_gap: target_performance.adaptability - current_metrics.adaptability
    }
  end
  
  defp generate_schema_modifications(schema, gaps, pressures) do
    modifications = []
    
    # Suggest attribute modifications based on performance gaps
    modifications = if gaps.effectiveness_gap > 0.1 do
      [%{
        type: :attribute_enhancement,
        target: :decision_making,
        change: %{sophistication_level: :increase},
        expected_improvement: gaps.effectiveness_gap * 0.8,
        risk_level: :low
      } | modifications]
    else
      modifications
    end
    
    # Suggest behavior modifications based on efficiency gaps
    modifications = if gaps.efficiency_gap > 0.1 do
      [%{
        type: :behavior_optimization,
        target: :message_processing,
        change: %{batch_size: :increase, priority_filtering: :enable},
        expected_improvement: gaps.efficiency_gap * 0.7,
        risk_level: :medium
      } | modifications]
    else
      modifications
    end
    
    # Suggest new capabilities based on environmental pressures
    modifications = if pressures.cooperation_demand > 0.7 do
      [%{
        type: :capability_addition,
        target: :social_coordination,
        change: %{coalition_formation: :add, negotiation_protocols: :add},
        expected_improvement: 0.3,
        risk_level: :high
      } | modifications]
    else
      modifications
    end
    
    modifications
  end
  
  defp filter_safe_changes(proposed_changes, constraints) do
    Enum.filter(proposed_changes, fn change ->
      # Check if change violates evolution rate limits
      evolution_rate_ok = change.expected_improvement <= constraints.max_evolution_rate
      
      # Check if change affects immutable core attributes
      core_attributes_ok = if constraints.core_attributes_immutable do
        change.type != :core_attribute_modification
      else
        true
      end
      
      # Risk assessment
      risk_acceptable = change.risk_level in [:low, :medium]
      
      evolution_rate_ok and core_attributes_ok and risk_acceptable
    end)
  end
  
  defp apply_schema_change(schema, change) do
    # Create new version
    new_version = increment_version(schema.version)
    
    # Apply the change based on its type
    {updated_attributes, updated_behaviors} = case change.type do
      :attribute_enhancement ->
        enhanced_attrs = Map.update(schema.attributes, change.target, %{}, fn attr ->
          Map.merge(attr, change.change)
        end)
        {enhanced_attrs, schema.behaviors}
      
      :behavior_optimization ->
        optimized_behaviors = Map.update(schema.behaviors, change.target, %{}, fn behavior ->
          Map.merge(behavior, change.change)
        end)
        {schema.attributes, optimized_behaviors}
      
      :capability_addition ->
        new_behaviors = Map.merge(schema.behaviors, change.change)
        {schema.attributes, new_behaviors}
    end
    
    # Record evolution in history
    evolution_record = %{
      from_version: schema.version,
      to_version: new_version,
      change_applied: change,
      timestamp: DateTime.utc_now(),
      reason: "Performance optimization"
    }
    
    updated_history = [evolution_record | Enum.take(schema.evolution_history, 4)]
    
    evolved_schema = %{schema |
      version: new_version,
      attributes: updated_attributes,
      behaviors: updated_behaviors,
      evolution_history: updated_history
    }
    
    IO.puts("🧬 Schema #{schema.id} evolved from #{schema.version} → #{new_version}")
    IO.puts("   Change: #{change.type} on #{change.target}")
    IO.puts("   Expected improvement: #{Float.round(change.expected_improvement * 100, 1)}%")
    
    {:evolved, evolved_schema}
  end
  
  defp increment_version(version) do
    [major, minor, patch] = String.split(version, ".") |> Enum.map(&amp;String.to_integer/1)
    "#{major}.#{minor}.#{patch + 1}"
  end
  
  def display_schema_status(schema) do
    IO.puts("\n📋 Schema #{schema.id} (v#{schema.version}):")
    IO.puts("   Attributes: #{map_size(schema.attributes)}")
    IO.puts("   Behaviors: #{map_size(schema.behaviors)}")
    IO.puts("   Performance:")
    IO.puts("     Effectiveness: #{Float.round(schema.performance_metrics.effectiveness * 100, 1)}%")
    IO.puts("     Efficiency: #{Float.round(schema.performance_metrics.efficiency * 100, 1)}%")
    IO.puts("     Adaptability: #{Float.round(schema.performance_metrics.adaptability * 100, 1)}%")
    IO.puts("   Evolution History: #{length(schema.evolution_history)} changes")
    
    if length(schema.evolution_history) > 0 do
      latest = hd(schema.evolution_history)
      IO.puts("   Latest Evolution: #{latest.change_applied.type} (#{latest.from_version} → #{latest.to_version})")
    end
  end
end

# Create a basic agent schema
agent_schema = Schema.new(:agent_alpha, 
  %{
    decision_making: %{sophistication_level: :basic, learning_rate: 0.1},
    memory: %{capacity: 1000, retention_policy: :fifo},
    communication: %{protocols: [:basic_messaging], bandwidth: :standard}
  },
  %{
    message_processing: %{batch_size: 1, priority_filtering: false},
    goal_pursuit: %{planning_depth: 3, adaptation_frequency: :daily},
    social_interaction: %{trust_model: :simple, reputation_tracking: false}
  }
)

Schema.display_schema_status(agent_schema)

Step 2: Performance-Driven Evolution

# Simulate performance data indicating room for improvement
current_performance = %{
  effectiveness: 0.4,  # Below target
  efficiency: 0.3,     # Needs improvement 
  adaptability: 0.6    # Acceptable
}

target_performance = %{
  effectiveness: 0.8,
  efficiency: 0.7,
  adaptability: 0.7
}

environmental_pressures = %{
  cooperation_demand: 0.8,  # High need for cooperation
  resource_scarcity: 0.6,   # Moderate resource pressure
  change_frequency: 0.7     # Frequent environmental changes
}

IO.puts("🎯 Performance Analysis:")
IO.puts("Current vs Target Effectiveness: #{current_performance.effectiveness} → #{target_performance.effectiveness}")
IO.puts("Current vs Target Efficiency: #{current_performance.efficiency} → #{target_performance.efficiency}")
IO.puts("Environmental Cooperation Demand: #{environmental_pressures.cooperation_demand}")

# Attempt schema evolution
case Schema.evolve_schema(agent_schema, target_performance, environmental_pressures) do
  {:evolved, evolved_schema} ->
    Schema.display_schema_status(evolved_schema)
    agent_schema = evolved_schema
  
  {:no_evolution, reason} ->
    IO.puts("❌ Evolution failed: #{reason}")
end

Step 3: Multi-Generation Evolution

defmodule EvolutionSimulator do
  def simulate_evolution_cycles(schema, num_cycles \\ 5) do
    IO.puts("\n🔄 Simulating #{num_cycles} evolution cycles...")
    
    Enum.reduce(1..num_cycles, schema, fn cycle, current_schema ->
      IO.puts("\n--- Cycle #{cycle} ---")
      
      # Simulate varying performance and environmental conditions
      performance = %{
        effectiveness: 0.3 + :rand.uniform() * 0.4,  # 0.3 to 0.7
        efficiency: 0.2 + :rand.uniform() * 0.5,      # 0.2 to 0.7
        adaptability: 0.4 + :rand.uniform() * 0.4     # 0.4 to 0.8
      }
      
      target = %{
        effectiveness: 0.8,
        efficiency: 0.8,
        adaptability: 0.8
      }
      
      pressures = %{
        cooperation_demand: :rand.uniform(),
        resource_scarcity: :rand.uniform(),
        change_frequency: :rand.uniform()
      }
      
      # Update schema performance metrics based on simulation
      updated_metrics = %{
        effectiveness: min(1.0, current_schema.performance_metrics.effectiveness + (:rand.uniform() - 0.5) * 0.1),
        efficiency: min(1.0, current_schema.performance_metrics.efficiency + (:rand.uniform() - 0.5) * 0.1),
        adaptability: min(1.0, current_schema.performance_metrics.adaptability + (:rand.uniform() - 0.5) * 0.1)
      }
      
      schema_with_metrics = %{current_schema | performance_metrics: updated_metrics}
      
      case Schema.evolve_schema(schema_with_metrics, target, pressures) do
        {:evolved, evolved} -> evolved
        {:no_evolution, _} -> schema_with_metrics
      end
    end)
  end
  
  def analyze_evolution_trajectory(schema) do
    IO.puts("\n📈 Evolution Trajectory Analysis:")
    IO.puts("Schema #{schema.id} - Final Version: #{schema.version}")
    
    if length(schema.evolution_history) > 0 do
      IO.puts("\nEvolution History:")
      
      schema.evolution_history
      |> Enum.reverse()  # Show chronological order
      |> Enum.with_index(1)
      |> Enum.each(fn {evolution, index} ->
        IO.puts("  #{index}. #{evolution.from_version} → #{evolution.to_version}")
        IO.puts("     Change: #{evolution.change_applied.type}")
        IO.puts("     Target: #{evolution.change_applied.target}")
        IO.puts("     Expected improvement: #{Float.round(evolution.change_applied.expected_improvement * 100, 1)}%")
      end)
      
      # Calculate evolution velocity
      total_changes = length(schema.evolution_history)
      if total_changes > 1 do
        first_evolution = List.last(schema.evolution_history)
        latest_evolution = hd(schema.evolution_history)
        time_span = DateTime.diff(latest_evolution.timestamp, first_evolution.timestamp, :hour)
        evolution_velocity = if time_span > 0, do: total_changes / time_span, else: 0
        
        IO.puts("\nEvolution Velocity: #{Float.round(evolution_velocity, 3)} changes/hour")
      end
    else
      IO.puts("No evolution history recorded.")
    end
  end
end

# Run evolution simulation
final_schema = EvolutionSimulator.simulate_evolution_cycles(agent_schema, 5)
EvolutionSimulator.analyze_evolution_trajectory(final_schema)

Step 4: Schema Compatibility and Inheritance

defmodule SchemaInheritance do
  def create_specialized_schema(parent_schema, specialization) do
    specialized_attributes = case specialization do
      :researcher ->
        Map.merge(parent_schema.attributes, %{
          knowledge_base: %{domains: [], update_frequency: :continuous},
          hypothesis_generation: %{creativity_level: :high, validation_rigor: :strict},
          data_analysis: %{statistical_methods: [:regression, :clustering], visualization: true}
        })
      
      :coordinator ->
        Map.merge(parent_schema.attributes, %{
          resource_management: %{optimization_algorithm: :genetic, load_balancing: true},
          team_coordination: %{communication_style: :directive, delegation_strategy: :capability_based},
          conflict_resolution: %{mediation_approach: :collaborative, escalation_rules: []}
        })
      
      :creative ->
        Map.merge(parent_schema.attributes, %{
          creative_processes: %{inspiration_sources: [:random, :analogical], idea_generation: :divergent},
          aesthetic_evaluation: %{criteria: [:novelty, :beauty, :usefulness], weighting: :adaptive},
          iteration_strategy: %{refinement_cycles: 5, feedback_integration: :selective}
        })
    end
    
    specialized_behaviors = case specialization do
      :researcher ->
        Map.merge(parent_schema.behaviors, %{
          research_methodology: %{approach: :systematic, documentation: :comprehensive},
          peer_review: %{collaboration_level: :high, critique_acceptance: :open},
          knowledge_sharing: %{publication_frequency: :regular, open_access: true}
        })
      
      :coordinator ->
        Map.merge(parent_schema.behaviors, %{
          task_allocation: %{fairness_priority: :high, efficiency_optimization: true},
          performance_monitoring: %{metrics_tracking: :real_time, feedback_delivery: :constructive},
          strategic_planning: %{horizon: :long_term, contingency_preparation: true}
        })
      
      :creative ->
        Map.merge(parent_schema.behaviors, %{
          brainstorming: %{session_structure: :loose, idea_capture: :comprehensive},
          prototype_development: %{iteration_speed: :rapid, user_testing: :frequent},
          artistic_expression: %{medium_flexibility: :high, style_evolution: :continuous}
        })
    end
    
    specialized_schema = %{parent_schema |
      id: :"#{specialization}_#{parent_schema.id}",
      version: "1.0.0-#{specialization}",
      attributes: specialized_attributes,
      behaviors: specialized_behaviors,
      evolution_history: []
    }
    
    IO.puts("🎭 Created specialized schema: #{specialized_schema.id}")
    IO.puts("   Inherited from: #{parent_schema.id} (v#{parent_schema.version})")
    IO.puts("   Specialization: #{specialization}")
    IO.puts("   New attributes: #{map_size(specialized_attributes) - map_size(parent_schema.attributes)}")
    IO.puts("   New behaviors: #{map_size(specialized_behaviors) - map_size(parent_schema.behaviors)}")
    
    specialized_schema
  end
  
  def calculate_schema_compatibility(schema1, schema2) do
    # Calculate attribute compatibility
    common_attributes = Map.keys(schema1.attributes) 
                       |> Enum.filter(&amp;Map.has_key?(schema2.attributes, &amp;1))
    
    attribute_compatibility = if length(common_attributes) > 0 do
      compatible_attrs = Enum.count(common_attributes, fn attr ->
        attr1 = schema1.attributes[attr]
        attr2 = schema2.attributes[attr]
        # Simplified compatibility check
        map_size(Map.take(attr1, Map.keys(attr2))) / map_size(attr1) > 0.5
      end)
      
      compatible_attrs / length(common_attributes)
    else
      0.0
    end
    
    # Calculate behavior compatibility
    common_behaviors = Map.keys(schema1.behaviors)
                      |> Enum.filter(&amp;Map.has_key?(schema2.behaviors, &amp;1))
    
    behavior_compatibility = if length(common_behaviors) > 0 do
      compatible_behaviors = Enum.count(common_behaviors, fn behavior ->
        beh1 = schema1.behaviors[behavior]
        beh2 = schema2.behaviors[behavior]
        # Check if behaviors have similar structure
        map_size(Map.take(beh1, Map.keys(beh2))) / map_size(beh1) > 0.5
      end)
      
      compatible_behaviors / length(common_behaviors)
    else
      0.0
    end
    
    # Overall compatibility score
    overall_compatibility = (attribute_compatibility + behavior_compatibility) / 2
    
    %{
      overall: overall_compatibility,
      attributes: attribute_compatibility,
      behaviors: behavior_compatibility,
      common_attributes: length(common_attributes),
      common_behaviors: length(common_behaviors)
    }
  end
  
  def attempt_schema_merge(schema1, schema2, compatibility_threshold \\ 0.6) do
    compatibility = calculate_schema_compatibility(schema1, schema2)
    
    if compatibility.overall >= compatibility_threshold do
      # Merge compatible schemas
      merged_attributes = Map.merge(schema1.attributes, schema2.attributes)
      merged_behaviors = Map.merge(schema1.behaviors, schema2.behaviors)
      
      merged_schema = %{schema1 |
        id: :"merged_#{schema1.id}_#{schema2.id}",
        version: "1.0.0-merged",
        attributes: merged_attributes,
        behaviors: merged_behaviors,
        evolution_history: []
      }
      
      IO.puts("🤝 Successfully merged schemas!")
      IO.puts("   Compatibility score: #{Float.round(compatibility.overall * 100, 1)}%")
      IO.puts("   Merged schema: #{merged_schema.id}")
      
      {:success, merged_schema}
    else
      IO.puts("❌ Schema merge failed - insufficient compatibility")
      IO.puts("   Compatibility score: #{Float.round(compatibility.overall * 100, 1)}%")
      IO.puts("   Required threshold: #{Float.round(compatibility_threshold * 100, 1)}%")
      
      {:failed, compatibility}
    end
  end
end

# Create specialized schemas from our evolved schema
researcher_schema = SchemaInheritance.create_specialized_schema(final_schema, :researcher)
coordinator_schema = SchemaInheritance.create_specialized_schema(final_schema, :coordinator)
creative_schema = SchemaInheritance.create_specialized_schema(final_schema, :creative)

# Check compatibility between specialized schemas
IO.puts("\n🔍 Schema Compatibility Analysis:")

researcher_coordinator_compat = SchemaInheritance.calculate_schema_compatibility(researcher_schema, coordinator_schema)
IO.puts("Researcher ↔ Coordinator: #{Float.round(researcher_coordinator_compat.overall * 100, 1)}%")

researcher_creative_compat = SchemaInheritance.calculate_schema_compatibility(researcher_schema, creative_schema)
IO.puts("Researcher ↔ Creative: #{Float.round(researcher_creative_compat.overall * 100, 1)}%")

coordinator_creative_compat = SchemaInheritance.calculate_schema_compatibility(coordinator_schema, creative_schema)
IO.puts("Coordinator ↔ Creative: #{Float.round(coordinator_creative_compat.overall * 100, 1)}%")

# Attempt to merge the most compatible schemas
IO.puts("\n🔄 Attempting Schema Merge:")
case SchemaInheritance.attempt_schema_merge(researcher_schema, coordinator_schema, 0.5) do
  {:success, merged_schema} ->
    Schema.display_schema_status(merged_schema)
  
  {:failed, compatibility_info} ->
    IO.puts("Merge details: #{inspect(compatibility_info)}")
end

Step 5: Real-Time Schema Adaptation

defmodule AdaptiveSchemaObject do
  defstruct [:id, :schema, :performance_history, :adaptation_triggers, :current_state]
  
  def new(id, initial_schema) do
    %__MODULE__{
      id: id,
      schema: initial_schema,
      performance_history: [],
      adaptation_triggers: %{
        performance_drop_threshold: 0.2,
        stagnation_period: 5,
        environmental_change_sensitivity: 0.3
      },
      current_state: %{
        energy: 100,
        task_completion_rate: 0.5,
        social_satisfaction: 0.6,
        learning_progress: 0.4
      }
    }
  end
  
  def execute_task(object, task_type, difficulty) do
    # Simulate task execution based on current schema capabilities
    success_probability = calculate_success_probability(object.schema, task_type, difficulty)
    success = :rand.uniform() < success_probability
    
    # Update performance history
    performance_record = %{
      task_type: task_type,
      difficulty: difficulty,
      success: success,
      timestamp: DateTime.utc_now(),
      schema_version: object.schema.version
    }
    
    updated_history = [performance_record | Enum.take(object.performance_history, 19)]
    
    # Update current state based on task outcome
    state_changes = if success do
      %{
        task_completion_rate: min(1.0, object.current_state.task_completion_rate + 0.05),
        learning_progress: min(1.0, object.current_state.learning_progress + 0.02)
      }
    else
      %{
        task_completion_rate: max(0.0, object.current_state.task_completion_rate - 0.03),
        energy: max(0, object.current_state.energy - 5)
      }
    end
    
    updated_state = Map.merge(object.current_state, state_changes)
    
    IO.puts("#{if success, do: "✅", else: "❌"} #{object.id} executed #{task_type} (difficulty: #{difficulty}) - #{if success, do: "SUCCESS", else: "FAILED"}")
    
    updated_object = %{object |
      performance_history: updated_history,
      current_state: updated_state
    }
    
    # Check if adaptation is needed
    check_adaptation_triggers(updated_object)
  end
  
  defp calculate_success_probability(schema, task_type, difficulty) do
    # Base probability from schema capabilities
    base_prob = case task_type do
      :research when Map.has_key?(schema.attributes, :knowledge_base) -> 0.8
      :coordination when Map.has_key?(schema.attributes, :resource_management) -> 0.8
      :creative when Map.has_key?(schema.attributes, :creative_processes) -> 0.8
      :social when Map.has_key?(schema.behaviors, :social_interaction) -> 0.7
      _ -> 0.5  # Generic capability
    end
    
    # Adjust for difficulty
    difficulty_modifier = case difficulty do
      :easy -> 1.2
      :medium -> 1.0
      :hard -> 0.7
      :extreme -> 0.4
    end
    
    # Apply performance metrics influence
    performance_modifier = (schema.performance_metrics.effectiveness + schema.performance_metrics.efficiency) / 2
    
    min(1.0, max(0.1, base_prob * difficulty_modifier * performance_modifier))
  end
  
  defp check_adaptation_triggers(object) do
    recent_performance = object.performance_history
                        |> Enum.take(5)
                        |> Enum.map(&amp; &amp;1.success)
    
    success_rate = if length(recent_performance) > 0 do
      Enum.count(recent_performance, &amp; &amp;1) / length(recent_performance)
    else
      0.5
    end
    
    # Check for performance drop
    performance_drop = object.current_state.task_completion_rate < object.adaptation_triggers.performance_drop_threshold
    
    # Check for stagnation
    version_changes = object.performance_history
                     |> Enum.take(object.adaptation_triggers.stagnation_period)
                     |> Enum.map(&amp; &amp;1.schema_version)
                     |> Enum.uniq()
    
    stagnation = length(version_changes) <= 1 and length(object.performance_history) >= object.adaptation_triggers.stagnation_period
    
    cond do
      performance_drop ->
        IO.puts("🚨 Performance drop detected - triggering adaptation")
        adapt_schema(object, :performance_recovery)
      
      stagnation and success_rate < 0.7 ->
        IO.puts("📈 Stagnation detected - triggering optimization")
        adapt_schema(object, :optimization)
      
      success_rate > 0.9 ->
        IO.puts("🎯 High performance - considering capability expansion")
        adapt_schema(object, :capability_expansion)
      
      true ->
        object
    end
  end
  
  defp adapt_schema(object, adaptation_reason) do
    target_performance = case adaptation_reason do
      :performance_recovery -> %{
        effectiveness: object.schema.performance_metrics.effectiveness + 0.3,
        efficiency: object.schema.performance_metrics.efficiency + 0.2,
        adaptability: object.schema.performance_metrics.adaptability + 0.1
      }
      
      :optimization -> %{
        effectiveness: min(1.0, object.schema.performance_metrics.effectiveness + 0.1),
        efficiency: min(1.0, object.schema.performance_metrics.efficiency + 0.2),
        adaptability: min(1.0, object.schema.performance_metrics.adaptability + 0.1)
      }
      
      :capability_expansion -> %{
        effectiveness: min(1.0, object.schema.performance_metrics.effectiveness + 0.1),
        efficiency: object.schema.performance_metrics.efficiency,
        adaptability: min(1.0, object.schema.performance_metrics.adaptability + 0.3)
      }
    end
    
    environmental_pressures = %{
      cooperation_demand: :rand.uniform(),
      resource_scarcity: :rand.uniform(),
      change_frequency: :rand.uniform()
    }
    
    case Schema.evolve_schema(object.schema, target_performance, environmental_pressures) do
      {:evolved, evolved_schema} ->
        IO.puts("🧬 Schema adapted successfully for #{adaptation_reason}")
        %{object | schema: evolved_schema}
      
      {:no_evolution, reason} ->
        IO.puts("⚠️  Schema adaptation failed: #{reason}")
        object
    end
  end
  
  def display_adaptive_status(object) do
    recent_success_rate = if length(object.performance_history) > 0 do
      recent_successes = object.performance_history
                        |> Enum.take(5)
                        |> Enum.count(&amp; &amp;1.success)
      recent_successes / min(5, length(object.performance_history)) * 100
    else
      0
    end
    
    IO.puts("\n🤖 Adaptive Object #{object.id}:")
    IO.puts("   Schema Version: #{object.schema.version}")
    IO.puts("   Recent Success Rate: #{Float.round(recent_success_rate, 1)}%")
    IO.puts("   Task Completion Rate: #{Float.round(object.current_state.task_completion_rate * 100, 1)}%")
    IO.puts("   Energy: #{object.current_state.energy}/100")
    IO.puts("   Learning Progress: #{Float.round(object.current_state.learning_progress * 100, 1)}%")
    IO.puts("   Schema Evolutions: #{length(object.schema.evolution_history)}")
    
    if length(object.performance_history) > 0 do
      latest_task = hd(object.performance_history)
      IO.puts("   Latest Task: #{latest_task.task_type} (#{latest_task.difficulty}) - #{if latest_task.success, do: "✅", else: "❌"}")
    end
  end
end

# Create an adaptive object with the researcher schema
adaptive_researcher = AdaptiveSchemaObject.new(:adaptive_alpha, researcher_schema)
AdaptiveSchemaObject.display_adaptive_status(adaptive_researcher)

# Simulate a series of tasks that will trigger adaptations
tasks = [
  {:research, :easy},
  {:research, :medium}, 
  {:coordination, :hard},    # Should struggle with this
  {:coordination, :hard},    # Continued struggle
  {:research, :extreme},     # Very difficult
  {:creative, :medium},
  {:research, :easy},        # Should improve after adaptation
  {:research, :medium},
  {:social, :easy},
  {:research, :hard}
]

IO.puts("\n🎮 Starting Task Simulation...")

final_adaptive_object = Enum.reduce(tasks, adaptive_researcher, fn {task_type, difficulty}, acc ->
  updated_object = AdaptiveSchemaObject.execute_task(acc, task_type, difficulty)
  Process.sleep(100)  # Small delay for readability
  updated_object
end)

IO.puts("\n=== Final Adaptive Status ===")
AdaptiveSchemaObject.display_adaptive_status(final_adaptive_object)

Key Insights from Meta-Schema Evolution

This demonstration shows how objects can:

  1. Self-Analyze: Detect performance gaps and identify improvement opportunities
  2. Self-Modify: Generate and apply safe schema modifications
  3. Self-Optimize: Continuously adapt based on environmental feedback
  4. Inherit & Specialize: Create specialized variants while maintaining compatibility
  5. Collaborate: Merge compatible schemas to create hybrid capabilities

The meta-schema system ensures that:

  • Evolution is safe (constraints prevent harmful changes)
  • Changes are beneficial (performance-driven modifications)
  • History is preserved (rollback capability)
  • Compatibility is maintained (inheritance and merging rules)

Next up: See how these self-modifying objects learn through OORL!

IO.puts("🎉 Meta-Schema Evolution Demo Complete!")
IO.puts("The objects are literally rewriting their own code based on experience!")
IO.puts("Next: Check out the OORL livebook to see how they learn and form coalitions!")

Other notebooks:

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