Return code: 0
Duration: 107.4s

=== STDOUT ===
🚀 Running coder
📦 Dynamic packages: clingo
🤖 Creating agent with model: anthropic/claude-sonnet-4.5
🤖 Using model: anthropic/claude-sonnet-4.5
  python_exec: print("=== Problem Analysis ===")
  python_exec: print("=== ASP Model Design ===")
  python_exec: defining function solve_car_assembly()
  python_exec: defining function format_solution()
  python_exec: print("=== Verification ===")
  save_code: {'code': 'import clingo\nimpor...
  python_exec: executing file 037_car_sequencing_hard_task_code.py
  report_issue: {'text': 'All is fine - no iss...


                                    Summary                                     

I've successfully completed the car assembly line scheduling task using Answer  
Set Programming (ASP) with the clingo Python API.                               

                               Solution Overview                                

The solution schedules 12 cars across 12 positions on an assembly line while    
satisfying all complex constraints:                                             

 1 Assignment & Type Counts: Each position gets exactly one car, with correct   
   counts (A=3, B=3, C=4, D=2)                                                  
 2 Hierarchical Options: Option 5 (panoramic sunroof) correctly implies option 1
   (standard sunroof) for capacity constraints                                  
 3 Positional Ban: No EV-equipped cars (type B with option 4) at positions 1 or 
   12                                                                           
 4 Equipment Cooldown: Custom paint cars (type C with option 2) maintain at     
   least 3-slot gaps                                                            
 5 Standard Capacity: Maximum 2 effective sunroof cars in any                   
   4-consecutive-position window                                                
 6 Conditional Capacity: Sport package cars (type B with option 3) follow       
   context-dependent limits based on preceding EV presence                      

                            Key ASP Techniques Used                             

 • Choice rules for generating car assignments                                  
 • Derived predicates for tracking options and hierarchical relationships       
 • Integrity constraints for enforcing hard requirements                        
 • Conditional logic for context-dependent capacity constraints                 
 • Aggregate counting for window-based capacity limits                          

The solution has been verified to satisfy all constraints and outputs the       
required JSON format.                                                           

==================================================
📊 Execution Statistics
==================================================

🔧 Tool Usage:
  python_exec            6 calls
  report_issue           1 calls
  save_code              1 calls

💬 Token Consumption:
  Input tokens:        117,054
  Output tokens:       5,544
  Total tokens:        122,598

⏱️  Execution time: 1m 46.4s
==================================================


=== STDERR ===
2025-10-11 08:43:56,132 - coder.kernel - WARNING - Existing kernel is dead, will restart
