Return code: 0
Duration: 167.4s

=== 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("=== Task 1: Problem Analysis ===")
  python_exec: print("=== Task 2: ASP Model Design ===")
  python_exec: defining function generate_asp_facts()
  python_exec: import clingo
  python_exec: defining function on_model()
  python_exec: print("Solution found with makespan:", solution_da...
  python_exec: defining function format_solution_json()
  python_exec: defining function verify_solution()
  python_exec: defining function generate_asp_facts()
  save_code: {'code': 'import clingo\nimpor...
  report_issue: {'text': 'All is fine - no iss...

Perfect! I have successfully completed the ASP recipe scheduling task. Here's   
what was accomplished:                                                          


                                    Summary                                     

Task: Plan the cooking of 4 recipes with shared resources, optimizing for       
minimal total time.                                                             

Solution Approach:                                                              

 1 Modeled the problem using Answer Set Programming with the clingo Python API  
 2 Encoded all constraints:                                                     
    • Intra-recipe step precedences (e.g., prep → bake → rest for roast chicken)
    • Inter-recipe dependency (simmer_stock must finish before cook_risotto     
      starts)                                                                   
    • Resource capacity limits (prep_area: 2 concurrent tasks, oven & stove: 1  
      each)                                                                     
    • Oven preheating requirement (must complete before baking)                 
 3 Optimized using #minimize to find the minimal makespan                       
 4 Achieved optimal solution: total_time = 75 (matching expected value)         

Key Features:                                                                   

 • All 9 recipe steps + 1 special task (preheat_oven) scheduled correctly       
 • Parallel execution where possible (e.g., prep_chicken and chop_veg_soup both 
   use prep_area simultaneously)                                                
 • All constraints verified and satisfied                                       
 • Clean JSON output with complete schedule details                             

The solution demonstrates efficient resource utilization and proper temporal    
reasoning in ASP!                                                               

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

🔧 Tool Usage:
  python_exec            9 calls
  report_issue           1 calls
  save_code              1 calls

💬 Token Consumption:
  Input tokens:        167,378
  Output tokens:       9,598
  Total tokens:        176,976

⏱️  Execution time: 2m 44.5s
==================================================


=== STDERR ===
<block>:77:13-14: info: tuple ignored:
  #inf@0

