Return code: 0
Duration: 112.6s

=== STDOUT ===
🚀 Running coder
📦 Dynamic packages: clingo
🤖 Creating agent with model: anthropic/claude-sonnet-4.5
🤖 Using model: anthropic/claude-sonnet-4.5
  python_exec: defining function generate_asp_program()
  python_exec: defining function solve_meeting_scheduling()
  python_exec: defining function verify_solution()
  python_exec: print(json.dumps(result, indent=2))
  save_code: {'code': 'import clingo\nimpor...
  report_issue: {'text': 'All is fine - no iss...


                                    Summary                                     

I've successfully completed the meeting scheduling task using Answer Set        
Programming (ASP) with the clingo Python API. Here's what was accomplished:     

                               Solution Approach                                

 1 Modeled the problem with clear ASP predicates:                               
    • Rooms, equipment, meetings, people, days, and time slots as facts         
    • Equipment requirements and meeting attendees as relationships             
    • Choice rule for scheduling assignments                                    
 2 Applied efficient ASP patterns:                                              
    • Constrain at source: Only generated assignments to rooms that have the    
      required equipment (avoiding generate-and-filter anti-pattern)            
    • Used helper predicate missing_equipment/2 to identify invalid room-meeting
      pairs                                                                     
    • Enforced constraints for person availability and room occupancy           
 3 Key constraints implemented:                                                 
    • Each meeting assigned to exactly one (day, slot, room) combination        
    • No person can attend two meetings at the same time                        
    • No room can be double-booked                                              
    • Equipment requirements must be satisfied                                  
 4 Verification: The solution was validated against all constraints and         
   confirmed to be correct.                                                     

                                     Result                                     

The solver found a valid schedule for all 20 meetings that satisfies all        
constraints. The solution is output in the required JSON format with feasible:  
true and a complete schedule array.                                             

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

🔧 Tool Usage:
  python_exec            4 calls
  report_issue           1 calls
  save_code              1 calls

💬 Token Consumption:
  Input tokens:        160,658
  Output tokens:       7,069
  Total tokens:        167,727

⏱️  Execution time: 1m 51.5s
==================================================


=== STDERR ===
