Return code: 0
Duration: 99.5s

=== 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 analysis complete. Proceeding with imp...
  python_exec: defining function solve_ricochet_robots()
  python_exec: solution = {
  save_code: {'code': 'import clingo\nimpor...
  report_issue: {'text': 'All is fine - no iss...


                              Solution Complete! ✓                              

I've successfully solved the Ricochet Robots puzzle using Answer Set Programming
with the clingo Python API. Here's what was accomplished:                       

                                 Key Features:                                  

 1 Temporal Planning Model: Implemented proper state tracking with frame axioms 
   for robot positions over time                                                
 2 Single-Step Movement: Each move is exactly one cell in a cardinal direction  
   (Manhattan distance = 1)                                                     
 3 Wall Constraints: Vertical walls properly block movement between cells       
 4 Collision Avoidance: Ensures no two robots occupy the same cell at any time  
 5 Sequential Execution: Only one move happens per timestep                     
 6 Optimization: Minimizes total number of moves using #minimize                

                                Solution Found:                                 

 • 7 moves (optimal, matching expected minimum)                                 
 • Robot A successfully reaches target position (2, 3)                          
 • Strategy: Robot B clears the path by moving through the bridge opening,      
   allowing Robot A to follow and reach the target                              

The solution has been saved to 051_ricochet_robots_hard_task_code.py and        
produces the required JSON output format.                                       

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

🔧 Tool Usage:
  python_exec            3 calls
  report_issue           1 calls
  save_code              1 calls

💬 Token Consumption:
  Input tokens:        73,535
  Output tokens:       5,397
  Total tokens:        78,932

⏱️  Execution time: 1m 38.5s
==================================================


=== STDERR ===
