Return code: 0
Duration: 582.3s

=== 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: import clingo
  python_exec: defining function on_model()
  python_exec: import time
  python_exec: print(f"Solution found: {solution_found}")
  python_exec: import time
  python_exec: import clingo
  python_exec: defining function on_model_v2()
  python_exec: defining function extract_model()
  python_exec: defining function solve_blocks_world()
  python_exec: if 'solution_data' in dir() and solution_data:
  python_exec: defining function extract_final()
  python_exec: defining function extract_opt()
  python_exec: defining function extract_nh()
  python_exec: defining function extract_nh()
  python_exec: defining function extract_final2()
  python_exec: initial_state = {
  python_exec: print("=== Verifying Solution ===")
  save_code: {'code': 'import clingo\nimpor...
  report_issue: {'text': 'Task completed succe...

Perfect! I have successfully completed the blocks world planning task using     
Answer Set Programming. Here's a summary of what was accomplished:              


                                Solution Summary                                

Task: Transform 12 weighted blocks from an initial configuration (3 stacks of   
height 4) to a goal configuration (3 different stacks of height 4) while        
respecting weight and structural constraints.                                   

Result: Found an optimal solution with 36 moves (well within the 50 move limit).

Key Features of the ASP Solution:                                               

 1 State Modeling: Used fluent predicates loc(Block, Position, Time) to track   
   block positions over time                                                    
 2 Action Generation: Choice rules generate possible moves (block-to-block or   
   block-to-table)                                                              
 3 Constraints Enforced:                                                        
    • Only clear blocks can be moved                                            
    • Target positions must be clear                                            
    • Weight constraint (heavier blocks cannot go on lighter blocks)            
    • Table limit (max 6 blocks on table)                                       
    • No redundant moves                                                        
 4 Optimization: Minimized the number of moves using #minimize                  
 5 Verification: The solution was verified by simulating the plan and confirming
   the goal state is correctly achieved                                         

The final code is saved and produces a JSON output with the complete 36-step    
plan that successfully solves the puzzle.                                       

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

🔧 Tool Usage:
  python_exec           18 calls
  report_issue           1 calls
  save_code              1 calls

💬 Token Consumption:
  Input tokens:        585,053
  Output tokens:       16,907
  Total tokens:        601,960

⏱️  Execution time: 9m 41.3s
==================================================


=== STDERR ===
2025-10-11 18:41:49,784 - coder.kernel - WARNING - Existing kernel is dead, will restart
<block>:28:62-63: error: syntax error, unexpected ), expecting : or "," or . or ;

