Return code: 0
Duration: 91.5s

=== STDOUT ===
🚀 Running coder
📦 Dynamic packages: clingo
🤖 Creating agent with model: anthropic/claude-sonnet-4.5
🤖 Using model: anthropic/claude-sonnet-4.5
  python_exec: import clingo
  python_exec: defining function solve_prisoners_dilemma()
  python_exec: defining function format_output()
  save_code: {'code': 'import clingo\nimpor...
  python_exec: executing file 045_prisoners_dilemma_hard_task_code.py
  report_issue: {'text': "All is fine - no iss...


                              Solution Complete! ✓                              

I've successfully solved the Prisoners' Dilemma tournament problem using Answer 
Set Programming with clingo.                                                    

Key Results:                                                                    

 • Best Strategy: DEFECT with expected score of 48                              
 • All Scores: DEFECT (48), TFT (35), COOP (30)                                 

How the ASP Model Works:                                                        

 1 Strategy Encoding: Each strategy (COOP, DEFECT, TFT) is modeled with rules   
   defining their moves based on round and opponent behavior                    
 2 Opponent Modeling: Three opponent types with their specific behaviors (type_A
   always defects, type_B uses TFT, type_C always cooperates)                   
 3 Score Calculation: Uses ASP aggregates to sum round scores into game scores, 
   then weights by opponent counts                                              
 4 Optimization: The #maximize directive finds the strategy with highest        
   expected total score                                                         

The solution correctly identifies that DEFECT is optimal because it exploits    
cooperative opponents (type_B and type_C) while still performing reasonably     
against other defectors (type_A).                                               

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

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

💬 Token Consumption:
  Input tokens:        88,919
  Output tokens:       5,143
  Total tokens:        94,062

⏱️  Execution time: 1m 30.6s
==================================================


=== STDERR ===
