--- Input for ANALYZER at 20250925_065309 ---
================================ Human Message =================================


ANALYZER OBJECTIVE:

Start with: "After Running The New foo_player.py Player, Here is my analysis and findings:"

If the game failed to compile/run (no game_results JSON or score==0):
- ERROR SUMMARY:
  - First error line (verbatim), exception type, file, exact line number, and the exact code line (from game_output.txt).
- LIKELY CAUSE (1–2 bullets): short hypothesis based on the error/log text (e.g., unknown ActionType, bad import, attribute missing).
- QUICK FIX FOCUS: 1–2 bullets pointing to the specific function/line in foo_player.py (or adapters.py) to inspect.

If the game ran (game_results JSON present):
1) PERFORMANCE SUMMARY:
   - Outcome (Win/Loss), our VP vs opponent VP, VP diff.
   - Key counts: cities, settlements, roads, dev cards (if available), total turns.
2) VERDICT:
   - Good if Win OR VP diff ≥ +0.5
   - Borderline if −0.5 < VP diff < +0.5
   - Poor if Loss OR VP diff ≤ −0.5
3) IF BORDERLINE/POOR — LIKELY REASONS:
   - Briefly scan foo_player.py and list 2–4 concrete issues with short citations (line numbers/snippets), prioritizing:
     - Missing 1-ply value lookahead (no `copy_game` + `make_value_fn` usage).
     - No chance handling (dice/dev/robber), or robber/knight policy absent.
     - Placement helpers stubbed/always False (roads/settlements).
     - No end-turn policy or repeated random selection.
     - Illegal/unknown actions (e.g., trying to play `VICTORY_POINT`).
   - Pull 2–4 corroborating log lines from game_output.txt (e.g., "Unrecognized action type", "Defaulting to Random Action", stack traces).
4) NEXT STEP (one line):
   - Clear route like: "Send to Coder to add 1-ply value lookahead", or "Send to Strategizer to specify robber/placement policy", etc.

End with: "Let me know if you need anything else".

================================== Ai Message ==================================

ANALYZER
---
After Running The New foo_player.py Player, Here is my analysis and findings:

### PERFORMANCE SUMMARY
- **Outcome**: Loss (0 wins out of 30 games).
- **VP Comparison**: FooPlayer (2.0 VP) vs. Opponent (10.0 VP), VP diff: **-8.0**.
- **Key Counts**:
  - Settlements: 2.0 (FooPlayer) vs. 2.93 (Opponent).
  - Cities: 0.0 (FooPlayer) vs. 2.53 (Opponent).
  - Roads: 0.0 (FooPlayer) vs. 1.0 (Opponent).
  - Dev Cards: 0.0 (FooPlayer) vs. 0.03 (Opponent).
  - Total Turns: 64.4.

### VERDICT
- **Poor**: The FooPlayer lost all games with a significant VP deficit.

### LIKELY REASONS
1. **No Action Evaluation**: The player always selects the first action in `playable_actions` without any evaluation or strategy (Line 20, `foo_player.py`).
   - **Log Evidence**: Repeated lines of `"Choosing First Action on Default"` in `game_output.txt`.
2. **Lack of Lookahead**: No use of `copy_game` or `make_value_fn` to simulate future states or evaluate long-term rewards.
3. **No Chance Handling**: No logic to handle dice rolls, robber placement, or development card usage.
4. **No Progression**: The player never builds cities, roads, or armies, indicating a complete lack of strategic development.

### STRATEGIC FLAW
The player's logic is entirely reactive and naive, always choosing the first available action without evaluating its impact or considering long-term rewards.

### NEXT STEP
Send to Coder to implement a basic value function and 1-ply lookahead for action evaluation.

---
Let me know if you need anything else.
END ANALYZER
================================ Human Message =================================


ANALYZER OBJECTIVE:

Start with: "After Running The New foo_player.py Player, Here is my analysis and findings:"

If the game failed to compile/run (no game_results JSON or score==0):
- ERROR SUMMARY:
  - First error line (verbatim), exception type, file, exact line number, and the exact code line (from game_output.txt).
- LIKELY CAUSE (1–2 bullets): short hypothesis based on the error/log text (e.g., unknown ActionType, bad import, attribute missing).
- QUICK FIX FOCUS: 1–2 bullets pointing to the specific function/line in foo_player.py (or adapters.py) to inspect.

If the game ran (game_results JSON present):
1) PERFORMANCE SUMMARY:
   - Outcome (Win/Loss), our VP vs opponent VP, VP diff.
   - Key counts: cities, settlements, roads, dev cards (if available), total turns.
2) VERDICT:
   - Good if Win OR VP diff ≥ +0.5
   - Borderline if −0.5 < VP diff < +0.5
   - Poor if Loss OR VP diff ≤ −0.5
3) IF BORDERLINE/POOR — LIKELY REASONS:
   - Briefly scan foo_player.py and list 2–4 concrete issues with short citations (line numbers/snippets), prioritizing:
     - Missing 1-ply value lookahead (no `copy_game` + `make_value_fn` usage).
     - No chance handling (dice/dev/robber), or robber/knight policy absent.
     - Placement helpers stubbed/always False (roads/settlements).
     - No end-turn policy or repeated random selection.
     - Illegal/unknown actions (e.g., trying to play `VICTORY_POINT`).
   - Pull 2–4 corroborating log lines from game_output.txt (e.g., "Unrecognized action type", "Defaulting to Random Action", stack traces).
4) NEXT STEP (one line):
   - Clear route like: "Send to Coder to add 1-ply value lookahead", or "Send to Strategizer to specify robber/placement policy", etc.

End with: "Let me know if you need anything else".

================================== Ai Message ==================================

ANALYZER
---
After Running The New foo_player.py Player, Here is my analysis and findings:

### PERFORMANCE SUMMARY
- **Outcome**: Loss (0 wins out of 30 games).
- **VP Comparison**: FooPlayer (2.43 VP) vs. Opponent (10.0 VP), VP diff: **-7.57**.
- **Key Counts**:
  - Settlements: 1.93 (FooPlayer) vs. 2.73 (Opponent).
  - Cities: 0.07 (FooPlayer) vs. 2.6 (Opponent).
  - Roads: 0.0 (FooPlayer) vs. 1.0 (Opponent).
  - Dev Cards: 0.23 (FooPlayer) vs. 0.07 (Opponent).
  - Total Turns: 66.87.

### VERDICT
- **Poor**: The FooPlayer lost all games with a significant VP deficit.

---

### LIKELY REASONS
1. **Zero-Score Evaluations**:
   - The `_evaluate_state` function consistently returns a score of **0.0** for all actions, as evidenced by the repeated log lines:
     ```
     FooPlayer.decide: action #0 -> score 0.0
     FooPlayer.decide: action #1 -> score 0.0
     ...
     FooPlayer.decide: selected action -> Action(...) with score 0.0
     ```
   - This indicates that the evaluator is failing to extract meaningful state information (e.g., `victory_points`, `settlements`, `cities`, `roads`) from the game state.
   - **Code Issue**: The `_evaluate_state` function (Lines 100–200) uses overly defensive attribute access, which likely fails to extract any values from the actual game state structure.

2. **No Strategic Prioritization**:
   - The player evaluates all actions as equally valuable (score = 0.0), leading to random tie-breaking.
   - **Log Evidence**: Actions like `MOVE_ROBBER`, `BUY_DEVELOPMENT_CARD`, and `BUILD_ROAD` are selected arbitrarily, with no regard for their strategic impact.

3. **Lack of Progression**:
   - The player rarely builds cities, roads, or armies, indicating a failure to prioritize long-term development.
   - **Log Evidence**: The opponent consistently builds cities (2.6 avg) and roads (1.0 avg), while FooPlayer averages **0.0 roads** and **0.07 cities**.

4. **No Chance Handling**:
   - The player does not account for dice rolls, robber placement, or development card usage, which are critical for resource accumulation and disruption.

---

### STRATEGIC FLAW
The player's `_evaluate_state` function fails to extract meaningful state information, causing all actions to be scored as 0.0. This results in random action selection with no strategic direction or progression.

---

### NEXT STEP
Send to Coder to **fix the `_evaluate_state` function** to correctly extract `victory_points`, `settlements`, `cities`, and `roads` from the game state, and prioritize actions that maximize these metrics.

---
Let me know if you need anything else.
END ANALYZER
================================ Human Message =================================


ANALYZER OBJECTIVE:

Start with: "After Running The New foo_player.py Player, Here is my analysis and findings:"

If the game failed to compile/run (no game_results JSON or score==0):
- ERROR SUMMARY:
  - First error line (verbatim), exception type, file, exact line number, and the exact code line (from game_output.txt).
- LIKELY CAUSE (1–2 bullets): short hypothesis based on the error/log text (e.g., unknown ActionType, bad import, attribute missing).
- QUICK FIX FOCUS: 1–2 bullets pointing to the specific function/line in foo_player.py (or adapters.py) to inspect.

If the game ran (game_results JSON present):
1) PERFORMANCE SUMMARY:
   - Outcome (Win/Loss), our VP vs opponent VP, VP diff.
   - Key counts: cities, settlements, roads, dev cards (if available), total turns.
2) VERDICT:
   - Good if Win OR VP diff ≥ +0.5
   - Borderline if −0.5 < VP diff < +0.5
   - Poor if Loss OR VP diff ≤ −0.5
3) IF BORDERLINE/POOR — LIKELY REASONS:
   - Briefly scan foo_player.py and list 2–4 concrete issues with short citations (line numbers/snippets), prioritizing:
     - Missing 1-ply value lookahead (no `copy_game` + `make_value_fn` usage).
     - No chance handling (dice/dev/robber), or robber/knight policy absent.
     - Placement helpers stubbed/always False (roads/settlements).
     - No end-turn policy or repeated random selection.
     - Illegal/unknown actions (e.g., trying to play `VICTORY_POINT`).
   - Pull 2–4 corroborating log lines from game_output.txt (e.g., "Unrecognized action type", "Defaulting to Random Action", stack traces).
4) NEXT STEP (one line):
   - Clear route like: "Send to Coder to add 1-ply value lookahead", or "Send to Strategizer to specify robber/placement policy", etc.

End with: "Let me know if you need anything else".

================================== Ai Message ==================================

ANALYZER
---
After Running The New foo_player.py Player, Here is my analysis and findings:

### ERROR SUMMARY
- **First Error Line**:
  ```
  ModuleNotFoundError: No module named 'catanatron.models.player'
  ```
- **Exception Type**: `ModuleNotFoundError`
- **File**: `/catanatron_core/catanatron/models/board.py`
- **Line Number**: 9
- **Exact Code Line**:
  ```python
  from catanatron.models.player import Color
  ```

### LIKELY CAUSE
- The error is due to a **missing or misplaced module** in the Catanatron core library.
- The `adapters.py` file imports `Color` from `catanatron.models.player`, but the game engine cannot locate this module during runtime.

### QUICK FIX FOCUS
- **Inspect `adapters.py`**: Verify the import path for `Color` and ensure it aligns with the actual module structure.
- **Check Catanatron Core**: Confirm that `catanatron.models.player` exists and is accessible in the environment. If not, update the import path or reinstall the package.

---
Let me know if you need anything else.
END ANALYZER
================================ Human Message =================================


ANALYZER OBJECTIVE:

Start with: "After Running The New foo_player.py Player, Here is my analysis and findings:"

If the game failed to compile/run (no game_results JSON or score==0):
- ERROR SUMMARY:
  - First error line (verbatim), exception type, file, exact line number, and the exact code line (from game_output.txt).
- LIKELY CAUSE (1–2 bullets): short hypothesis based on the error/log text (e.g., unknown ActionType, bad import, attribute missing).
- QUICK FIX FOCUS: 1–2 bullets pointing to the specific function/line in foo_player.py (or adapters.py) to inspect.

If the game ran (game_results JSON present):
1) PERFORMANCE SUMMARY:
   - Outcome (Win/Loss), our VP vs opponent VP, VP diff.
   - Key counts: cities, settlements, roads, dev cards (if available), total turns.
2) VERDICT:
   - Good if Win OR VP diff ≥ +0.5
   - Borderline if −0.5 < VP diff < +0.5
   - Poor if Loss OR VP diff ≤ −0.5
3) IF BORDERLINE/POOR — LIKELY REASONS:
   - Briefly scan foo_player.py and list 2–4 concrete issues with short citations (line numbers/snippets), prioritizing:
     - Missing 1-ply value lookahead (no `copy_game` + `make_value_fn` usage).
     - No chance handling (dice/dev/robber), or robber/knight policy absent.
     - Placement helpers stubbed/always False (roads/settlements).
     - No end-turn policy or repeated random selection.
     - Illegal/unknown actions (e.g., trying to play `VICTORY_POINT`).
   - Pull 2–4 corroborating log lines from game_output.txt (e.g., "Unrecognized action type", "Defaulting to Random Action", stack traces).
4) NEXT STEP (one line):
   - Clear route like: "Send to Coder to add 1-ply value lookahead", or "Send to Strategizer to specify robber/placement policy", etc.

End with: "Let me know if you need anything else".

================================== Ai Message ==================================

ANALYZER
---
After Running The New foo_player.py Player, Here is my analysis and findings:

### ERROR SUMMARY
- **First Error Line**:
  ```
  ModuleNotFoundError: No module named 'catanatron.models.player'
  ```
- **Exception Type**: `ModuleNotFoundError`
- **File**: `/Users/dakotabarnes/Develop/CollectiveComputingLabs/strategy-game-agents/catanatron/catanatron_core/catanatron/models/board.py`
- **Line Number**: 9
- **Exact Code Line**:
  ```python
  from catanatron.models.player import Color
  ```

### LIKELY CAUSE
- The error is due to a **missing or misplaced module** in the Catanatron core library.
- The `catanatron.models.player` module, which is required by `board.py`, is either:
  - Not installed in the environment.
  - Located in a different directory than expected.

### QUICK FIX FOCUS
- **Inspect the Catanatron Core Installation**:
  - Verify that the `catanatron.models.player` module exists in the installed package.
  - If missing, reinstall or update the Catanatron package to ensure all dependencies are correctly installed.
- **Check Import Paths in `adapters.py`**:
  - Ensure that the import paths in `adapters.py` align with the actual module structure of the installed Catanatron package.

---
Let me know if you need anything else.
END ANALYZER
================================ Human Message =================================


ANALYZER OBJECTIVE:

Start with: "After Running The New foo_player.py Player, Here is my analysis and findings:"

If the game failed to compile/run (no game_results JSON or score==0):
- ERROR SUMMARY:
  - First error line (verbatim), exception type, file, exact line number, and the exact code line (from game_output.txt).
- LIKELY CAUSE (1–2 bullets): short hypothesis based on the error/log text (e.g., unknown ActionType, bad import, attribute missing).
- QUICK FIX FOCUS: 1–2 bullets pointing to the specific function/line in foo_player.py (or adapters.py) to inspect.

If the game ran (game_results JSON present):
1) PERFORMANCE SUMMARY:
   - Outcome (Win/Loss), our VP vs opponent VP, VP diff.
   - Key counts: cities, settlements, roads, dev cards (if available), total turns.
2) VERDICT:
   - Good if Win OR VP diff ≥ +0.5
   - Borderline if −0.5 < VP diff < +0.5
   - Poor if Loss OR VP diff ≤ −0.5
3) IF BORDERLINE/POOR — LIKELY REASONS:
   - Briefly scan foo_player.py and list 2–4 concrete issues with short citations (line numbers/snippets), prioritizing:
     - Missing 1-ply value lookahead (no `copy_game` + `make_value_fn` usage).
     - No chance handling (dice/dev/robber), or robber/knight policy absent.
     - Placement helpers stubbed/always False (roads/settlements).
     - No end-turn policy or repeated random selection.
     - Illegal/unknown actions (e.g., trying to play `VICTORY_POINT`).
   - Pull 2–4 corroborating log lines from game_output.txt (e.g., "Unrecognized action type", "Defaulting to Random Action", stack traces).
4) NEXT STEP (one line):
   - Clear route like: "Send to Coder to add 1-ply value lookahead", or "Send to Strategizer to specify robber/placement policy", etc.

End with: "Let me know if you need anything else".

================================== Ai Message ==================================

ANALYZER
---
After Running The New foo_player.py Player, Here is my analysis and findings:

### ERROR SUMMARY
- **First Error Line**:
  ```
  ModuleNotFoundError: No module named 'catanatron.models.player'
  ```
- **Exception Type**: `ModuleNotFoundError`
- **File**: `/Users/dakotabarnes/Develop/CollectiveComputingLabs/strategy-game-agents/catanatron/catanatron_core/catanatron/models/board.py`
- **Line Number**: 9
- **Exact Code Line**:
  ```python
  from catanatron.models.player import Color
  ```

### LIKELY CAUSE
- The error is due to a **missing or misplaced module** in the Catanatron core library.
- The `catanatron.models.player` module is required by `board.py` but is either:
  - Not installed in the environment.
  - Located in a different directory than expected, causing the import to fail.

### QUICK FIX FOCUS
- **Verify Catanatron Installation**:
  - Ensure the `catanatron` package is installed correctly and includes the `models.player` module.
  - Reinstall or update the package if necessary.
- **Check Import Paths**:
  - Confirm that the import paths in `adapters.py` and `foo_player.py` align with the actual module structure of the installed Catanatron package.

---
Let me know if you need anything else.
END ANALYZER
================================ Human Message =================================


ANALYZER OBJECTIVE:

Start with: "After Running The New foo_player.py Player, Here is my analysis and findings:"

If the game failed to compile/run (no game_results JSON or score==0):
- ERROR SUMMARY:
  - First error line (verbatim), exception type, file, exact line number, and the exact code line (from game_output.txt).
- LIKELY CAUSE (1–2 bullets): short hypothesis based on the error/log text (e.g., unknown ActionType, bad import, attribute missing).
- QUICK FIX FOCUS: 1–2 bullets pointing to the specific function/line in foo_player.py (or adapters.py) to inspect.

If the game ran (game_results JSON present):
1) PERFORMANCE SUMMARY:
   - Outcome (Win/Loss), our VP vs opponent VP, VP diff.
   - Key counts: cities, settlements, roads, dev cards (if available), total turns.
2) VERDICT:
   - Good if Win OR VP diff ≥ +0.5
   - Borderline if −0.5 < VP diff < +0.5
   - Poor if Loss OR VP diff ≤ −0.5
3) IF BORDERLINE/POOR — LIKELY REASONS:
   - Briefly scan foo_player.py and list 2–4 concrete issues with short citations (line numbers/snippets), prioritizing:
     - Missing 1-ply value lookahead (no `copy_game` + `make_value_fn` usage).
     - No chance handling (dice/dev/robber), or robber/knight policy absent.
     - Placement helpers stubbed/always False (roads/settlements).
     - No end-turn policy or repeated random selection.
     - Illegal/unknown actions (e.g., trying to play `VICTORY_POINT`).
   - Pull 2–4 corroborating log lines from game_output.txt (e.g., "Unrecognized action type", "Defaulting to Random Action", stack traces).
4) NEXT STEP (one line):
   - Clear route like: "Send to Coder to add 1-ply value lookahead", or "Send to Strategizer to specify robber/placement policy", etc.

End with: "Let me know if you need anything else".

================================== Ai Message ==================================

ANALYZER
---
After Running The New foo_player.py Player, Here is my analysis and findings:

### ERROR SUMMARY
- **First Error Line**:
  ```
  ModuleNotFoundError: No module named 'catanatron.models.player'
  ```
- **Exception Type**: `ModuleNotFoundError`
- **File**: `/Users/dakotabarnes/Develop/CollectiveComputingLabs/strategy-game-agents/catanatron/catanatron_core/catanatron/models/board.py`
- **Line Number**: 9
- **Exact Code Line**:
  ```python
  from catanatron.models.player import Color
  ```

### LIKELY CAUSE
- The error is due to a **missing or misnamed module** in the Catanatron core library.
- The `catanatron.models.player` module is required by `board.py`, but it is either:
  - Not installed in the environment.
  - Located in a different directory or renamed (e.g., `catanatron.models.player` might now be `catanatron.player` or similar).

### QUICK FIX FOCUS
- **Inspect the Catanatron Core Installation**:
  - Verify the existence of the `catanatron.models.player` module in the installed package.
  - If missing, reinstall or update the Catanatron package to ensure all dependencies are correctly installed.
- **Check Import Paths in `adapters.py`**:
  - Ensure the import paths in `adapters.py` align with the actual module structure of the installed Catanatron package.
  - If the module has been renamed or moved, update the import statement in `adapters.py` to reflect the correct path.

---
Let me know if you need anything else.
END ANALYZER
================================ Human Message =================================

This is the current performance history

{
  "Evolution 0": {
    "wins": 0,
    "avg_score": 2.0,
    "avg_turns": 64.4,
    "full_game_log_path": "game_20250925_062130_fg/game_output.txt",
    "json_game_results_path": "game_20250925_062130_fg/game_20250925_062130_fg.json",
    "cur_foo_player_path": "game_20250925_062130_fg/foo_player.py",
    "cli_run_id": "game_20250925_062130_fg",
    "timestamp": "2025-09-25 06:22:24"
  },
  "Evolution 1": {
    "wins": 0,
    "avg_score": 2.433333333333333,
    "avg_turns": 66.86666666666666,
    "full_game_log_path": "game_20250925_062611_fg/game_output.txt",
    "json_game_results_path": "game_20250925_062611_fg/game_20250925_062611_fg.json",
    "cur_foo_player_path": "game_20250925_062611_fg/foo_player.py",
    "cli_run_id": "game_20250925_062611_fg",
    "timestamp": "2025-09-25 06:27:04"
  },
  "Evolution 2": {
    "wins": 0,
    "avg_score": 0,
    "avg_turns": 0,
    "full_game_log_path": "game_20250925_063122_fg/game_output.txt",
    "json_game_results_path": "None",
    "cur_foo_player_path": "game_20250925_063122_fg/foo_player.py",
    "cli_run_id": "game_20250925_063122_fg",
    "timestamp": "2025-09-25 06:31:26"
  },
  "Evolution 3": {
    "wins": 0,
    "avg_score": 0,
    "avg_turns": 0,
    "full_game_log_path": "game_20250925_063519_fg/game_output.txt",
    "json_game_results_path": "None",
    "cur_foo_player_path": "game_20250925_063519_fg/foo_player.py",
    "cli_run_id": "game_20250925_063519_fg",
    "timestamp": "2025-09-25 06:35:23"
  },
  "Evolution 4": {
    "wins": 0,
    "avg_score": 0,
    "avg_turns": 0,
    "full_game_log_path": "game_20250925_064039_fg/game_output.txt",
    "json_game_results_path": "None",
    "cur_foo_player_path": "game_20250925_064039_fg/foo_player.py",
    "cli_run_id": "game_20250925_064039_fg",
    "timestamp": "2025-09-25 06:40:42"
  },
  "Evolution 5": {
    "wins": 0,
    "avg_score": 0,
    "avg_turns": 0,
    "full_game_log_path": "game_20250925_064447_fg/game_output.txt",
    "json_game_results_path": "None",
    "cur_foo_player_path": "game_20250925_064447_fg/foo_player.py",
    "cli_run_id": "game_20250925_064447_fg",
    "timestamp": "2025-09-25 06:44:51"
  },
  "Evolution 6": {
    "wins": 0,
    "avg_score": 0,
    "avg_turns": 0,
    "full_game_log_path": "game_20250925_065306_fg/game_output.txt",
    "json_game_results_path": "None",
    "cur_foo_player_path": "game_20250925_065306_fg/foo_player.py",
    "cli_run_id": "game_20250925_065306_fg",
    "timestamp": "2025-09-25 06:53:09"
  }
}
================================ Human Message =================================

This is the current game_output.txt file

Traceback (most recent call last):
  File "/Users/dakotabarnes/miniconda3/envs/catanatron/bin/catanatron-play", line 33, in <module>
    sys.exit(load_entry_point('catanatron-experimental', 'console_scripts', 'catanatron-play')())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/dakotabarnes/miniconda3/envs/catanatron/bin/catanatron-play", line 25, in importlib_load_entry_point
    return next(matches).load()
           ^^^^^^^^^^^^^^^^^^^^
  File "/Users/dakotabarnes/miniconda3/envs/catanatron/lib/python3.11/importlib/metadata/__init__.py", line 202, in load
    module = import_module(match.group('module'))
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/dakotabarnes/miniconda3/envs/catanatron/lib/python3.11/importlib/__init__.py", line 126, in import_module
    return _bootstrap._gcd_import(name[level:], package, level)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "<frozen importlib._bootstrap>", line 1204, in _gcd_import
  File "<frozen importlib._bootstrap>", line 1176, in _find_and_load
  File "<frozen importlib._bootstrap>", line 1126, in _find_and_load_unlocked
  File "<frozen importlib._bootstrap>", line 241, in _call_with_frames_removed
  File "<frozen importlib._bootstrap>", line 1204, in _gcd_import
  File "<frozen importlib._bootstrap>", line 1176, in _find_and_load
  File "<frozen importlib._bootstrap>", line 1147, in _find_and_load_unlocked
  File "<frozen importlib._bootstrap>", line 690, in _load_unlocked
  File "<frozen importlib._bootstrap_external>", line 940, in exec_module
  File "<frozen importlib._bootstrap>", line 241, in _call_with_frames_removed
  File "/Users/dakotabarnes/Develop/CollectiveComputingLabs/strategy-game-agents/catanatron/catanatron_experimental/catanatron_experimental/__init__.py", line 1, in <module>
    from catanatron_experimental.cli.simulation_accumulator import SimulationAccumulator
  File "/Users/dakotabarnes/Develop/CollectiveComputingLabs/strategy-game-agents/catanatron/catanatron_experimental/catanatron_experimental/cli/simulation_accumulator.py", line 1, in <module>
    from catanatron.game import GameAccumulator
  File "/Users/dakotabarnes/Develop/CollectiveComputingLabs/strategy-game-agents/catanatron/catanatron_core/catanatron/__init__.py", line 6, in <module>
    from catanatron.game import Game, GameAccumulator
  File "/Users/dakotabarnes/Develop/CollectiveComputingLabs/strategy-game-agents/catanatron/catanatron_core/catanatron/game.py", line 11, in <module>
    from catanatron.state import State, apply_action
  File "/Users/dakotabarnes/Develop/CollectiveComputingLabs/strategy-game-agents/catanatron/catanatron_core/catanatron/state.py", line 11, in <module>
    from catanatron.models.board import Board
  File "/Users/dakotabarnes/Develop/CollectiveComputingLabs/strategy-game-agents/catanatron/catanatron_core/catanatron/models/board.py", line 9, in <module>
    from catanatron.models.player import Color
ModuleNotFoundError: No module named 'catanatron.models.player'
================================ Human Message =================================

This is the current game_results json file

No game-results file recorded for Evolution -1.
================================ Human Message =================================

This is the current foo_player.py file

import math
import random
import statistics
import sys
import traceback
from typing import Any, Optional

# MUST import only from the adapters surface provided
from .adapters import (
    Game, Player, Color
)

# Optionally import a value function builder if adapters provide one
try:
    from .adapters import make_value_fn
    HAVE_VALUE_FN = True
except Exception:
    HAVE_VALUE_FN = False

# Hyperparameters for this player. Tweak across evolutions.
ITERATIONS = 300              # MCTS iterations per decision (budget)
MAX_DEPTH = 8                 # rollout / simulation depth
MAX_ACTIONS_TO_EVALUATE = 12  # progressive widening cap at root
UCT_C = 1.4                   # exploration constant for UCT
DEBUG = True                  # enable verbose debugging prints

# One-time diagnostic guard to avoid log flooding
_DUMPED_PLAYER_SCHEMA = False


class FooPlayer(Player):
    """A stronger FooPlayer that performs MCTS (budgeted UCT) with a
    robust static evaluator fallback.

    Behavior summary:
    - If only one legal action -> return it.
    - Otherwise run a budgeted MCTS search from the current state using
      the adapters-exposed game copy/execute APIs. The MCTS uses the
      static evaluator (self._evaluate_state) or make_value_fn (if
      available) at leaf nodes.
    - All adapter calls are defensive: multiple possible method names
      are tried (copy/clone, execute/apply, get_playable_actions/legal_actions).
    - If MCTS fails for any reason, fall back to a 1-ply static eval
      over a sampled set of actions (existing behavior).

    The evaluator extracts VP/settlements/cities/roads/dev_vp/army using
    an ordered probing plan and emits a one-time diagnostic dump if it
    cannot find any of the core metrics.
    """

    def __init__(self, name: Optional[str] = None):
        # Try to initialize the base Player with a default color if
        # constructor signatures differ across harness versions.
        try:
            super().__init__(Color.BLUE, name)
        except Exception:
            try:
                super().__init__()
            except Exception:
                # Last resort: ignore and hope harness injects necessary fields
                pass

        # Local RNG for tie-breaking and sampling
        random.seed(None)

    # ----------------------------- MCTS -----------------------------
    class _MCTSNode:
        def __init__(self, game: Game, parent: Optional['FooPlayer._MCTSNode'] = None, action: Any = None):
            self.game = game
            self.parent = parent
            self.action = action
            self.children: list[FooPlayer._MCTSNode] = []
            self.untried_actions: list = []  # to be populated at expansion
            self.visits = 0
            self.total_value = 0.0

        def avg_value(self) -> float:
            return self.total_value / self.visits if self.visits > 0 else 0.0

        def uct_score(self, c: float = UCT_C) -> float:
            # UCT value; if unvisited return +inf to ensure exploration
            if self.visits == 0:
                return float('inf')
            if self.parent is None or self.parent.visits == 0:
                return self.avg_value()
            return self.avg_value() + c * math.sqrt(math.log(self.parent.visits) / self.visits)

    def _get_playable_actions(self, game: Game):
        """Defensive attempt to query playable actions from a game state.

        Tries several common API names and returns a list (may be empty).
        """
        # Try a few common method/attribute names; return list(actions)
        try_names = [
            'get_playable_actions',
            'playable_actions',
            'legal_actions',
            'get_legal_actions',
        ]
        for name in try_names:
            try:
                attr = getattr(game, name, None)
                if attr is None:
                    continue
                if callable(attr):
                    res = attr()
                else:
                    res = attr
                if res is None:
                    continue
                # Ensure it's iterable/list
                try:
                    return list(res)
                except Exception:
                    return [res]
            except Exception:
                continue

        # As a last resort, try to look inside game.state for a helper
        try:
            st = getattr(game, 'state', None)
            if st is not None:
                for name in ('get_playable_actions', 'playable_actions', 'legal_actions'):
                    try:
                        attr = getattr(st, name, None)
                        if callable(attr):
                            res = attr()
                            if res is not None:
                                return list(res)
                    except Exception:
                        continue
        except Exception:
            pass

        return []

    def _copy_game(self, game: Game) -> Optional[Game]:
        """Defensive copy of the game state using several possible APIs."""
        try:
            # Preferred
            return game.copy()
        except Exception:
            pass
        try:
            # Alternative name
            clone = getattr(game, 'clone', None)
            if callable(clone):
                return clone()
        except Exception:
            pass
        try:
            # Try a deeper copy as last resort
            import copy

            return copy.deepcopy(game)
        except Exception:
            return None

    def _apply_action(self, game: Game, action: Any) -> bool:
        """Try to apply an action on the given game; return True on success."""
        try:
            if hasattr(game, 'execute') and callable(getattr(game, 'execute')):
                game.execute(action)
                return True
        except Exception:
            pass
        try:
            if hasattr(game, 'apply') and callable(getattr(game, 'apply')):
                game.apply(action)
                return True
        except Exception:
            pass
        try:
            if hasattr(game, 'do_action') and callable(getattr(game, 'do_action')):
                game.do_action(action)
                return True
        except Exception:
            pass
        return False

    def _is_terminal(self, game: Game) -> bool:
        """Detect terminal/finished game state."""
        try:
            if hasattr(game, 'is_terminal') and callable(getattr(game, 'is_terminal')):
                return bool(game.is_terminal())
        except Exception:
            pass
        try:
            if hasattr(game, 'game_over'):
                return bool(getattr(game, 'game_over'))
        except Exception:
            pass
        try:
            # some engines expose winning_color or similar
            if hasattr(game, 'winning_color'):
                wc = getattr(game, 'winning_color')
                if wc is not None:
                    return True
        except Exception:
            pass
        return False

    def _reward_from_game(self, game: Game, my_color: Any) -> float:
        """Compute a normalized reward in [0,1] for my_color in game.

        Prefer make_value_fn if available; otherwise attempt to extract
        victory points via the static _evaluate_state and normalize.
        Terminal wins yield reward 1.0.
        """
        # Terminal check: if game declares a winner, give 1.0 for win
        try:
            if hasattr(game, 'winning_color'):
                wc = getattr(game, 'winning_color')
                if wc is not None:
                    try:
                        if wc == my_color:
                            return 1.0
                        else:
                            return 0.0
                    except Exception:
                        # fallback to string/enum compare
                        try:
                            if str(wc) == str(my_color):
                                return 1.0 if str(wc) == str(my_color) else 0.0
                        except Exception:
                            pass
        except Exception:
            pass

        # Use value function if present
        if HAVE_VALUE_FN:
            try:
                vfn = make_value_fn(game)
                try:
                    val = vfn(game, my_color)
                except Exception:
                    val = vfn(game)
                # Normalize val to [0,1] assuming scale roughly 0..10 VP
                try:
                    v = float(val)
                    return max(0.0, min(1.0, v / 10.0))
                except Exception:
                    pass
            except Exception:
                # Fall through to static eval
                if DEBUG:
                    print('FooPlayer._reward_from_game: make_value_fn failed; falling back', file=sys.stderr)

        # Fall back: use static evaluator to estimate VP and normalize
        try:
            score = float(self._evaluate_state(game))
            # Our static score is not VPs but a weighted sum; attempt to convert
            # back to an approximate VP by dividing by 1000 (since VP*1000 is dominant)
            approx_vp = score / 1000.0
            return max(0.0, min(1.0, approx_vp / 10.0))
        except Exception:
            return 0.0

    def _simulate_rollout(self, root_game: Game, max_depth: int, my_color: Any) -> float:
        """Perform a random (or value-guided) rollout and return normalized reward."""
        try:
            game = self._copy_game(root_game)
            if game is None:
                return 0.0
            depth = 0
            while depth < max_depth and not self._is_terminal(game):
                actions = self._get_playable_actions(game)
                if not actions:
                    break
                # If we have a value function we can do a greedy pick with
                # some randomness (epsilon-greedy). Otherwise pick uniformly.
                if HAVE_VALUE_FN:
                    try:
                        # try scoring each action quickly by applying on a copy
                        best_a = None
                        best_v = -float('inf')
                        for a in actions:
                            try:
                                g2 = self._copy_game(game)
                                if g2 is None:
                                    continue
                                applied = self._apply_action(g2, a)
                                if not applied:
                                    continue
                                vfn = make_value_fn(g2)
                                try:
                                    v = vfn(g2, my_color)
                                except Exception:
                                    v = vfn(g2)
                                v = float(v)
                                if v > best_v:
                                    best_v = v
                                    best_a = a
                            except Exception:
                                continue
                        if best_a is None:
                            action = random.choice(actions)
                        else:
                            # epsilon-greedy: small chance to explore
                            if random.random() < 0.1:
                                action = random.choice(actions)
                            else:
                                action = best_a
                    except Exception:
                        action = random.choice(actions)
                else:
                    action = random.choice(actions)

                applied = self._apply_action(game, action)
                if not applied:
                    # If we couldn't apply the chosen action, break to avoid infinite loop
                    break
                depth += 1

            # Compute reward for my_color
            return self._reward_from_game(game, my_color)
        except Exception:
            if DEBUG:
                print('FooPlayer._simulate_rollout: exception during rollout', file=sys.stderr)
                traceback.print_exc()
            return 0.0

    def _run_mcts(self, game: Game, playable_actions: list, iterations: int, max_depth: int, my_color: Any):
        """Run a simple MCTS (UCT) search and return action statistics.

        Returns a dict mapping action -> (visits, total_value, avg_value).
        """
        root_game_copy = self._copy_game(game)
        if root_game_copy is None:
            raise RuntimeError('FooPlayer._run_mcts: failed to copy root game')

        root = FooPlayer._MCTSNode(root_game_copy)

        # Initialize root.untried_actions with a sample (progressive widening)
        root_actions = playable_actions
        if len(root_actions) > MAX_ACTIONS_TO_EVALUATE:
            try:
                sampled = random.sample(root_actions, MAX_ACTIONS_TO_EVALUATE)
            except Exception:
                sampled = root_actions[:MAX_ACTIONS_TO_EVALUATE]
            root.untried_actions = list(sampled)
        else:
            root.untried_actions = list(root_actions)

        for it in range(iterations):
            node = root
            # SELECTION & EXPANSION
            # Select until a node with untried actions or a leaf is reached
            while True:
                if node.untried_actions:
                    # Expand one action from untried_actions
                    try:
                        a = node.untried_actions.pop()
                    except Exception:
                        a = None
                    if a is None:
                        break
                    # Apply action on a copy of the node's game
                    gcopy = self._copy_game(node.game)
                    if gcopy is None:
                        break
                    applied = self._apply_action(gcopy, a)
                    if not applied:
                        # skip this action
                        continue
                    child = FooPlayer._MCTSNode(gcopy, parent=node, action=a)
                    # populate child's untried_actions lazily
                    try:
                        acts = self._get_playable_actions(gcopy)
                        if len(acts) > MAX_ACTIONS_TO_EVALUATE:
                            child.untried_actions = random.sample(acts, min(len(acts), MAX_ACTIONS_TO_EVALUATE))
                        else:
                            child.untried_actions = list(acts)
                    except Exception:
                        child.untried_actions = []
                    node.children.append(child)
                    node = child
                    break
                else:
                    # No untried actions: descend to best child
                    if not node.children:
                        break
                    # pick child with highest UCT
                    try:
                        node = max(node.children, key=lambda n: n.uct_score(UCT_C))
                    except Exception:
                        # fallback to visits
                        node = max(node.children, key=lambda n: n.visits)

            # SIMULATION from node.game
            reward = self._simulate_rollout(node.game, max_depth, my_color)

            # BACKPROPAGATION
            while node is not None:
                node.visits += 1
                node.total_value += reward
                node = node.parent

        # Aggregate stats for root's children
        stats = {}
        for child in root.children:
            try:
                visits = child.visits
                total = child.total_value
                avg = (total / visits) if visits > 0 else 0.0
                stats[child.action] = (visits, total, avg)
            except Exception:
                continue
        return stats

    # ------------------------ Decide (entry point) ------------------------
    def decide(self, game: Game, playable_actions):
        # Defensive: no actions
        if not playable_actions:
            if DEBUG:
                print('FooPlayer.decide: no playable_actions -> returning None')
            return None

        # Ensure list
        try:
            actions = list(playable_actions)
        except Exception:
            try:
                return playable_actions[0]
            except Exception:
                return None

        # Trivial case
        if len(actions) == 1:
            if DEBUG:
                print('FooPlayer.decide: only one action -> returning it')
            return actions[0]

        # Try running MCTS; if it fails, fall back to 1-ply static evaluation
        try:
            if DEBUG:
                print(f'FooPlayer.decide: starting MCTS with ITERATIONS={ITERATIONS}, MAX_DEPTH={MAX_DEPTH}')
            stats = self._run_mcts(game, actions, ITERATIONS, MAX_DEPTH, getattr(self, 'color', None))
            if not stats:
                raise RuntimeError('MCTS produced no child stats')

            # Choose action by highest visit count, tie-break by avg value
            best_action = None
            best_visits = -1
            best_avg = -float('inf')
            for a, (visits, total, avg) in stats.items():
                if visits > best_visits or (visits == best_visits and avg > best_avg):
                    best_action = a
                    best_visits = visits
                    best_avg = avg

            if best_action is None:
                raise RuntimeError('MCTS failed to select an action')

            if DEBUG:
                print(f'FooPlayer.decide: MCTS selected action {repr(best_action)} visits={best_visits} avg={best_avg}')

            return best_action

        except Exception as e:
            if DEBUG:
                print(f'FooPlayer.decide: MCTS failed with error: {e}; falling back to 1-ply eval', file=sys.stderr)
                traceback.print_exc()

            # Fall back: evaluate up to MAX_ACTIONS_TO_EVALUATE actions via static eval
            # (this code mirrors the previous implementation but is local here)
            if len(actions) > MAX_ACTIONS_TO_EVALUATE:
                try:
                    candidates = random.sample(actions, MAX_ACTIONS_TO_EVALUATE)
                except Exception:
                    candidates = actions[:MAX_ACTIONS_TO_EVALUATE]
                if DEBUG:
                    print(f'FooPlayer.decide: sampled {len(candidates)} of {len(actions)} actions to evaluate')
            else:
                candidates = actions
                if DEBUG:
                    print(f'FooPlayer.decide: evaluating all {len(candidates)} actions')

            scores = []
            for i, action in enumerate(candidates):
                try:
                    new_game = self._copy_game(game)
                    if new_game is None:
                        if DEBUG:
                            print(f'FooPlayer.decide: unable to copy game for action #{i}; marking -inf')
                        scores.append((action, float('-inf')))
                        continue

                    executed = False
                    try:
                        new_game.execute(action)
                        executed = True
                    except Exception:
                        try:
                            new_game.apply(action)
                            executed = True
                        except Exception:
                            executed = False

                    if not executed:
                        if DEBUG:
                            print(f'FooPlayer.decide: failed to execute candidate action #{i}; marking score -inf')
                        scores.append((action, float('-inf')))
                        continue

                    if HAVE_VALUE_FN:
                        try:
                            vfn = make_value_fn(new_game)
                            try:
                                val = vfn(new_game, getattr(self, 'color', None))
                            except Exception:
                                val = vfn(new_game)
                            score = float(val)
                            scores.append((action, score))
                            if DEBUG:
                                print(f'FooPlayer.decide: action #{i} -> value_fn score {score}')
                            continue
                        except Exception:
                            if DEBUG:
                                print(f'FooPlayer.decide: make_value_fn failed for action #{i}; falling back to static eval', file=sys.stderr)

                    score = self._evaluate_state(new_game)
                    scores.append((action, score))
                    if DEBUG:
                        print(f'FooPlayer.decide: action #{i} -> score {score}')

                except Exception as e2:
                    if DEBUG:
                        print(f'FooPlayer.decide: exception while evaluating action #{i}: {e2}! Marking -inf', file=sys.stderr)
                        traceback.print_exc()
                    scores.append((action, float('-inf')))

            if not scores:
                if DEBUG:
                    print('FooPlayer.decide: no scores produced -> defaulting to first action')
                return actions[0]

            try:
                max_score = max(score for (_, score) in scores)
            except Exception:
                max_score = float('-inf')

            best_candidates = [a for (a, s) in scores if s == max_score]
            if not best_candidates or max_score == float('-inf'):
                if DEBUG:
                    print('FooPlayer.decide: all evaluations failed -> defaulting to first action')
                return actions[0]

            chosen = random.choice(best_candidates)
            if DEBUG:
                try:
                    print(f'FooPlayer.decide: selected action -> {repr(chosen)} with score {max_score}')
                except Exception:
                    print('FooPlayer.decide: selected an action (repr failed)')
            return chosen

    # ------------------- Static evaluation (copied and hardened) -------------------
    def _evaluate_state(self, game: Game) -> float:
        """Static evaluation of a game state from this player's perspective.

        Robust player lookup and extraction plan implemented here. This
        function follows the Strategizer's recommendations for attribute
        probing and emits a one-time diagnostic dump if probing fails to
        find useful information.
        """
        global _DUMPED_PLAYER_SCHEMA

        # Default metric values
        vp = 0
        settlements = 0
        cities = 0
        roads = 0
        dev_vp = 0
        army = 0

        # Defensive player container lookup
        players = None
        try:
            players = getattr(game, 'state', None)
            if players is not None:
                # Prefer game.state.players but guard against different shapes
                try:
                    players = getattr(players, 'players', None) or getattr(game, 'players', None)
                except Exception:
                    players = getattr(game, 'players', None) or getattr(players, 'players', None)
        except Exception:
            players = None

        if players is None:
            try:
                players = getattr(game, 'players', None)
            except Exception:
                players = None

        if players is None:
            try:
                players = getattr(game, 'player_state', None)
            except Exception:
                players = None

        # Helper: attempt to canonicalize keys we will probe
        def _candidate_keys():
            keys = []
            keys.append(getattr(self, 'color', None))
            try:
                keys.append(str(getattr(self, 'color', None)))
            except Exception:
                pass
            keys.append(getattr(getattr(self, 'color', None), 'name', None))
            try:
                keys.append(int(getattr(self, 'color', None)))
            except Exception:
                pass
            return [k for k in keys if k is not None]

        player_obj = None
        player_key_used = None

        # If players is a dict-like mapping, try direct key access then fallbacks
        try:
            if isinstance(players, dict):
                for key in _candidate_keys():
                    try:
                        if key in players:
                            player_obj = players[key]
                            player_key_used = key
                            break
                    except Exception:
                        # Some keys may not be valid for 'in' checks; ignore
                        continue
                # Fallback: iterate values and match by attributes
                if player_obj is None:
                    for p in players.values():
                        try:
                            if (hasattr(p, 'color') and getattr(p, 'color', None) == getattr(self, 'color', None)):
                                player_obj = p
                                break
                            if isinstance(p, dict) and ('color' in p and p.get('color') == getattr(self, 'color', None)):
                                player_obj = p
                                break
                            if hasattr(p, 'name') and getattr(p, 'name', None) == getattr(self, 'name', None):
                                player_obj = p
                                break
                        except Exception:
                            continue

            # If players is a list/tuple/iterable, iterate and match by attributes
            elif isinstance(players, (list, tuple)):
                for p in players:
                    try:
                        if (hasattr(p, 'color') and getattr(p, 'color', None) == getattr(self, 'color', None)):
                            player_obj = p
                            break
                        if hasattr(p, 'name') and getattr(p, 'name', None) == getattr(self, 'name', None):
                            player_obj = p
                            break
                        if isinstance(p, dict) and ('color' in p and p.get('color') == getattr(self, 'color', None)):
                            player_obj = p
                            break
                    except Exception:
                        continue
                # Fallback to index mapping if available
                if player_obj is None and hasattr(self, 'index'):
                    try:
                        idx = getattr(self, 'index')
                        player_obj = players[idx]
                        player_key_used = idx
                    except Exception:
                        player_obj = None

            # If players is a single object (not mapping/list), treat as the player container
            else:
                # If game exposes a direct player object
                if players is not None:
                    player_obj = players

        except Exception:
            player_obj = None

        # As a last resort choose a first-entry fallback to avoid crashing
        if player_obj is None:
            try:
                # If mapping-like
                if isinstance(players, dict):
                    vals = list(players.values())
                    if vals:
                        player_obj = vals[0]
                        player_key_used = list(players.keys())[0]
                elif isinstance(players, (list, tuple)) and len(players) > 0:
                    player_obj = players[0]
                    player_key_used = 0
                else:
                    # Give up; player_obj remains None
                    player_obj = None
            except Exception:
                player_obj = None

        # Now attempt to extract metrics from player_obj using ordered attempts
        def _to_int(x: Any) -> Optional[int]:
            try:
                if x is None:
                    return None
                if isinstance(x, bool):
                    return int(x)
                if isinstance(x, (list, tuple, set)):
                    return len(x)
                # If it's callable, call it and then try convert
                if callable(x):
                    x = x()
                return int(x)
            except Exception:
                return None

        try:
            p = player_obj
            # Victory Points (vp)
            for attr in ('victory_points', 'victoryPoints', 'vp', 'points'):
                try:
                    if isinstance(p, dict) and attr in p:
                        val = p[attr]
                    else:
                        val = getattr(p, attr, None)
                    if callable(val):
                        val = val()
                    iv = _to_int(val)
                    if iv is not None:
                        vp = iv
                        break
                except Exception:
                    continue

            # If game exposes a helper, try it
            if vp == 0:
                try:
                    if hasattr(game, 'get_victory_points'):
                        try:
                            # Try passing player object
                            val = game.get_victory_points(p)
                            vv = _to_int(val)
                            if vv is not None:
                                vp = vv
                        except Exception:
                            # Maybe get_victory_points expects a player index/color
                            try:
                                val = game.get_victory_points(getattr(self, 'color', None))
                                vv = _to_int(val)
                                if vv is not None:
                                    vp = vv
                            except Exception:
                                pass
                except Exception:
                    pass

            # Settlements
            for attr in ('settlements', 'settlement_positions', 'settlement_count', 'settle_list', 'settles'):
                try:
                    if isinstance(p, dict) and attr in p:
                        val = p[attr]
                    else:
                        val = getattr(p, attr, None)
                    if callable(val):
                        val = val()
                    iv = _to_int(val)
                    if iv is not None:
                        settlements = iv
                        break
                except Exception:
                    continue

            # Cities
            for attr in ('cities', 'city_count'):
                try:
                    if isinstance(p, dict) and attr in p:
                        val = p[attr]
                    else:
                        val = getattr(p, attr, None)
                    if callable(val):
                        val = val()
                    iv = _to_int(val)
                    if iv is not None:
                        cities = iv
                        break
                except Exception:
                    continue

            # Roads
            for attr in ('roads', 'road_count'):
                try:
                    if isinstance(p, dict) and attr in p:
                        val = p[attr]
                    else:
                        val = getattr(p, attr, None)
                    if callable(val):
                        val = val()
                    iv = _to_int(val)
                    if iv is not None:
                        roads = iv
                        break
                except Exception:
                    continue

            # Dev VP
            for attr in ('dev_vp', 'dev_points'):
                try:
                    if isinstance(p, dict) and attr in p:
                        val = p[attr]
                    else:
                        val = getattr(p, attr, None)
                    if callable(val):
                        val = val()
                    iv = _to_int(val)
                    if iv is not None:
                        dev_vp = iv
                        break
                except Exception:
                    continue
            # If not found, try counting vp-like dev cards
            if dev_vp == 0:
                try:
                    if hasattr(p, 'dev_cards'):
                        cards = getattr(p, 'dev_cards')
                        if callable(cards):
                            cards = cards()
                        # Count cards that look like victory VPs
                        count = 0
                        for d in cards:
                            try:
                                if getattr(d, 'is_victory', False) or getattr(d, 'type', None) == 'vp':
                                    count += 1
                            except Exception:
                                continue
                        if count:
                            dev_vp = count
                except Exception:
                    pass

            # Army
            for attr in ('army_size', 'largest_army'):
                try:
                    if isinstance(p, dict) and attr in p:
                        val = p[attr]
                    else:
                        val = getattr(p, attr, None)
                    if callable(val):
                        val = val()
                    iv = _to_int(val)
                    if iv is not None:
                        army = iv
                        break
                except Exception:
                    continue

        except Exception as e:
            if DEBUG:
                print('FooPlayer._evaluate_state: exception during probing:', e, file=sys.stderr)
                traceback.print_exc()
            # In the event of unexpected errors, return a very low score to
            # discourage picking states we couldn't evaluate.
            return float(-1e6)

        # If we failed to extract useful metrics, emit a one-time diagnostic
        # dump to help adjust the probing logic. This prints to stderr and
        # is gated by a process-level flag so it only happens once.
        try:
            if DEBUG and not _DUMPED_PLAYER_SCHEMA and vp == 0 and settlements == 0 and cities == 0 and roads == 0:
                print('\n=== DIAGNOSTIC DUMP (FooPlayer) ===', file=sys.stderr)
                try:
                    print(f'Game type: {type(game)}', file=sys.stderr)
                    print(f'Game.state type: {type(getattr(game, "state", None))}', file=sys.stderr)
                    print(f'Players container type: {type(players)}', file=sys.stderr)
                    try:
                        plen = len(players) if players is not None else 'N/A'
                    except Exception:
                        plen = 'N/A'
                    print(f"Players length: {plen}", file=sys.stderr)

                    # If it's a mapping, show keys and a sample of values
                    if isinstance(players, dict):
                        print('Player keys:', list(players.keys())[:10], file=sys.stderr)
                        cnt = 0
                        for k, v in list(players.items())[:4]:
                            print(f'-- Player key: {k} type: {type(v)}', file=sys.stderr)
                            try:
                                preview = repr(v)
                                print('   repr:', preview[:200], file=sys.stderr)
                            except Exception:
                                print('   repr: <unrepr-able>', file=sys.stderr)
                            try:
                                attrs = [a for a in dir(v) if not a.startswith('_')]
                                print('   attrs sample:', attrs[:40], file=sys.stderr)
                            except Exception:
                                print('   attrs: <failed>', file=sys.stderr)
                            cnt += 1
                    elif isinstance(players, (list, tuple)):
                        for idx, v in enumerate(list(players)[:4]):
                            print(f'-- Player idx: {idx} type: {type(v)}', file=sys.stderr)
                            try:
                                preview = repr(v)
                                print('   repr:', preview[:200], file=sys.stderr)
                            except Exception:
                                print('   repr: <unrepr-able>', file=sys.stderr)
                            try:
                                attrs = [a for a in dir(v) if not a.startswith('_')]
                                print('   attrs sample:', attrs[:40], file=sys.stderr)
                            except Exception:
                                print('   attrs: <failed>', file=sys.stderr)
                    else:
                        # Print a small repr of the players object
                        try:
                            print('Players repr:', repr(players)[:400], file=sys.stderr)
                        except Exception:
                            print('Players repr: <failed>', file=sys.stderr)

                except Exception:
                    print('Diagnostic dump failed to fully collect details', file=sys.stderr)
                    traceback.print_exc()
                # mark dumped so we don't flood logs
                _DUMPED_PLAYER_SCHEMA = True
        except Exception:
            # If diagnostic printing causes an issue, swallow it -- do not
            # crash the harness for debugging output.
            try:
                traceback.print_exc()
            except Exception:
                pass

        # Build a composite score. Primary contributor is victory points.
        # Use the Strategizer's recommended formula (VP prioritized):
        # score = vp*1000 + cities*100 + settlements*10 + roads*3 + dev_vp*50 + army*50
        try:
            score = float(vp * 1000 + cities * 100 + settlements * 10 + roads * 3 + dev_vp * 50 + army * 50)
        except Exception:
            # Defensive fallback
            score = float(vp)

        if DEBUG:
            try:
                print(f'FooPlayer._evaluate_state: vp={vp}, cities={cities}, settlements={settlements}, roads={roads}, dev_vp={dev_vp}, army={army} -> score={score}')
            except Exception:
                print('FooPlayer._evaluate_state: computed a score (repr failed)')

        return score

================================ Human Message =================================

This is the current adapters.py file

"""Unified adapter for Catanatron agents.

Expose a small, stable surface for multi-agent systems to:
- Inspect game state
- Enumerate legal actions
- Execute hypothetical moves (with/without validation)
- Expand chance outcomes (dice, dev cards, robber)
- Use pruning helpers
- Build/evaluate heuristics

Everything here is a thin re-export or trivial wrapper from catanatron & friends.
"""
### KEEP THESE IMPORTS BELOW THIS LINE ###
from catanatron.game import Game  # has .state, .copy(), .execute(), .winning_color()
from catanatron.models.player import Player, Color
### KEEP THESE IMPORTS ABOVE THIS LINE ###
================================ Human Message =================================


ANALYZER OBJECTIVE:

Start with: "After Running The New foo_player.py Player, Here is my analysis and findings:"

If the game failed to compile/run (no game_results JSON or score==0):
- ERROR SUMMARY:
  - First error line (verbatim), exception type, file, exact line number, and the exact code line (from game_output.txt).
- LIKELY CAUSE (1–2 bullets): short hypothesis based on the error/log text (e.g., unknown ActionType, bad import, attribute missing).
- QUICK FIX FOCUS: 1–2 bullets pointing to the specific function/line in foo_player.py (or adapters.py) to inspect.

If the game ran (game_results JSON present):
1) PERFORMANCE SUMMARY:
   - Outcome (Win/Loss), our VP vs opponent VP, VP diff.
   - Key counts: cities, settlements, roads, dev cards (if available), total turns.
2) VERDICT:
   - Good if Win OR VP diff ≥ +0.5
   - Borderline if −0.5 < VP diff < +0.5
   - Poor if Loss OR VP diff ≤ −0.5
3) IF BORDERLINE/POOR — LIKELY REASONS:
   - Briefly scan foo_player.py and list 2–4 concrete issues with short citations (line numbers/snippets), prioritizing:
     - Missing 1-ply value lookahead (no `copy_game` + `make_value_fn` usage).
     - No chance handling (dice/dev/robber), or robber/knight policy absent.
     - Placement helpers stubbed/always False (roads/settlements).
     - No end-turn policy or repeated random selection.
     - Illegal/unknown actions (e.g., trying to play `VICTORY_POINT`).
   - Pull 2–4 corroborating log lines from game_output.txt (e.g., "Unrecognized action type", "Defaulting to Random Action", stack traces).
4) NEXT STEP (one line):
   - Clear route like: "Send to Coder to add 1-ply value lookahead", or "Send to Strategizer to specify robber/placement policy", etc.

End with: "Let me know if you need anything else".


--- Output from ANALYZER at 20250925_065317 ---
================================== Ai Message ==================================

ANALYZER
---
After Running The New foo_player.py Player, Here is my analysis and findings:

### ERROR SUMMARY
- **First Error Line**:
  ```
  ModuleNotFoundError: No module named 'catanatron.models.player'
  ```
- **Exception Type**: `ModuleNotFoundError`
- **File**: `/Users/dakotabarnes/Develop/CollectiveComputingLabs/strategy-game-agents/catanatron/catanatron_core/catanatron/models/board.py`
- **Line Number**: 9
- **Exact Code Line**:
  ```python
  from catanatron.models.player import Color
  ```

### LIKELY CAUSE
- The error is due to a **missing or misnamed module** in the Catanatron core library.
- The `catanatron.models.player` module is required by `board.py`, but it is either:
  - Not installed in the environment.
  - The module structure has changed (e.g., `catanatron.models.player` may have been renamed or moved to `catanatron.player` or similar).

### QUICK FIX FOCUS
- **Verify Catanatron Installation**:
  - Check if the `catanatron.models.player` module exists in the installed package.
  - If missing, reinstall or update the Catanatron package to ensure all dependencies are correctly installed.
- **Check Import Paths in `adapters.py`**:
  - Confirm that the import paths in `adapters.py` align with the actual module structure of the installed Catanatron package.
  - If the module has been renamed or moved, update the import statement in `adapters.py` to reflect the correct path.

---
Let me know if you need anything else.
END ANALYZER

