--- Input for CODER at 20250925_065418 ---
================================ Human Message =================================

- META THOUGHTS:
The FooPlayer's current performance is abysmal, with 0 wins and a significant VP deficit compared to the AlphaBeta opponent. The root cause is the naive decision-making logic, which always selects the first available action without any evaluation. The Analyzer has identified this flaw and proposed a 1-ply value lookahead strategy as a foundational improvement. This strategy will use `copy_game` and `base_fn` from `adapters.py` to evaluate actions based on immediate outcomes, replacing the current reactive approach. While this may not be sufficient to consistently beat the AlphaBeta opponent, it is a critical first step toward introducing strategic decision-making.

- META GOAL:
Systematically improve the `foo_player.py` code to develop a strategic algorithm that can consistently win against the AlphaBeta opponent. The immediate focus is on implementing a 1-ply value lookahead to replace the naive decision-making logic.

- CHOSEN AGENT: CODER

- AGENT OBJECTIVE:
Implement a **1-ply value lookahead** strategy in `foo_player.py` to replace the current naive logic (`return playable_actions[0]`). This involves:
1. Using `copy_game` to simulate the game state after executing each action in `playable_actions`.
2. Using `base_fn` to evaluate the value of the resulting game state for each simulated action.
3. Selecting the action with the highest evaluated value as the chosen move.
Ensure the implementation is robust and handles edge cases, such as empty `playable_actions`. Do not introduce additional heuristics or rule-based logic. Focus solely on the 1-ply lookahead using the provided adapters.
================================== Ai Message ==================================

CODER

Changes made to foo_player.py:
- Rewrote the FooPlayer.decide implementation to perform a 1-ply expected-value lookahead instead of always choosing the first action.
- Added imports from adapters: copy_game, execute_deterministic, base_fn (keeps the required adapters surface usage).
- Implementation details:
  - For each legal action:
    - Copy the current game with copy_game to avoid side effects.
    - Execute the action using execute_deterministic which returns a list of (game_after, probability).
    - Evaluate each outcome with the value function produced by base_fn() for this player's color.
    - Compute an expected value as the probability-weighted sum of outcome values.
  - Select the action with the highest expected value. If all evaluations fail, fallback to the first action.
  - If no playable_actions are provided, return None.
- Added robust error handling:
  - Catches exceptions in execution or evaluation and assigns -inf to bad outcomes so they won't be chosen.
- Added print debug statements to report:
  - When there are no playable actions.
  - The expected value computed for each action.
  - Any exceptions during evaluation.
  - The final chosen action and its value.
- Comments: Included extensive comments explaining the strategy, assumptions, and debugging outputs.

Files touched:
- foo_player.py (completely replaced/updated with the new code)

New functions or major code blocks added:
- No new top-level functions were created. The FooPlayer.decide method was substantially rewritten to implement the 1-ply lookahead.

Adapter usage assurances:
- The code only uses the adapters surface (copy_game, execute_deterministic, base_fn) and does not import catanatron directly. The required import pattern from .adapters is preserved.

Print statements added:
- Several print() calls added for debugging and traceability (action evaluations, exceptions, chosen action).

Potential issues / uncertainties / notes for next iteration:
- execute_deterministic is assumed to return a list of (game_after, probability). If deterministic actions always return a single (game, 1.0) tuple, this still works; the code handles multiple outcomes for generality.
- base_fn() is used with default parameters (DEFAULT_WEIGHTS) as provided by adapters. If you want tuned weights or a different value function, consider using get_value_fn or contender_fn from adapters.
- This is a single-ply lookahead. Against strong opponents (AlphaBeta), deeper search or better pruning will likely be necessary for competitive play.
- Next improvements to request:
  - Use expand_spectrum or execute_spectrum to handle chance nodes more precisely (e.g., dice, dev card draws) when relevant.
  - Use list_prunned_actions or prune_robber_actions to reduce branching before simulation.
  - Consider implementing 2-ply (expectimax/minimax) search or integrating the AlphaBeta player utilities if allowed.
  - Optionally expose configurable value function weights and allow passing a custom value function to FooPlayer.

If you'd like, I can:
- Implement a 2-ply expectimax or alpha-beta search using the adapters helpers.
- Integrate action pruning to reduce branching factor.
- Expose an init parameter to FooPlayer to select different value functions or weight sets.

END CODER
================================ Human Message =================================

- META THOUGHTS:
The Strategizer has proposed a clear and actionable plan to implement a **multi-ply Expectimax algorithm** with probabilistic simulation. This approach addresses the current limitations of the 1-ply lookahead by incorporating deeper strategic planning and handling uncertainty through `execute_spectrum`. The use of Expectimax is well-suited for Catan's mix of adversarial and chance-based elements. The pseudocode provided is a strong foundation for the Coder to build upon, and the focus on leveraging `adapters.py` functions ensures alignment with the project's goals.

- META GOAL:
Systematically improve the `foo_player.py` code to develop a strategic algorithm that can consistently outperform the AlphaBeta opponent. The immediate focus is on implementing the **multi-ply Expectimax algorithm** with probabilistic simulation.

- CHOSEN AGENT: CODER

- AGENT OBJECTIVE:
Implement the **multi-ply Expectimax algorithm** with probabilistic simulation as proposed by the Strategizer. Specifically:
1. Create a recursive `expectimax` function that:
   - Maximizes for the player's turn (adversarial node).
   - Expects for chance nodes (probabilistic outcomes).
   - Evaluates terminal states using `base_fn` or `contender_fn`.
2. Integrate probabilistic simulation using `execute_spectrum` for actions involving chance (e.g., dice rolls, robber placement, dev cards).
3. Update the `decide` method to use the `expectimax` function with a depth of 2.
4. Ensure the implementation is robust, with error handling and debugging prints for traceability.
5. Optimize performance by pruning actions and caching intermediate results where possible.

Provide a clear and concise implementation that adheres to the proposed strategy.
================================== Ai Message ==================================

CODER

Changes made to foo_player.py:
- Replaced the previous 1-ply evaluation with a 2-ply Expectimax search (multi-ply framework) that uses probabilistic simulation.
- Key adapter functions used (only from .adapters): copy_game, execute_spectrum, execute_deterministic (fallback), list_prunned_actions, base_fn.
- Implemented a recursive inner function expectimax(node_game, depth) inside decide:
  - Evaluates terminal states or depth==0 with value_fn = base_fn().
  - Uses list_prunned_actions(node_game) to reduce branching.
  - Treats nodes whose acting color equals self.color as MAX nodes; otherwise as MIN nodes (adversarial).
  - For each action at a node, calls execute_spectrum(node_game, action) to get (game_after, probability) outcomes and computes expected values by recursively calling expectimax on outcomes.
  - Returns max over actions at MAX nodes, min over actions at MIN nodes (both using expected values over chance outcomes).
- Top-level decide:
  - Iterates over provided playable_actions (keeps original ordering & fallback behavior).
  - For each top-level action, uses copy_game and execute_spectrum to produce outcomes.
  - Calls expectimax on each outcome with depth = MAX_DEPTH - 1 (MAX_DEPTH set to 2).
  - Picks the action with the highest expected value; falls back to the first action if all evaluations fail.
- Robustness and fallback:
  - If execute_spectrum fails for a given action, falls back to execute_deterministic.
  - Many try/excepts added around adapter calls and recursive calls; debug prints issued for failures so bad branches get -inf/inf and are avoided.
- Debugging:
  - Added informative print statements:
    - When there are no playable actions.
    - When execute_spectrum/execute_deterministic fails.
    - Expected values for each top-level action and intermediate errors inside expectimax.
    - Final chosen action and its value.
- Comments:
  - Added explanatory comments throughout the code clarifying assumptions, behavior, and why adapters are used.

New functions / code blocks added:
- expectimax (inner recursive function inside decide) — implements the Expectimax recursion.
- MAX_DEPTH constant defined inside decide (set to 2 plies as requested).

Adapter usage notes / assurances:
- The code only imports and uses functions from .adapters; there are no direct imports from catanatron or other modules.
- Uses execute_spectrum to handle probabilistic actions; execute_deterministic is retained as a fallback for actions that do not support spectrum execution.

Print statements added:
- Prints added for:
  - No playable actions.
  - execute_spectrum failure handling (top-level and within expectimax).
  - execute_deterministic fallback failure.
  - Each top-level action's expected value.
  - Unexpected errors encountered during expectimax recursion.
  - Final chosen action and the corresponding expected value.

Potential issues / items for follow-up:
- Performance: This implementation does not implement caching (transposition table) or move ordering; deeper depths may be slow. Consider implementing memoization keyed by a game state hash or using adapters' expand_spectrum to batch expansions.
- Robber-specific pruning: list_prunned_actions is used to reduce branching, but there may be additional adapters like prune_robber_actions that can further reduce branching on robber moves — consider integrating that for efficiency.
- Terminal detection: The code attempts to call node_game.winning_color() per adapters' documentation. If the Game implementation uses a different API for terminal detection, this may need adjustment.
- Heuristic configuration: Currently uses base_fn() default weights. Consider exposing contender_fn or get_value_fn to test alternate heuristics.
- MIN node model: I treat opponent nodes as minimizers of our heuristic. If opponent behavior is not adversarial (e.g., noisy or stochastic), consider modeling opponent as expected-value maximizer for their own color or using opponent-specific value functions.
- Edge cases: If execute_spectrum returns an empty list for an action (unexpected), the code treats expected value as -inf and skips that action.

Suggestions for the next objective (to help Coder in next iteration):
- Add a transposition cache keyed by a deterministic serialization or hash of Game state to avoid recomputing expectimax on repeated states.
- Consider exposing MAX_DEPTH as a constructor parameter or environment/config option to allow tuning.
- Replace MIN-node heuristic with opponent-specific value function (e.g., evaluate from opponent perspective) or simulate opponent policy if known.
- Use prune_robber_actions for robber-specific pruning and expand_spectrum for batched expansion if available and beneficial.
- Add a simple time or node budget to avoid long blocking decisions and allow dynamic depth (iterative deepening).

If you'd like, I can:
- Add memoization (transposition table) to expectimax.
- Integrate prune_robber_actions and expand_spectrum for more efficient chance handling.
- Expose depth or value function choices via FooPlayer constructor arguments.

END CODER
================================ Human Message =================================

- META THOUGHTS:
The Strategizer has proposed a comprehensive plan to address the current limitations of the FooPlayer. The focus on **3-4 ply Expectimax search**, **custom heuristics**, and **search optimizations** directly targets the issues identified by the Analyzer. The use of iterative deepening, move ordering, and caching will help manage computational complexity, while specialized heuristics for robber placement, development cards, and expansion will improve strategic decision-making. The pseudocode provided is clear and actionable, ensuring the Coder can implement the changes effectively.

- META GOAL:
Develop a **deeper search strategy** (3-4 ply) with **custom heuristics** and **search optimizations** to consistently outperform the AlphaBeta opponent in Catan.

- CHOSEN AGENT: CODER

- AGENT OBJECTIVE:
Implement the **3-4 ply Expectimax search** with **custom heuristics** and **search optimizations** as proposed by the Strategizer. Specifically:
1. **Extend Search Depth**:
   - Use **iterative deepening** to gradually increase the search depth from 1 to 3-4 plies.
   - Implement **move ordering** to prioritize high-value actions (e.g., building settlements, playing development cards).
2. **Integrate Custom Heuristics**:
   - Implement `custom_heuristic` to evaluate robber placement, development cards, and road/settlement expansion.
   - Use `prune_robber_actions` and `value_production` to inform heuristic decisions.
3. **Optimize Search Space**:
   - Use `list_prunned_actions` and `prune_robber_actions` to reduce the action space.
   - Cache intermediate results of the `expectimax` function to avoid redundant computations.
4. **Update the `decide` Method**:
   - Replace the current 2-ply search with the new 3-4 ply Expectimax.
   - Ensure robust error handling and debugging prints for traceability.

Provide a clear and concise implementation that adheres to the proposed strategy.
================================== Ai Message ==================================

CODER

I rewrote foo_player.py to implement the Strategizer's recommendations:

Summary of changes:
- Implemented iterative-deepening Expectimax (up to 3-4 plies; default max_depth=3).
- Added a transposition cache to reuse evaluations across the search.
- Implemented a node budget to limit computation per decide() call.
- Added move ordering to prioritize likely-strong actions (builds, dev cards).
- Integrated prune_robber_actions to focus robber moves.
- Used execute_spectrum for probabilistic outcomes, with execute_deterministic as a fallback.
- Implemented a conservative custom_heuristic that augments base_fn with a small production bonus via value_production.
- Added numerous print debug statements for traceability and error handling.
- Constrained MAX_DEPTH to [1..4] to balance performance.

New/modified functions:
- custom_heuristic (inner function) to safely wrap base_fn and add a small production bonus.
- expectimax (inner function) now supports caching, node budgeting, robber pruning, and move ordering.
- Iterative deepening loop in decide that reuses cache and respects node budget.

Notes / caveats:
- I used repr(g.state) as the cache key when possible; if Game exposes a deterministic hash method, replacing _state_key with that will improve caching accuracy.
- value_production usage is best-effort; if the game state shape differs, the call will silently be ignored.
- This keeps strictly to adapters.py functions; no direct catanatron imports present.
- The node budget and max depth are configurable through FooPlayer constructor parameters.

What I want for the next iteration:
- Feedback on acceptable time/node budgets and desired default max_depth (3 vs 4).
- If there's a canonical game state hash method, provide its name so I can use it for cache keys.
- Whether to add opponent-specific value functions (contender_fn) or model opponent's heuristic explicitly.

END CODER
================================ Human Message =================================

- META THOUGHTS:
The Strategizer has provided a detailed and actionable plan to address the current limitations of the FooPlayer. The proposed **4-5 ply Expectimax search** with **specialized heuristics** for road/settlement expansion, robber placement, and development card optimization directly targets the issues identified in the analysis. The use of iterative deepening, move ordering, and caching will help manage computational complexity, while the specialized heuristics will improve strategic decision-making. The pseudocode and clear implementation steps ensure the Coder can effectively translate this plan into code.

- META GOAL:
Implement a **4-5 ply Expectimax search** with **specialized heuristics** for road/settlement expansion, robber placement, and development card optimization to consistently outperform the AlphaBeta opponent.

- CHOSEN AGENT: CODER

- AGENT OBJECTIVE:
Implement the **4-5 ply Expectimax search** with **specialized heuristics** as proposed by the Strategizer. Specifically:
1. **Extend Search Depth**:
   - Use **iterative deepening** to gradually increase the search depth up to 5 plies.
   - Enforce a **node budget** (e.g., 10,000 nodes) to manage computational cost.
2. **Integrate Specialized Heuristics**:
   - Replace the generic `custom_heuristic` with **three specialized components**:
     - **Expansion Heuristic**: Evaluate long-term value using `value_production` and resource diversity.
     - **Robber Heuristic**: Target high-impact placements to disrupt opponents or secure resources.
     - **Development Card Heuristic**: Prioritize cards like "MONOPOLY" or "ROAD_BUILDING" based on game state.
   - Combine these heuristics into a **weighted sum** for evaluation.
3. **Optimize Search Space**:
   - Use `list_prunned_actions` and `prune_robber_actions` to reduce the action space.
   - Cache intermediate results of `expectimax` to avoid redundant computations.
4. **Update the `decide` Method**:
   - Replace the current `expectimax` call with the new **4-5 ply search** and **combined heuristic**.
   - Ensure the node budget is respected and iterative deepening is used.

Provide a clear and concise implementation that adheres to the proposed strategy. Include robust error handling and debugging prints for traceability.
================================== Ai Message ==================================

CODER

I updated foo_player.py to implement the Strategizer's recommendations. Summary of changes written to foo_player.py:

- Extended iterative-deepening Expectimax to support depths up to 5 (capped).
- Implemented specialized heuristics:
  - expansion_heuristic using value_production to estimate long-term expansion potential.
  - robber_heuristic to penalize opponent production (encourages disruptive robber moves).
  - dev_card_heuristic to reward availability of MONOPOLY and ROAD_BUILDING dev cards.
  - combined_heuristic that adds these small nudges to base_fn evaluation.
- Search optimizations:
  - list_prunned_actions and prune_robber_actions usage to reduce branching.
  - Move ordering via _action_priority to explore promising moves first.
  - Global node budget enforced across iterative deepening (default node_budget now 10000).
  - Transposition cache keyed by repr(game.state) and depth.
- Robustness:
  - Defensive coding with try/except around adapter calls.
  - Fallback to execute_deterministic when execute_spectrum fails.
  - Conservative scaling of heuristic components to avoid overwhelming base_fn.
- Debug prints added for traceability:
  - Iterative deepening progress, per-action expected values, node budget exhaustion, and final selection.

Report of modifications and notes:
- Created/modified functions:
  - expansion_heuristic, robber_heuristic, dev_card_heuristic, combined_heuristic (all inner functions inside decide).
  - expectimax (rewritten to include caching, budget, and specialized heuristics).
- Added print statements for debugging: iterative deepening, per-action evaluation, budget messages, and final selection.
- Caching key uses repr(g.state) if available; if a canonical hash exists in Game, using it would be better.
- The heuristics use value_production and attempt to access dev_cards defensively; if the game's state shape differs, these heuristics will safely return 0.
- Node budget and max_depth are configurable via FooPlayer constructor. Default node_budget was increased to 10000 to allow deeper search.
- No direct imports from catanatron are present; adapters surface is used exclusively.

Questions / Next steps you can include in the next OBJECTIVE:
- Provide a canonical deterministic game-state hashing method if available (to improve caching).
- Confirm acceptable default node budget and target max_depth (3, 4, or 5) for time constraints.
- Optionally allow opponent-specific value functions (contender_fn) to better model MIN nodes.
- Decide whether to add iterative deepening time cap in addition to node budget.

END CODER
================================ Human Message =================================

- META THOUGHTS:
The Strategizer has provided a clear and actionable plan to address the current performance issues. The proposed **4-5 ply Expectimax search** with **stronger heuristic weighting** and **adaptive prioritization** directly targets the over-reliance on `END_TURN` actions and the lack of strategic focus in the previous iterations. The increased weights for expansion, robber placement, and development cards, combined with the adjusted move ordering, should drive more aggressive and strategic decision-making. The use of iterative deepening and a node budget will ensure computational efficiency while enabling deeper search.

- META GOAL:
Implement a **4-5 ply Expectimax search** with **stronger heuristic weighting** and **adaptive prioritization** to consistently outperform the AlphaBeta opponent.

- CHOSEN AGENT: CODER

- AGENT OBJECTIVE:
Implement the **4-5 ply Expectimax search** with the following enhancements:
1. **Increased Heuristic Weighting**:
   - Update the `combined_heuristic` function to use the new weights:
     - Expansion: **0.25** (up from 0.08)
     - Robber: **-0.35** (up from -0.12)
     - Development Cards: **0.5 for MONOPOLY** and **0.4 for ROAD_BUILDING** (up from 0.25 and 0.15, respectively).
2. **Adaptive Prioritization**:
   - Modify the `_action_priority` function to prioritize strategic actions and deprioritize `END_TURN`:
     - `BUILD_SETTLEMENT`: **120.0**
     - `BUILD_ROAD`: **90.0**
     - `PLAY_DEV_CARD`: **130.0**
     - `END_TURN`: **-100.0**
3. **Extend Search Depth**:
   - Set `MAX_DEPTH = 5` and `NODE_BUDGET = 15000` in the `decide` method.
4. **Search Optimization**:
   - Use `list_prunned_actions` and `prune_robber_actions` to reduce the search space.
   - Cache intermediate results of `expectimax` to avoid redundant computations.

Provide a clear and concise implementation that adheres to the proposed strategy. Include robust error handling and debugging prints for traceability.
================================== Ai Message ==================================

CODER

I implemented the Strategizer's recommendations and updated foo_player.py accordingly.

Summary of concrete changes:
- Increased default max_depth to 5 and node_budget to 15000 in FooPlayer.__init__.
- Updated move ordering (_action_priority) to strongly prioritize PLAY_DEV_CARD, BUILD_SETTLEMENT, BUILD_ROAD and to strongly deprioritize END_TURN (-100.0).
- Rewrote specialized heuristics:
  - expansion_heuristic now returns raw production value; combined_heuristic applies weight 0.25.
  - robber_heuristic returns raw max opponent production; combined_heuristic applies weight -0.35.
  - dev_card_heuristic returns strong bonuses: 0.5 for MONOPOLY, 0.4 for ROAD_BUILDING.
- combined_heuristic now uses the stronger weights: exp 0.25, rob -0.35, dev card bonuses as above.
- Kept Expectimax implementation with caching, iterative deepening up to 5, move ordering, robber pruning, and node budget enforcement.
- Added/adjusted debug prints to indicate iterative deepening progress, top-level action expected values, node budget exhaustion, and final chosen action.

Notes and assumptions:
- The code continues to use only the adapters surface (copy_game, execute_spectrum, execute_deterministic, list_prunned_actions, prune_robber_actions, base_fn, value_production).
- Caching keys use repr(g.state) when available; if there is a canonical game-state hashing function, replacing _state_key with it will improve cache hits.
- Heuristics access game state defensively and will fall back to neutral values on errors.
- The stronger heuristic weights are intentionally larger to drive aggressive strategic actions rather than passive END_TURN choices.

Next suggestions / requests for the Strategizer:
- If the game exposes a deterministic state hash (e.g., game.state_hash()), provide its name so caching can be improved.
- If you want to tune the exact weights (e.g., expansion 0.20 vs 0.25), specify target values to iterate faster.
- If runtime remains high, we can add a time-based cutoff in addition to the node budget or implement partial move ordering pruning.

END CODER
================================ Human Message =================================

- META THOUGHTS:
The Strategizer has provided a well-structured plan to address the current performance issues. The proposed **dynamic heuristic weighting** and **refined move ordering** directly target the over-reliance on passive actions and the lack of strategic focus. By introducing **game phase detection**, the player can adapt its strategy based on the stage of the game, ensuring aggressive expansion in the early phase and prioritizing city upgrades and development cards in the mid/late phases. The refined move ordering will further ensure that high-value actions like `BUILD_CITY` and `PLAY_KNIGHT_CARD` are prioritized over passive moves.

- META GOAL:
Implement a **dynamic heuristic weighting system** and **refined move ordering** to aggressively prioritize strategic actions like city upgrades, knight plays, and development card usage, ensuring the FooPlayer consistently outperforms the AlphaBeta opponent.

- CHOSEN AGENT: CODER

- AGENT OBJECTIVE:
Implement the **dynamic heuristic weighting system** and **refined move ordering** as proposed by the Strategizer. Specifically:
1. **Game Phase Detection**:
   - Add a `_get_game_phase` function to the `FooPlayer` class to detect the game phase (early, mid, late) based on turn count.
2. **Dynamic Heuristic Weighting**:
   - Modify the `combined_heuristic` function to use **phase-specific weights**:
     - Early Game: `expansion_heuristic` weight = **0.4**, `robber_heuristic` weight = **-0.2**.
     - Mid/Late Game: `expansion_heuristic` weight = **0.2**, `robber_heuristic` weight = **-0.4**, `dev_card_heuristic` weight = **0.6**.
3. **Refined Move Ordering**:
   - Update the `_action_priority` function to aggressively prioritize high-value actions:
     - `BUILD_CITY`: **150.0**
     - `PLAY_DEV_CARD`: **140.0**
     - `BUILD_SETTLEMENT`: **130.0**
     - `BUILD_ROAD`: **100.0**
     - `PLAY_KNIGHT_CARD`: **140.0**
     - `END_TURN`: **-200.0**
4. **Search Optimization**:
   - Maintain the **4-5 ply depth** with iterative deepening and a **node budget of 15,000**.
   - Use `list_prunned_actions` and `prune_robber_actions` to reduce the search space.

Provide a clear and concise implementation that adheres to the proposed strategy. Include robust error handling and debugging prints for traceability.
================================== Ai Message ==================================

CODER

I updated foo_player.py with the Strategizer's recommendations and wrote the new file.

Summary of changes made:
- Added game phase detection via _get_game_phase to classify early/mid/late game.
- Implemented dynamic phase-based heuristic weights in combined_heuristic:
  - Early: expansion weight = 0.4, robber = -0.2, dev = 0.2
  - Mid/Late: expansion = 0.2, robber = -0.4, dev = 0.6
- Updated move ordering (_action_priority) to aggressively prioritize high-value actions:
  - BUILD_CITY: 150.0
  - PLAY_DEV_CARD: 140.0
  - PLAY_KNIGHT_CARD: 140.0
  - BUILD_SETTLEMENT: 130.0
  - BUILD_ROAD: 100.0
  - END_TURN: -200.0
- Kept and reinforced search optimizations:
  - Iterative deepening up to 5 plies (or self.max_depth if lower)
  - Global node budget enforced (default 15000)
  - Use of list_prunned_actions and prune_robber_actions
  - Caching (transposition table) keyed by repr(game.state) fallback
- Specialized heuristics maintained:
  - expansion_heuristic using value_production
  - robber_heuristic measuring max opponent production
  - dev_card_heuristic returning weighted counts for MONOPOLY and ROAD_BUILDING
- Numerous defensive try/except blocks and informative debug prints to trace decisions and budget usage.

Notes / caveats:
- The code strictly uses adapters.py for game interactions as required.
- Cache keys use repr(g.state); if there's a canonical game hash available, replacing _state_key accordingly will improve cache performance.
- Heuristic access is defensive; if dev card structures differ, dev_card_heuristic falls back to 0.
- If runtime remains high, consider adding time-based cutoffs or more aggressive action pruning.

What I need next (for further improvements):
- Confirmation of a canonical game-state hash method (e.g., game.state_hash()) to improve caching.
- If desired, further tuning of phase thresholds (turn counts for early/mid/late) and exact heuristic weights.
- If opponent behavior is known (e.g., AlphaBeta uses base_fn), consider modeling opponent valuation using contender_fn for MIN nodes.

END CODER
================================ Human Message =================================

CODER

I updated foo_player.py with the Strategizer's recommendations and wrote the new file.

Summary of changes made:
- Added game phase detection via _get_game_phase to classify early/mid/late game.
- Implemented dynamic phase-based heuristic weights in combined_heuristic:
  - Early: expansion weight = 0.4, robber = -0.2, dev = 0.2
  - Mid/Late: expansion = 0.2, robber = -0.4, dev = 0.6
- Updated move ordering (_action_priority) to aggressively prioritize high-value actions:
  - BUILD_CITY: 150.0
  - PLAY_DEV_CARD: 140.0
  - PLAY_KNIGHT_CARD: 140.0
  - BUILD_SETTLEMENT: 130.0
  - BUILD_ROAD: 100.0
  - END_TURN: -200.0
- Kept and reinforced search optimizations:
  - Iterative deepening up to 5 plies (or self.max_depth if lower)
  - Global node budget enforced (default 15000)
  - Use of list_prunned_actions and prune_robber_actions
  - Caching (transposition table) keyed by repr(game.state) fallback
- Specialized heuristics maintained:
  - expansion_heuristic using value_production
  - robber_heuristic measuring max opponent production
  - dev_card_heuristic returning weighted counts for MONOPOLY and ROAD_BUILDING
- Numerous defensive try/except blocks and informative debug prints to trace decisions and budget usage.

Notes / caveats:
- The code strictly uses adapters.py for game interactions as required.
- Cache keys use repr(g.state); if there's a canonical game hash available, replacing _state_key accordingly will improve cache performance.
- Heuristic access is defensive; if dev card structures differ, dev_card_heuristic falls back to 0.
- If runtime remains high, consider adding time-based cutoffs or more aggressive action pruning.

What I need next (for further improvements):
- Confirmation of a canonical game-state hash method (e.g., game.state_hash()) to improve caching.
- If desired, further tuning of phase thresholds (turn counts for early/mid/late) and exact heuristic weights.
- If opponent behavior is known (e.g., AlphaBeta uses base_fn), consider modeling opponent valuation using contender_fn for MIN nodes.

END CODER
================================ Human Message =================================

GAME RESULTS:

{
  "Player Summary": {
    "AlphaBetaPlayer:RED(depth=2,value_fn=base_fn,prunning=False)": {
      "WINS": 23,
      "AVG VP": 8.966666666666667,
      "AVG SETTLES": 2.8333333333333335,
      "AVG CITIES": 2.2333333333333334,
      "AVG ROAD": 0.8333333333333334,
      "AVG ARMY": 0.0,
      "AVG DEV VP": 0.0
    },
    "FooPlayer:BLUE": {
      "WINS": 7,
      "AVG VP": 6.233333333333333,
      "AVG SETTLES": 2.1333333333333333,
      "AVG CITIES": 1.5666666666666667,
      "AVG ROAD": 0.16666666666666666,
      "AVG ARMY": 0.16666666666666666,
      "AVG DEV VP": 0.3
    }
  },
  "Game Summary": {
    "AVG TICKS": 206.0,
    "AVG TURNS": 70.53333333333333,
    "AVG DURATION": 66.49721171061198
  }
}
================================ Human Message =================================

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

---
### **PERFORMANCE SUMMARY**
- **Outcome**: **Poor** (7 wins, 23 losses).
- **VP Comparison**: FooPlayer:BLUE averaged **6.23 VP** vs. AlphaBetaPlayer:RED's **8.97 VP** (VP diff: **-2.74**).
- **Key Counts**:
  - Settlements: 2.13 (FooPlayer) vs. 2.83 (Opponent)
  - Cities: 1.57 (FooPlayer) vs. 2.23 (Opponent)
  - Roads: 0.17 (FooPlayer) vs. 0.83 (Opponent)
  - Dev Cards: 0.30 (FooPlayer) vs. 0.00 (Opponent)
  - Total Turns: 70.53

---
### **VERDICT**
**Poor**: The player is significantly underperforming, with a VP deficit of -2.74 and only 23% win rate.

---
### **LIKELY REASONS**
1. **Ineffective Heuristic Weighting and Phase Handling**:
   - The **combined heuristic** (`base_val + exp_v + rob_v + dev_v`) uses **phase-specific weights** (e.g., `exp_w = 0.4`, `rob_w = -0.2`, `dev_w = 0.2` for early game), but these weights are too modest to drive meaningful strategic decisions.
   - **Code Citation**: Lines 175–185, `foo_player.py`.
   - **Log Corroboration**:
     - The player frequently defaults to `END_TURN` or `ROLL` actions, even when higher-value actions (e.g., `BUILD_ROAD`, `BUILD_CITY`) are available.
     - Example: "FooPlayer: Chosen action = Action(color=<Color.BLUE: 'BLUE'>, action_type=<ActionType.END_TURN: 'END_TURN'>, value=None) with value 1199999973708469.5".

2. **Over-Reliance on `END_TURN` and `ROLL` Actions**:
   - The player frequently selects `END_TURN` or `ROLL` actions, even when strategic actions like `BUILD_ROAD`, `BUILD_CITY`, or `PLAY_KNIGHT_CARD` are available and have higher expected values.
   - **Log Corroboration**:
     - "FooPlayer: Depth 5 Top-level Action #0 = Action(color=<Color.BLUE: 'BLUE'>, action_type=<ActionType.END_TURN: 'END_TURN'>, value=None) => expected value 1199999950177407.8".
     - "FooPlayer: Chosen action = Action(color=<Color.BLUE: 'BLUE'>, action_type=<ActionType.ROLL: 'ROLL'>, value=None) with value 1199999952997721.8".

3. **Limited Robber/Knight Strategy**:
   - While robber actions are pruned for efficiency, the player lacks a **targeted strategy** to disrupt the opponent or optimize resource denial.
   - **Log Corroboration**:
     - "FooPlayer: Depth 5 Top-level Action #0 = Action(color=<Color.BLUE: 'BLUE'>, action_type=<ActionType.MOVE_ROBBER: 'MOVE_ROBBER'>, value=((-1, -1, 2), None, None)) => expected value 1200000088909241.5".
     - The player chooses robber actions based on generic expected values, without a clear strategic focus.

4. **No Adaptive Development Card Strategy**:
   - The player evaluates development card actions but does not prioritize or strategically use them (e.g., "MONOPOLY", "ROAD_BUILDING").
   - **Log Corroboration**:
     - "FooPlayer: Depth 5 Top-level Action #0 = Action(color=<Color.BLUE: 'BLUE'>, action_type=<ActionType.BUY_DEVELOPMENT_CARD: 'BUY_DEVELOPMENT_CARD'>, value=None) => expected value 2700000047076590.0".
     - Development card actions are considered but lack strategic prioritization.

---
### **STRATEGIC FLAW**
The player's **heuristic weighting** and **move ordering** are insufficient to prioritize high-value actions like `BUILD_CITY`, `PLAY_KNIGHT_CARD`, or development card usage, leading to suboptimal decisions and missed opportunities.

---
### **NEXT STEP**
Send to **Strategizer** to **redesign heuristic weights** and **refine move ordering** to prioritize strategic actions like city upgrades, knight plays, and development card usage, while **reducing reliance on `END_TURN` and `ROLL` actions**.

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

- META THOUGHTS:
The FooPlayer's performance has consistently declined or stagnated, with the latest iteration achieving only 7 wins out of 30 games. The root cause appears to be a **fundamental issue with the strategic framework** rather than just heuristic weights or move ordering. Incremental adjustments have not yielded meaningful improvements, suggesting the need for a **radical shift in strategy**.

Key insights from reflection:
1. **Current Approach Limitations**: The Expectimax search with weighted heuristics is not capturing the nuances of strategic play in Catan, particularly in road networks, city upgrades, and development card usage.
2. **Alternative Strategies**: A **Monte Carlo Tree Search (MCTS)** approach could be more adaptive and better suited to handle the complexity of Catan's decision space.
3. **Enhanced Heuristic Design**: A more sophisticated heuristic is needed, one that directly evaluates the strategic value of actions (e.g., resource monopolies, expansion potential) rather than relying on generic weighted sums.
4. **Opponent Modeling**: Introducing a model of the opponent's strategy could improve decision-making by anticipating and countering their moves.

Given these insights, the next step is to consult the **Strategizer** to explore a **fundamental shift in strategy**, such as adopting MCTS or redesigning the heuristic framework to focus on high-level strategic goals.

- META GOAL:
Explore a **fundamental shift in strategy**, such as adopting **Monte Carlo Tree Search (MCTS)** or redesigning the heuristic framework to focus on high-level strategic goals like road networks, city upgrades, and development card usage.

- CHOSEN AGENT: STRATEGIZER

- AGENT OBJECTIVE:
Propose a **radical shift in strategy** to address the persistent performance issues. Specifically:
1. **Alternative Search Strategy**:
   - Evaluate the feasibility of switching to **Monte Carlo Tree Search (MCTS)**. Provide a high-level design for how MCTS could be implemented in this context, including:
     - **Selection Policy**: How to balance exploration vs. exploitation.
     - **Simulation Policy**: How to simulate random playouts or use heuristics for evaluation.
     - **Backpropagation**: How to update node values based on simulation outcomes.
     - **Integration with Adapters**: How to use `adapters.py` functions (e.g., `copy_game`, `execute_spectrum`) within the MCTS framework.
2. **Enhanced Heuristic Design**:
   - If continuing with Expectimax, propose a **completely new heuristic design** that directly evaluates strategic actions (e.g., road networks, settlement placement, resource control) rather than relying on weighted sums of generic metrics.
3. **Opponent Modeling**:
   - Suggest ways to model the opponent's strategy to better anticipate and counter their moves.
4. **Action Pruning**:
   - Propose a method to aggressively prune low-value actions (e.g., `END_TURN`, `ROLL`) unless they are the only options available.

Provide a **clear, actionable plan** for the Coder to implement, focusing on algorithmic solutions using `adapters.py` functions. Avoid simple rule-based heuristics.
================================ Human Message =================================

STRATEGIZER
---
### **Analysis of Current State**
The performance history shows a **consistent decline** from **Evolution 1 (14 wins)** to **Evolution 6 (7 wins)**. The current approach, using **Expectimax with weighted heuristics**, has failed to improve results despite incremental adjustments. The **root cause** is a **fundamental mismatch** between the strategy and the complexity of Catan's decision space.

The **Evolution 1** implementation (1-ply lookahead) was the most successful, suggesting that **simplicity and directness** may be more effective than deeper, heuristic-driven searches.

---
### **Proposed Strategy: Radical Shift to Monte Carlo Tree Search (MCTS)**
To address the persistent performance issues, we propose a **shift to Monte Carlo Tree Search (MCTS)**. MCTS is well-suited for games with high branching factors and probabilistic outcomes, such as Catan. Below is a **high-level design** for implementing MCTS in `foo_player.py`:

---

### **Key Components of MCTS Implementation**
1. **Selection Policy**:
   - Use **Upper Confidence Bound (UCB1)** to balance exploration and exploitation.
   - Formula: \( UCB1 = \frac{w_i}{n_i} + c \sqrt{\frac{\ln N}{n_i}} \), where:
     - \( w_i \): Total reward of node \( i \).
     - \( n_i \): Number of visits to node \( i \).
     - \( N \): Total visits to the parent node.
     - \( c \): Exploration constant (e.g., \( \sqrt{2} \)).

2. **Simulation Policy**:
   - Simulate random playouts from leaf nodes using `execute_spectrum` to handle probabilistic outcomes (e.g., dice rolls, robber placement).
   - Use `base_fn` or a **custom heuristic** to evaluate terminal states.

3. **Backpropagation**:
   - Update node values and visit counts based on simulation outcomes.

4. **Integration with Adapters**:
   - Use `copy_game` to create independent game copies for simulations.
   - Use `execute_spectrum` to simulate probabilistic actions.
   - Use `list_prunned_actions` and `prune_robber_actions` to reduce the search space.

5. **Opponent Modeling**:
   - Assume the opponent will act to maximize their own reward, and use this to guide the simulation policy.

6. **Action Pruning**:
   - Aggressively prune low-value actions (e.g., `END_TURN`, `ROLL`) unless they are the only options.

---

### **Implementation Plan**
#### **Step 1: Define MCTS Node Structure**
- Create a class `MCTSNode` to represent nodes in the search tree:
  ```python
  class MCTSNode:
      def __init__(self, game: Game, parent=None, action=None):
          self.game = game
          self.parent = parent
          self.action = action
          self.children = []
          self.visits = 0
          self.total_reward = 0.0
  ```

#### **Step 2: Implement UCB1 Selection**
- Add a method to select the best child node using UCB1:
  ```python
  def select_best_child(self, node: MCTSNode, exploration_weight=math.sqrt(2)) -> MCTSNode:
      log_parent_visits = math.log(node.visits)
      def ucb_score(child):
          exploitation = child.total_reward / child.visits
          exploration = exploration_weight * math.sqrt(log_parent_visits / child.visits)
          return exploitation + exploration
      return max(node.children, key=ucb_score)
  ```

#### **Step 3: Implement Simulation and Backpropagation**
- Simulate random playouts from leaf nodes:
  ```python
  def simulate(self, node: MCTSNode) -> float:
      game_copy = copy_game(node.game)
      while not game_copy.is_terminal():
          actions = list_prunned_actions(game_copy)
          if not actions:
              break
          action = random.choice(actions)
          outcomes = execute_spectrum(game_copy, action)
          if not outcomes:
              break
          game_copy, _ = random.choice(outcomes)
      return base_fn()(game_copy, self.color)
  ```
- Backpropagate the result:
  ```python
  def backpropagate(self, node: MCTSNode, reward: float):
      while node is not None:
          node.visits += 1
          node.total_reward += reward
          node = node.parent
  ```

#### **Step 4: Integrate MCTS into `decide` Method**
- Replace the current `decide` method with MCTS:
  ```python
  def decide(self, game: Game, playable_actions):
      root = MCTSNode(game)
      for _ in range(1000):  # Number of iterations
          node = self._select(root)
          reward = self.simulate(node)
          self.backpropagate(node, reward)
      best_child = max(root.children, key=lambda child: child.visits)
      return best_child.action
  ```

#### **Step 5: Optimize and Test**
- Test the MCTS implementation against the AlphaBeta opponent.
- Adjust the number of iterations and exploration constant as needed.

---
### **Actionable Steps for the Coder**
1. **Implement MCTSNode Class**:
   - Define the `MCTSNode` class to represent nodes in the search tree.
2. **Implement UCB1 Selection**:
   - Add the `select_best_child` method to balance exploration and exploitation.
3. **Implement Simulation and Backpropagation**:
   - Add the `simulate` and `backpropagate` methods to handle playouts and updates.
4. **Integrate MCTS into `decide`**:
   - Replace the current `decide` method with the MCTS logic.
5. **Test and Validate**:
   - Test the updated player and monitor performance.

---
### **Expected Outcome**
This shift to MCTS will enable the player to **adaptively explore high-value actions** while accounting for uncertainty, leading to improved decision-making and higher win rates.

END STRATEGIZER
================================== Ai Message ==================================

- META THOUGHTS:
The Strategizer has proposed a **radical shift to Monte Carlo Tree Search (MCTS)**, which is well-suited for handling the complexity and probabilistic nature of Catan. This approach addresses the persistent performance issues by introducing a more adaptive and exploratory strategy. The proposed MCTS implementation includes:
- **UCB1 for balancing exploration and exploitation**.
- **Random playouts for simulation**, leveraging `execute_spectrum` to handle probabilistic outcomes.
- **Backpropagation to update node values** based on simulation results.
- **Integration with adapters** to ensure compatibility with the existing framework.

This strategy has the potential to significantly improve decision-making by focusing on high-value actions and adapting to the game's dynamics.

- META GOAL:
Implement a **Monte Carlo Tree Search (MCTS)** strategy to replace the current Expectimax approach, focusing on adaptive exploration and exploitation to consistently outperform the AlphaBeta opponent.

- CHOSEN AGENT: CODER

- AGENT OBJECTIVE:
Implement the **MCTS strategy** as proposed by the Strategizer. Specifically:
1. **Define MCTS Node Structure**:
   - Create a class `MCTSNode` to represent nodes in the search tree, including attributes for game state, parent, action, children, visits, and total reward.
2. **Implement UCB1 Selection**:
   - Add a method `select_best_child` to balance exploration and exploitation using the UCB1 formula.
3. **Implement Simulation and Backpropagation**:
   - Add a `simulate` method to perform random playouts from leaf nodes using `execute_spectrum`.
   - Add a `backpropagate` method to update node values and visit counts based on simulation outcomes.
4. **Integrate MCTS into `decide` Method**:
   - Replace the current `decide` method with MCTS logic, running for a fixed number of iterations (e.g., 1000).
5. **Optimize and Test**:
   - Test the MCTS implementation against the AlphaBeta opponent.
   - Adjust the number of iterations and exploration constant as needed for optimal performance.

Provide a clear and concise implementation that adheres to the proposed strategy. Include robust error handling and debugging prints for traceability.
================================ Human Message =================================

This is the old foo_player.py file
Now It is your turn to update it with the new recommendations from META

from .adapters import (
    Game,
    Player,
    Color,
    Action,
    ActionType,
    copy_game,
    execute_deterministic,
    execute_spectrum,
    expand_spectrum,
    list_prunned_actions,
    prune_robber_actions,
    base_fn,
    value_production,
    get_value_fn,
)

import math
import time
from typing import List, Tuple


class FooPlayer(Player):
    def __init__(self, name=None, max_depth: int = 5, node_budget: int = 15000):
        """
        FooPlayer uses iterative-deepening Expectimax with specialized heuristics
        and search optimizations (move ordering, robber pruning, caching).

        Args:
            name: Optional player name
            max_depth: Maximum plies to search (iterative deepening will grow to this)
            node_budget: Hard limit on number of recursive nodes to evaluate per decide() call
        """
        # Initialize with BLUE by default to preserve compatibility
        super().__init__(Color.BLUE, name)
        self.max_depth = max_depth
        self.node_budget = node_budget

    def _get_game_phase(self, game: Game) -> str:
        """
        Determine the game phase (early, mid, late) based on an available turn counter.
        This tries several common locations for a turn counter to be robust.
        """
        try:
            # Prefer a direct attribute if present
            turn_count = getattr(game, 'turn_count', None)
            if turn_count is None:
                # Try nested state structures
                st = getattr(game, 'state', None)
                if isinstance(st, dict):
                    turn_count = st.get('turn_count') or st.get('turn') or st.get('tick')
                else:
                    turn_count = getattr(st, 'turn_count', None) if st is not None else None

            if turn_count is None:
                # Fall back to 0 if not available
                turn_count = 0

            turn_count = int(turn_count)
        except Exception:
            turn_count = 0

        if turn_count < 20:
            return 'early'
        elif turn_count < 40:
            return 'mid'
        else:
            return 'late'

    def decide(self, game: Game, playable_actions):
        """
        Choose an action using iterative-deepening Expectimax with probabilistic simulation.

        This implementation follows the Strategizer's recommendations:
        - Dynamic heuristic weighting by game phase (early/mid/late)
        - Aggressive move ordering prioritizing high-value actions
        - Iterative deepening up to self.max_depth (capped at 5)
        - Global node budget enforced
        - Use of list_prunned_actions and prune_robber_actions to reduce branching
        """
        actions = list(playable_actions) if playable_actions is not None else []

        if len(actions) == 0:
            print('FooPlayer.decide: No playable actions available; returning None')
            return None

        # Cap maximum search depth to [1..5]
        MAX_DEPTH = max(1, min(5, self.max_depth))
        NODE_BUDGET = max(100, self.node_budget)

        # Primary base value function
        base_value_fn = base_fn()

        # Transposition cache: (state_repr, depth) -> value
        cache = {}

        # Node counter and timing
        node_count = 0
        start_time = time.time()

        # Helper to produce a reproducible cache key for a game state
        def _state_key(g: Game) -> str:
            try:
                return repr(g.state)
            except Exception:
                try:
                    return repr(g)
                except Exception:
                    return str(id(g))

        # Move ordering heuristic (higher = more promising)
        def _action_priority(act: Action) -> float:
            try:
                at = act.action_type
                # Aggressive priorities recommended by the Strategizer
                if at == ActionType.BUILD_CITY:
                    return 150.0
                if at == ActionType.PLAY_DEV_CARD:
                    return 140.0
                if at == ActionType.PLAY_KNIGHT_CARD:
                    # Prioritize knight plays to move robber / gain army
                    return 140.0
                if at == ActionType.BUILD_SETTLEMENT:
                    return 130.0
                if at == ActionType.BUILD_ROAD:
                    return 100.0
                if at == ActionType.BUY_DEV_CARD:
                    return 80.0
                if at == ActionType.MOVE_ROBBER:
                    return 70.0
                if at == ActionType.TRADE:
                    return 60.0
                if at == ActionType.ROLL:
                    return 50.0
                if at == ActionType.END_TURN:
                    # Strongly deprioritize ending the turn
                    return -200.0
            except Exception:
                pass
            return 0.0

        # Specialized heuristics as suggested by Strategizer.
        # Each returns a raw signal; combined_heuristic will apply the configured weights.

        def expansion_heuristic(g: Game, color: Color) -> float:
            """Estimate long-term expansion potential using value_production.
            Returns raw production signal (not weighted).
            """
            try:
                sample = getattr(g, 'state', g)
                player_name = getattr(self, 'name', 'P0')
                prod = value_production(sample, player_name, include_variety=True)
                return float(prod)
            except Exception as e:
                # Be conservative on failures
                # print(f'FooPlayer.expansion_heuristic failed: {e}')
                return 0.0

        def robber_heuristic(g: Game, color: Color) -> float:
            """Estimate impact of robber placement by measuring opponent production.
            Returns the maximum opponent production (raw), combined_heuristic will weight it negatively.
            """
            try:
                sample = getattr(g, 'state', g)
                max_opponent_prod = 0.0
                # Iterate over known colors and measure production; skip our color
                for opp in list(Color):
                    if opp == color:
                        continue
                    try:
                        opp_name = getattr(self, 'name', f'P{opp.value}')
                        p = value_production(sample, opp_name, include_variety=False)
                        max_opponent_prod = max(max_opponent_prod, float(p))
                    except Exception:
                        continue
                return float(max_opponent_prod)
            except Exception:
                return 0.0

        def dev_card_heuristic(g: Game, color: Color) -> float:
            """Prefer states where playing certain dev cards (MONOPOLY, ROAD_BUILDING)
            is likely to be impactful. This returns a raw signal (counts weighted) and
            will be scaled by the combined heuristic based on game phase.
            """
            try:
                sample = getattr(g, 'state', None)
                player_name = getattr(self, 'name', 'P0')
                if sample is None:
                    return 0.0

                # Defensive access to dev card counts
                devs = None
                if isinstance(sample, dict):
                    devs = sample.get('dev_cards') or sample.get('devs')
                else:
                    devs = getattr(g, 'dev_cards', None) or getattr(sample, 'dev_cards', None)

                if not devs:
                    return 0.0

                # Extract counts robustly
                count_mon = 0
                count_rb = 0
                try:
                    # devs might be structured as {player_name: {'MONOPOLY': n, ...}, ...}
                    count_mon = int(devs.get(player_name, {}).get('MONOPOLY', 0))
                    count_rb = int(devs.get(player_name, {}).get('ROAD_BUILDING', 0))
                except Exception:
                    try:
                        # Or keyed by color
                        count_mon = int(devs.get(color, {}).get('MONOPOLY', 0))
                        count_rb = int(devs.get(color, {}).get('ROAD_BUILDING', 0))
                    except Exception:
                        count_mon = 0
                        count_rb = 0

                # Return a raw combined signal: weight monopoly higher than road building
                return float(count_mon) * 1.0 + float(count_rb) * 0.8
            except Exception:
                return 0.0

        # Combined heuristic: base value + phase-weighted specialized heuristics
        def combined_heuristic(g: Game, color: Color) -> float:
            # Base evaluation
            try:
                base_val = base_value_fn(g, color)
            except Exception as e:
                print(f'FooPlayer.combined_heuristic: base_fn failed: {e}')
                base_val = -1e9

            # Determine game phase for dynamic weights
            phase = self._get_game_phase(g)

            # Phase-specific weights
            if phase == 'early':
                exp_w = 0.4
                rob_w = -0.2
                dev_w = 0.2
            else:  # mid or late
                exp_w = 0.2
                rob_w = -0.4
                dev_w = 0.6

            try:
                exp_v = exp_w * expansion_heuristic(g, color)
                rob_v = rob_w * robber_heuristic(g, color)
                dev_v = dev_w * dev_card_heuristic(g, color)
            except Exception as e:
                print(f'FooPlayer.combined_heuristic: specialized heuristic failed: {e}')
                exp_v = rob_v = dev_v = 0.0

            # Debug print of heuristic components for traceability
            # (Keep prints moderate to avoid flooding)
            # print(f'FooPlayer.heuristic: phase={phase}, base={base_val:.2f}, exp={exp_v:.2f}, rob={rob_v:.2f}, dev={dev_v:.2f}')

            return base_val + exp_v + rob_v + dev_v

        # Expectimax with caching and node budget. Uses combined_heuristic at leaves.
        def expectimax(node_game: Game, depth: int) -> float:
            nonlocal node_count

            # Enforce node budget (global across iterative deepening)
            node_count += 1
            if node_count > NODE_BUDGET:
                # Return a heuristic estimate if we're out of budget
                return combined_heuristic(node_game, self.color)

            key = (_state_key(node_game), depth)
            if key in cache:
                return cache[key]

            # Terminal check (winning_color) if available
            try:
                winner = None
                try:
                    winner = node_game.winning_color()
                except Exception:
                    winner = None
                if winner is not None:
                    val = combined_heuristic(node_game, self.color)
                    cache[key] = val
                    return val
            except Exception as e:
                print(f'FooPlayer.expectimax: winner check failed: {e}')

            # Depth cutoff
            if depth == 0:
                val = combined_heuristic(node_game, self.color)
                cache[key] = val
                return val

            # Get pruned action list
            try:
                node_actions = list_prunned_actions(node_game)
            except Exception as e:
                print(f'FooPlayer.expectimax: list_prunned_actions failed: {e}')
                node_actions = []

            if not node_actions:
                val = combined_heuristic(node_game, self.color)
                cache[key] = val
                return val

            # If robber moves exist, prune them
            try:
                if any((getattr(a, 'action_type', None) == ActionType.MOVE_ROBBER) for a in node_actions):
                    node_actions = prune_robber_actions(self.color, node_game, node_actions)
            except Exception as e:
                print(f'FooPlayer.expectimax: prune_robber_actions failed: {e}')

            # Move ordering
            try:
                node_actions.sort(key=_action_priority, reverse=True)
            except Exception:
                pass

            # Determine node type: MAX if acting color == our color
            try:
                node_color = node_actions[0].color
                is_max = (node_color == self.color)
            except Exception:
                is_max = False

            if is_max:
                best_value = -math.inf
                for act in node_actions:
                    # Expand probabilistic outcomes
                    try:
                        outcomes = execute_spectrum(node_game, act)
                    except Exception:
                        try:
                            outcomes = execute_deterministic(node_game, act)
                        except Exception as e:
                            # Skip actions that cannot be executed
                            # print(f'FooPlayer.expectimax: action execution failed (max) for {act}: {e}')
                            continue

                    if not outcomes:
                        continue

                    expected = 0.0
                    for (g_after, prob) in outcomes:
                        try:
                            val = expectimax(g_after, depth - 1)
                        except Exception as e:
                            # On recursion error, give a conservative low value
                            # print(f'FooPlayer.expectimax: recursion error (max) for outcome: {e}')
                            val = -1e9
                        expected += prob * val

                    if expected > best_value:
                        best_value = expected

                    # Early stopping if node budget exhausted
                    if node_count > NODE_BUDGET:
                        break

                cache[key] = best_value
                return best_value
            else:
                # MIN node: model opponent as adversarial minimizing our heuristic
                worst_value = math.inf
                for act in node_actions:
                    try:
                        outcomes = execute_spectrum(node_game, act)
                    except Exception:
                        try:
                            outcomes = execute_deterministic(node_game, act)
                        except Exception as e:
                            # print(f'FooPlayer.expectimax: action execution failed (min) for {act}: {e}')
                            continue

                    if not outcomes:
                        continue

                    expected = 0.0
                    for (g_after, prob) in outcomes:
                        try:
                            val = expectimax(g_after, depth - 1)
                        except Exception as e:
                            # print(f'FooPlayer.expectimax: recursion error (min) for outcome: {e}')
                            val = 1e9
                        expected += prob * val

                    if expected < worst_value:
                        worst_value = expected

                    if node_count > NODE_BUDGET:
                        break

                cache[key] = worst_value
                return worst_value

        # Iterative deepening. Use a global node budget across all depths.
        best_action = None
        best_value = -math.inf
        depth_reached = 0

        # Determine current game phase for debugging and heuristic selection
        current_phase = self._get_game_phase(game)
        print(f'FooPlayer.decide: starting iterative deepening (phase={current_phase}, max_depth={MAX_DEPTH}, node_budget={NODE_BUDGET})')

        for depth in range(1, MAX_DEPTH + 1):
            print(f'FooPlayer.decide: Iterative deepening at depth {depth}')
            depth_reached = depth

            # Evaluate top-level actions in move-ordered sequence to get good bounds early
            ordered_actions = sorted(actions, key=_action_priority, reverse=True)

            for idx, action in enumerate(ordered_actions):
                if node_count > NODE_BUDGET:
                    print('FooPlayer.decide: Global node budget reached; stopping search')
                    break

                expected_value = -math.inf
                try:
                    game_copy = copy_game(game)
                    try:
                        outcomes = execute_spectrum(game_copy, action)
                    except Exception:
                        # Fallback to deterministic
                        try:
                            outcomes = execute_deterministic(game_copy, action)
                        except Exception as e:
                            print(f'FooPlayer.decide: execute_deterministic also failed for action {action}: {e}')
                            outcomes = []

                    if not outcomes:
                        expected_value = -math.inf
                    else:
                        expected_value = 0.0
                        for (g_after, prob) in outcomes:
                            try:
                                val = expectimax(g_after, depth - 1)
                            except Exception as e:
                                print(f'FooPlayer.decide: expectimax error on outcome of action {action}: {e}')
                                val = -1e9
                            expected_value += prob * val

                    print(f'FooPlayer: Depth {depth} Top-level Action #{idx} = {action} => expected value {expected_value}')

                except Exception as e:
                    print(f'FooPlayer: Exception while evaluating top-level action {action}: {e}')
                    expected_value = -math.inf

                # Update best action found so far (across depths)
                if expected_value > best_value:
                    best_value = expected_value
                    best_action = action

                # Respect global budget
                if node_count > NODE_BUDGET:
                    break

            # Stop deepening if budget exhausted
            if node_count > NODE_BUDGET:
                break

        if best_action is None and len(actions) > 0:
            print('FooPlayer: All evaluations failed or were skipped; defaulting to first action')
            best_action = actions[0]

        elapsed = time.time() - start_time
        print(f'FooPlayer: Chosen action = {best_action} with value {best_value} (depth reached {depth_reached}, nodes {node_count}, time {elapsed:.3f}s)')
        return best_action

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

This is the current adapters.py file that you must use to interact with the Catanatron API

"""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.
"""

from typing import Callable, List, Optional, Tuple, Dict, Any

# CORE RE-EXPORTS
from catanatron.game import Game  # Game instance with .state, .copy(), .execute(action), .winning_color()
from catanatron.models.player import Player, Color  # Player and Color types
from catanatron.models.enums import Action, ActionType  # Action = namedtuple("Action", ["color", "action_type", "value"]) 

# Player and debug node classes (re-exported so consumers can import them from adapters)
from catanatron_experimental.machine_learning.players.minimax import (
    AlphaBetaPlayer,  # Player that executes an AlphaBeta search with expected value calculation
    SameTurnAlphaBetaPlayer,  # AlphaBeta constrained to the same turn
    DebugStateNode,  # Node for debugging the AlphaBeta search tree
    DebugActionNode,  # Node representing an action in the AlphaBeta search tree
)
from catanatron_experimental.machine_learning.players.value import (
    ValueFunctionPlayer,  # Player using heuristic value functions
    DEFAULT_WEIGHTS,  # Default weight set for value functions
)

# Underlying implementation imports (underscore aliases to avoid recursion)
from catanatron_experimental.machine_learning.players.tree_search_utils import (
    execute_deterministic as _execute_deterministic,
    execute_spectrum as _execute_spectrum,
    expand_spectrum as _expand_spectrum,
    list_prunned_actions as _list_prunned_actions,  # spelling verified in source
    prune_robber_actions as _prune_robber_actions,
)
from catanatron_experimental.machine_learning.players.minimax import render_debug_tree as _render_debug_tree

from catanatron_experimental.machine_learning.players.value import (
    base_fn as _base_fn,
    contender_fn as _contender_fn,
    value_production as _value_production,
    get_value_fn as _get_value_fn,
)

# Public API
__all__ = [
    "Game",
    "Player",
    "Color",
    "Action",
    "ActionType",
    "AlphaBetaPlayer",
    "SameTurnAlphaBetaPlayer",
    "ValueFunctionPlayer",
    "DebugStateNode",
    "DebugActionNode",
    "copy_game",
    "execute_deterministic",
    "execute_spectrum",
    "expand_spectrum",
    "list_prunned_actions",
    "prune_robber_actions",
    "render_debug_tree",
    "base_fn",
    "contender_fn",
    "value_production",
    "get_value_fn",
]

# THIN CONVENIENCE WRAPPERS
def copy_game(game: Game) -> Game:
    '''Create a deep copy of the game state.'''
    return game.copy()

def execute_deterministic(game: Game, action: Action) -> List[Tuple[Game, float]]:
    '''Execute a deterministic action and return the resulting game state with probability 1.'''
    return _execute_deterministic(game, action)

def execute_spectrum(game: Game, action: Action) -> List[Tuple[Game, float]]:
    '''Return a list of (game_copy, probability) tuples for all possible outcomes of an action.'''
    return _execute_spectrum(game, action)

def expand_spectrum(game: Game, actions: List[Action]) -> Dict[Action, List[Tuple[Game, float]]]:
    '''Expand a game state into all possible outcomes for a list of actions.'''
    return _expand_spectrum(game, actions)

def list_prunned_actions(game: Game) -> List[Action]:
    '''Returns a pruned list of actions to reduce the search space.'''
    return _list_prunned_actions(game)

def prune_robber_actions(current_color: Color, game: Game, actions: List[Action]) -> List[Action]:
    '''Prunes robber actions to keep only the most impactful ones.'''
    return _prune_robber_actions(current_color, game, actions)

def render_debug_tree(node: DebugStateNode) -> str:
    '''Renders the AlphaBeta search tree using Graphviz.'''
    return _render_debug_tree(node)

# HEURISTIC BUILDERS
def base_fn(params=DEFAULT_WEIGHTS) -> Callable[[Game, Color], float]:
    '''Base value function factory for evaluating game states.'''
    return _base_fn(params)

def contender_fn(params) -> Callable[[Game, Color], float]:
    '''Alternative value function factory with tuned weights.'''
    return _contender_fn(params)

def value_production(sample, player_name: str = "P0", include_variety: bool = True) -> float:
    '''Compute the production value of a player's state.'''
    return _value_production(sample, player_name, include_variety)

def get_value_fn(name: str, params, value_function=None) -> Callable[[Game, Color], float]:
    '''Factory that returns a value function by name and parameters.'''
    return _get_value_fn(name, params, value_function)


--- Output from CODER at 20250925_065544 ---
================================== Ai Message ==================================
Tool Calls:
  write_foo (call_63G1ckGP6UGWVQUTuhLyXMcN)
 Call ID: call_63G1ckGP6UGWVQUTuhLyXMcN
  Args:
    new_text: from .adapters import (
    Game,
    Player,
    Color,
    Action,
    ActionType,
    copy_game,
    execute_deterministic,
    execute_spectrum,
    expand_spectrum,
    list_prunned_actions,
    prune_robber_actions,
    base_fn,
    value_production,
    get_value_fn,
)

import math
import time
import random
from typing import List, Optional


class FooPlayer(Player):
    """
    FooPlayer implementing a Monte Carlo Tree Search (MCTS) player.

    This replaces the earlier Expectimax approach with an adaptive MCTS that:
    - Uses UCB1 for selection (exploration vs exploitation)
    - Samples probabilistic outcomes via execute_spectrum during expansion/simulation
    - Uses base_fn to evaluate leaf/terminal states
    - Uses adapters' pruning helpers (list_prunned_actions, prune_robber_actions)

    Notes:
    - All interactions with the game use the adapters surface as required.
    - Plenty of defensive try/except blocks and print() calls are included to help
      trace decisions and debug runtime behavior while evolving the player.
    """

    def __init__(self, name=None, iterations: int = 1000, node_budget: int = 15000, exploration_const: float = math.sqrt(2)):
        # Initialize player color and name
        super().__init__(Color.BLUE, name)
        # How many MCTS iterations to run per decision (subject to node_budget)
        self.iterations = iterations
        # Hard cap on number of node expansions / evaluations
        self.node_budget = max(100, int(node_budget))
        # Exploration constant for UCB1
        self.exploration_const = float(exploration_const)

    def _get_game_phase(self, game: Game) -> str:
        """Robust game phase detection (early/mid/late) using available turn counters.
        This mirrors previous logic to allow phase-adaptive behaviors in simulations
        if desired later. For now MCTS uses base_fn for evaluation.
        """
        try:
            turn_count = getattr(game, 'turn_count', None)
            if turn_count is None:
                st = getattr(game, 'state', None)
                if isinstance(st, dict):
                    turn_count = st.get('turn_count') or st.get('turn') or st.get('tick')
                else:
                    turn_count = getattr(st, 'turn_count', None) if st is not None else None

            if turn_count is None:
                turn_count = 0
            turn_count = int(turn_count)
        except Exception:
            turn_count = 0

        if turn_count < 20:
            return 'early'
        elif turn_count < 40:
            return 'mid'
        else:
            return 'late'

    class MCTSNode:
        """Node in the MCTS tree.

        Attributes:
            game: Game state at this node
            parent: parent node or None
            action: Action that led from parent -> this node (None for root)
            children: list of child nodes
            visits: number of times node visited
            total_reward: cumulative reward (for our player) accumulated
            untried_actions: actions available at this node that are not yet expanded
        """

        def __init__(self, game: Game, parent: Optional['FooPlayer.MCTSNode'] = None, action: Optional[Action] = None):
            self.game = game
            self.parent = parent
            self.action = action
            self.children: List['FooPlayer.MCTSNode'] = []
            self.visits: int = 0
            self.total_reward: float = 0.0
            self.untried_actions: Optional[List[Action]] = None

        def is_fully_expanded(self) -> bool:
            return self.untried_actions is not None and len(self.untried_actions) == 0

        def best_child_by_ucb(self, exploration_const: float) -> Optional['FooPlayer.MCTSNode']:
            """Select child with highest UCB1 score."""
            if not self.children:
                return None
            log_parent = math.log(max(1, self.visits))
            best = None
            best_score = -math.inf
            for c in self.children:
                if c.visits == 0:
                    # Encourage unvisited children
                    score = math.inf
                else:
                    exploitation = c.total_reward / c.visits
                    exploration = exploration_const * math.sqrt(log_parent / c.visits)
                    score = exploitation + exploration
                if score > best_score:
                    best_score = score
                    best = c
            return best

    def decide(self, game: Game, playable_actions):
        """Run MCTS and select the best action.

        High-level flow:
          - Create root node for current game
          - For up to self.iterations (bounded by node_budget):
              - Selection: traverse tree via UCB1 until a node with untried actions or terminal is found
              - Expansion: expand one untried action (sample an outcome) and add child
              - Simulation: simulate a random playout from child (sampling chance outcomes) until terminal or depth limit
              - Backpropagation: propagate reward (base_fn relative to self.color) up the tree
          - Choose the root child with max visits (robust) or max average reward as final action

        The implementation samples probabilistic outcomes using execute_spectrum and falls back to execute_deterministic.
        """
        actions = list(playable_actions) if playable_actions is not None else []

        if not actions:
            print('FooPlayer.decide: No playable actions; returning None')
            return None

        # Setup
        iterations = max(1, int(self.iterations))
        node_budget = max(100, int(self.node_budget))
        exploration_const = float(self.exploration_const)
        base_value_fn = base_fn()

        node_count = 0  # counts expansions / simulations roughly
        start_time = time.time()

        # Create root node
        root_game = copy_game(game)
        root = FooPlayer.MCTSNode(root_game)

        # Initialize root untried actions defensively using pruned list helper
        try:
            root.untried_actions = list_prunned_actions(root_game) or []
        except Exception:
            root.untried_actions = list(actions)

        # Helper: sample an outcome from execute_spectrum's outcomes list
        def _sample_outcome(outcomes):
            # outcomes: list of (game, prob)
            if not outcomes:
                return None
            if len(outcomes) == 1:
                return outcomes[0][0]
            # sample by probability
            r = random.random()
            cum = 0.0
            for (g, p) in outcomes:
                cum += float(p)
                if r <= cum:
                    return g
            # Fallback to last
            return outcomes[-1][0]

        # Helper: get legal/pruned actions at a node (defensive)
        def _legal_actions_for(g: Game):
            try:
                acts = list_prunned_actions(g) or []
            except Exception:
                # Exhaustive fallback: no pruning available, try expand_spectrum or empty
                try:
                    acts = []
                except Exception:
                    acts = []
            return list(acts)

        # Helper: select an action for simulation playouts (avoid END_TURN/ROLL when possible)
        def _simulation_policy(g: Game):
            acts = _legal_actions_for(g)
            if not acts:
                return None
            # try to filter out passive actions if there are alternatives
            non_passive = [a for a in acts if getattr(a, 'action_type', None) not in (ActionType.END_TURN, ActionType.ROLL)]
            if non_passive:
                return random.choice(non_passive)
            return random.choice(acts)

        # Terminal detection using winning_color if available
        def _is_terminal(g: Game) -> bool:
            try:
                w = g.winning_color()
                return w is not None
            except Exception:
                # No winning_color API? Fallback heuristics could be added; assume not terminal
                return False

        # Simulation: play random (but slightly biased) moves until terminal or depth limit
        def _simulate_from(g: Game, max_sim_depth: int = 50) -> float:
            nonlocal node_count
            sim_game = copy_game(g)
            depth = 0
            while depth < max_sim_depth and not _is_terminal(sim_game):
                act = _simulation_policy(sim_game)
                if act is None:
                    break
                # Execute (sample) an outcome for this action
                try:
                    outcomes = execute_spectrum(sim_game, act)
                except Exception:
                    try:
                        outcomes = execute_deterministic(sim_game, act)
                    except Exception:
                        outcomes = []
                if not outcomes:
                    break
                chosen_after = _sample_outcome(outcomes)
                if chosen_after is None:
                    break
                sim_game = chosen_after
                depth += 1
                node_count += 1
                if node_count > node_budget:
                    # stop simulation early if we reached budget
                    break
            # Evaluate final state for our player
            try:
                val = base_value_fn(sim_game, self.color)
            except Exception as e:
                print(f'FooPlayer._simulate_from: base_fn evaluation failed: {e}')
                val = -1e9
            return float(val)

        # Backpropagation updates node statistics with reward
        def _backpropagate(node: FooPlayer.MCTSNode, reward: float):
            while node is not None:
                node.visits += 1
                node.total_reward += reward
                node = node.parent

        # Expand one action from node: pick an untried action, sample outcome, create child
        def _expand(node: FooPlayer.MCTSNode) -> Optional[FooPlayer.MCTSNode]:
            nonlocal node_count
            if node.untried_actions is None:
                node.untried_actions = _legal_actions_for(node.game)
            if not node.untried_actions:
                return None
            # Pop one action to expand
            try:
                action = node.untried_actions.pop()
            except Exception:
                return None
            # Execute and sample an outcome to create a deterministic child state
            try:
                outcomes = execute_spectrum(node.game, action)
            except Exception:
                try:
                    outcomes = execute_deterministic(node.game, action)
                except Exception:
                    outcomes = []

            if not outcomes:
                return None

            g_after = _sample_outcome(outcomes)
            if g_after is None:
                return None

            child = FooPlayer.MCTSNode(copy_game(g_after), parent=node, action=action)
            # Initialize child's untried actions lazily
            child.untried_actions = None
            node.children.append(child)
            node_count += 1
            return child

        # Selection: traverse from root using UCB1 until a node with untried actions or terminal
        def _select(node: FooPlayer.MCTSNode) -> FooPlayer.MCTSNode:
            current = node
            while True:
                if _is_terminal(current.game):
                    return current
                # initialize untried_actions if needed
                if current.untried_actions is None:
                    current.untried_actions = _legal_actions_for(current.game)
                if current.untried_actions:
                    # node has untried actions -> stop at current (expandable)
                    return current
                # otherwise fully expanded: move to best child by UCB
                best = current.best_child_by_ucb(exploration_const)
                if best is None:
                    return current
                current = best

        # Main MCTS loop
        print(f'FooPlayer.decide: Starting MCTS with iterations={iterations}, node_budget={node_budget}')
        iters = 0
        try:
            for it in range(iterations):
                if node_count > node_budget:
                    print('FooPlayer.decide: node_budget reached; stopping iterations')
                    break
                iters += 1
                # 1. Selection
                leaf = _select(root)

                # 2. Expansion
                if not _is_terminal(leaf.game):
                    child = _expand(leaf)
                    if child is None:
                        # Could not expand (no outcomes); treat leaf as child for simulation
                        node_to_simulate = leaf
                    else:
                        node_to_simulate = child
                else:
                    node_to_simulate = leaf

                # 3. Simulation
                reward = _simulate_from(node_to_simulate.game)

                # 4. Backpropagation
                _backpropagate(node_to_simulate, reward)

            # Completed iterations or budget
        except Exception as e:
            print(f'FooPlayer.decide: Exception during MCTS main loop: {e}')

        # Choose the best action: child of root with highest visit count (robust) or highest avg reward
        best_child = None
        best_visits = -1
        best_avg = -math.inf
        for c in root.children:
            avg = (c.total_reward / c.visits) if c.visits > 0 else -math.inf
            # prefer visits first
            if c.visits > best_visits or (c.visits == best_visits and avg > best_avg):
                best_child = c
                best_visits = c.visits
                best_avg = avg

        chosen_action = None
        if best_child is not None:
            chosen_action = best_child.action
        else:
            # Fallback: choose highest-priority playable action
            try:
                actions_sorted = sorted(actions, key=lambda a: 0 if getattr(a, 'action_type', None) not in (ActionType.END_TURN, ActionType.ROLL) else -1)
                chosen_action = actions_sorted[0]
            except Exception:
                chosen_action = actions[0]

        elapsed = time.time() - start_time
        print(f'FooPlayer.decide: MCTS finished iterations={iters}, node_count={node_count}, time={elapsed:.3f}s')
        print(f'FooPlayer.decide: Chosen action = {chosen_action} (visits={best_visits}, avg={best_avg:.3f})')

        return chosen_action
================================= Tool Message =================================
Name: write_foo

foo_player.py updated successfully
================================== Ai Message ==================================

CODER

I replaced the previous FooPlayer implementation with a new MCTS-based player in foo_player.py.

Summary of changes:
- Implemented Monte Carlo Tree Search (MCTS) to replace Expectimax.
  - MCTSNode class with visits, total_reward, children, and untried_actions.
  - UCB1 selection (best_child_by_ucb) with exploration constant.
  - Selection (_select) traverses using UCB1 until expandable node found.
  - Expansion (_expand) pops an untried action, uses execute_spectrum (fallback execute_deterministic), samples an outcome to create a child.
  - Simulation (_simulate_from) runs randomized playouts using execute_spectrum (fallback execute_deterministic) and base_fn to evaluate terminal/leaf states.
  - Backpropagation (_backpropagate) updates visits and total_reward back to root.
- Integrations and adapters:
  - Uses only functions from .adapters: copy_game, execute_spectrum, execute_deterministic, list_prunned_actions, prune_robber_actions, base_fn, value_production.
- Defensive coding:
  - Many try/except blocks to avoid crashes when adapters or game structures are unexpected.
  - Prints added for debugging: iteration start, node budget reached, exceptions, final chosen action, and statistics.
- Parameters:
  - Constructor accepts iterations (default 1000), node_budget (default 15000), and exploration_const (default sqrt(2)).
  - Node budget enforced during expansion and simulation.
- Fallback behaviour:
  - If MCTS finds no children, selects a non-passive action if possible, else the first action.

Report of functions created/modified:
- Created MCTSNode inner class with methods: is_fully_expanded, best_child_by_ucb.
- Implemented helpers: _sample_outcome, _legal_actions_for, _simulation_policy, _is_terminal, _simulate_from, _backpropagate, _expand, _select.
- Rewrote decide to run MCTS loop and return chosen action.

Debugging and instrumentation:
- Print statements to show progress, node budget exhaustion, and final decision metrics (iterations run, node_count, chosen action visits/average).

Notes and next steps (for future objectives):
- Tuning parameters: iterations, exploration_const, and node_budget will be critical. Start with iterations=1000 and node_budget=15000, then tune if too slow or ineffective.
- Consider adding progressive bias in selection or heuristic-guided rollouts to improve playout quality (use combined heuristics from earlier versions).
- Add caching/transposition table keyed by canonical game state hash if available (replace repr(game.state) approach).
- Optionally incorporate opponent modeling by adjusting reward calculations to reflect opponent-centric evaluations (contender_fn).
- If performance issues persist, consider hybrid approach: MCTS for high-level decisions and Expectimax for low-branching subtrees.

If you want, I can:
- Add heuristic-guided rollouts (biased simulation policy) using the previously implemented specialized heuristics.
- Implement a time-based cutoff (wall-clock) instead of/alongside node_budget or iterations.
- Expose the ability to use contender_fn or different base functions.

END CODER

