--- Input for META at 20250925_053217 ---
================================ Human Message =================================

GAME RESULTS:

{
  "Player Summary": {
    "AlphaBetaPlayer:RED(depth=2,value_fn=base_fn,prunning=False)": {
      "WINS": 30,
      "AVG VP": 10.0,
      "AVG SETTLES": 2.8333333333333335,
      "AVG CITIES": 2.566666666666667,
      "AVG ROAD": 1.0,
      "AVG ARMY": 0.0,
      "AVG DEV VP": 0.03333333333333333
    },
    "FooPlayer:BLUE": {
      "WINS": 0,
      "AVG VP": 2.0,
      "AVG SETTLES": 2.0,
      "AVG CITIES": 0.0,
      "AVG ROAD": 0.0,
      "AVG ARMY": 0.0,
      "AVG DEV VP": 0.0
    }
  },
  "Game Summary": {
    "AVG TICKS": 162.46666666666667,
    "AVG TURNS": 59.8,
    "AVG DURATION": 1.1284445206324258
  }
}
================================ Human 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:BLUE averaged **2.0 VP** vs. AlphaBetaPlayer:RED's **10.0 VP** (VP diff: **-8.0**).
- **Key Counts**:
  - Settlements: 2.0 (FooPlayer) vs. 2.83 (Opponent)
  - Cities: 0.0 (FooPlayer) vs. 2.57 (Opponent)
  - Roads: 0.0 (FooPlayer) vs. 1.0 (Opponent)
  - Dev Cards: 0.0 (FooPlayer) vs. 0.03 (Opponent)
  - Total Turns: 59.8

---
### **VERDICT**
**Poor**: The player lost all games and scored significantly lower than the opponent.

---
### **LIKELY REASONS**
1. **No Strategic Decision-Making**:
   - The player always selects the first action in `playable_actions` without evaluating its potential outcomes or long-term rewards.
   - **Code Citation**: `return playable_actions[0]` (Line 25, `foo_player.py`).
   - **Log Corroboration**: Repeated "Choosing First Action on Default" in `game_output.txt`.

2. **Lack of Game State Evaluation**:
   - No use of `adapters.py` utilities (e.g., `copy_game`, `execute_deterministic`, `base_fn`) to simulate or evaluate actions.
   - **Code Citation**: No calls to `copy_game` or value functions in `foo_player.py`.

3. **No Lookahead or Chance Handling**:
   - The player does not simulate future game states or account for probabilistic outcomes (e.g., dice rolls, dev cards, robber placement).

4. **No Placement or Development Strategy**:
   - The player does not prioritize building settlements, cities, or roads, nor does it handle development cards or the robber.

---
### **STRATEGIC FLAW**
The player's logic is entirely reactive and naive, always choosing the first available action without any evaluation of its impact on the game state or long-term strategy.

---
### **NEXT STEP**
Send to **Coder** to implement a **1-ply value lookahead** using `copy_game` and `base_fn` to evaluate actions strategically.

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

GAME RESULTS:

{
  "Player Summary": {
    "AlphaBetaPlayer:RED(depth=2,value_fn=base_fn,prunning=False)": {
      "WINS": 16,
      "AVG VP": 8.1,
      "AVG SETTLES": 2.6666666666666665,
      "AVG CITIES": 2.1,
      "AVG ROAD": 0.6,
      "AVG ARMY": 0.0,
      "AVG DEV VP": 0.03333333333333333
    },
    "FooPlayer:BLUE": {
      "WINS": 14,
      "AVG VP": 8.233333333333333,
      "AVG SETTLES": 2.7666666666666666,
      "AVG CITIES": 2.1333333333333333,
      "AVG ROAD": 0.4,
      "AVG ARMY": 0.06666666666666667,
      "AVG DEV VP": 0.26666666666666666
    }
  },
  "Game Summary": {
    "AVG TICKS": 217.8,
    "AVG TURNS": 75.03333333333333,
    "AVG DURATION": 1.7297517776489257
  }
}
================================ Human Message =================================

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

---
### **PERFORMANCE SUMMARY**
- **Outcome**: **Borderline** (14 wins, 16 losses).
- **VP Comparison**: FooPlayer:BLUE averaged **8.23 VP** vs. AlphaBetaPlayer:RED's **8.1 VP** (VP diff: **+0.13**).
- **Key Counts**:
  - Settlements: 2.77 (FooPlayer) vs. 2.67 (Opponent)
  - Cities: 2.13 (FooPlayer) vs. 2.1 (Opponent)
  - Roads: 0.4 (FooPlayer) vs. 0.6 (Opponent)
  - Dev Cards: 0.27 (FooPlayer) vs. 0.03 (Opponent)
  - Total Turns: 75.03

---
### **VERDICT**
**Borderline**: The player is competitive but slightly underperforms the opponent in wins and VP.

---
### **LIKELY REASONS**
1. **Limited Lookahead Depth**:
   - The player uses a **1-ply expected-value lookahead**, which evaluates immediate outcomes but lacks deeper strategic planning.
   - **Code Citation**: `outcomes = execute_deterministic(game_copy, action)` (Line 50, `foo_player.py`).
   - **Log Corroboration**: Actions are chosen based on immediate expected value (e.g., "FooPlayer: Chosen action = Action(color=<Color.BLUE: 'BLUE'>, action_type=<ActionType.BUILD_CITY: 'BUILD_CITY'>, value=5) with value 900000100019167.0").

2. **No Chance Handling for Probabilistic Actions**:
   - The player does not simulate or evaluate probabilistic outcomes (e.g., dice rolls, dev cards, robber placement).
   - **Code Citation**: Uses `execute_deterministic` instead of `execute_spectrum` for actions with inherent randomness.
   - **Log Corroboration**: No evidence of probabilistic evaluation in logs (e.g., no "execute_spectrum" calls).

3. **No Robber/Knight Strategy**:
   - The player does not prioritize or optimize robber placement or knight usage, which are critical for disrupting opponents.
   - **Log Corroboration**: Robber actions are evaluated but lack strategic depth (e.g., "FooPlayer: Action #0 = Action(color=<Color.BLUE: 'BLUE'>, action_type=<ActionType.MOVE_ROBBER: 'MOVE_ROBBER'>, value=((0, 0, 0), <Color.RED: 'RED'>, None)) => expected value 899999944462766.1").

4. **No End-Turn Optimization**:
   - The player often defaults to ending turns without evaluating alternative actions (e.g., trades, development cards).
   - **Log Corroboration**: Frequent "FooPlayer: Chosen action = Action(color=<Color.BLUE: 'BLUE'>, action_type=<ActionType.END_TURN: 'END_TURN'>, value=None)".

---
### **STRATEGIC FLAW**
The player's **1-ply lookahead** and **lack of probabilistic simulation** limit its ability to plan strategically, especially for actions like dice rolls, robber placement, and development card usage.

---
### **NEXT STEP**
Send to **Coder** to implement **multi-ply lookahead** and **probabilistic simulation** using `execute_spectrum` for actions like dice rolls and robber placement.

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

- META THOUGHTS:
The 1-ply lookahead implementation has significantly improved the FooPlayer's performance, achieving a near-even win rate (14 wins, 16 losses) and a slightly higher average VP than the AlphaBeta opponent. However, the current strategy is still limited by its shallow depth and lack of probabilistic handling for actions like dice rolls, robber placement, and development cards. The Analyzer has identified these gaps and proposed a multi-ply lookahead with probabilistic simulation as the next logical step. This will allow the player to evaluate deeper strategic outcomes and account for uncertainty, which is critical for outmaneuvering the AlphaBeta opponent.

- META GOAL:
Continue improving the `foo_player.py` code to develop a robust, strategic algorithm that can consistently outperform the AlphaBeta opponent. The immediate focus is on implementing a **multi-ply lookahead** with **probabilistic simulation** for actions involving chance.

- CHOSEN AGENT: STRATEGIZER

- AGENT OBJECTIVE:
Propose a **concrete strategy** to extend the current 1-ply lookahead to a **multi-ply lookahead** (e.g., 2-ply or deeper) that incorporates **probabilistic simulation** for actions like dice rolls, robber placement, and development cards. The strategy must:
1. Use `execute_spectrum` from `adapters.py` to simulate probabilistic outcomes.
2. Integrate a deeper search algorithm (e.g., expectimax or minimax) to evaluate multi-step sequences.
3. Prioritize actions with high long-term value while accounting for uncertainty.
4. Avoid simple heuristics and focus on algorithmic solutions using the provided adapters.
Provide a clear, actionable plan for the Coder to implement.
================================ Human Message =================================

STRATEGIZER
To extend the current 1-ply lookahead to a **multi-ply lookahead with probabilistic simulation**, we will implement an **Expectimax** algorithm. This approach is ideal for games with chance elements like Catan, as it combines probabilistic outcomes with adversarial reasoning. Below is the proposed strategy:

---

### **Proposed Strategy: Multi-Ply Expectimax with Probabilistic Simulation**
#### **Key Features**
1. **Expectimax Algorithm**:
   - Extend the current 1-ply lookahead to **2-ply or deeper** using Expectimax, which handles both adversarial (opponent) and chance (dice, robber, dev cards) nodes.
   - Use `execute_spectrum` to simulate probabilistic outcomes for actions like dice rolls, robber placement, and development cards.

2. **Probabilistic Simulation**:
   - For actions with chance (e.g., dice rolls), use `execute_spectrum` to generate all possible outcomes and their probabilities.
   - For deterministic actions (e.g., building), use `execute_deterministic` as before.

3. **Value Function**:
   - Use the existing `base_fn` or `contender_fn` to evaluate terminal states.
   - Propagate expected values backward through the tree, weighting by probabilities for chance nodes.

4. **Depth Limitation**:
   - Limit the search depth to **2-ply** initially to balance computational cost and strategic depth.
   - Optionally, add iterative deepening for later optimizations.

---

### **Implementation Plan**
#### **Step 1: Define the Expectimax Algorithm**
- 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`.

#### **Step 2: Integrate Probabilistic Simulation**
- For actions with chance (e.g., dice rolls, robber placement, dev cards):
  - Use `execute_spectrum` to generate all possible outcomes and their probabilities.
  - For each outcome, recursively call `expectimax` and weight the result by its probability.
- For deterministic actions (e.g., building):
  - Use `execute_deterministic` as before.

#### **Step 3: Update the `decide` Method**
- Replace the current 1-ply evaluation with the `expectimax` function.
- Pass the current game state, playable actions, and depth limit (e.g., 2) to `expectimax`.
- Return the action with the highest expected value.

#### **Step 4: Optimize for Performance**
- Use `list_prunned_actions` to reduce the action space for deeper searches.
- Cache intermediate results to avoid redundant computations.

---

### **Pseudocode for Expectimax**
```python
def expectimax(game: Game, depth: int, is_max_player: bool) -> float:
    if depth == 0 or game.is_terminal():
        return base_fn()(game, self.color)

    if is_max_player:
        # Adversarial node: maximize
        best_value = -math.inf
        for action in list_prunned_actions(game):
            outcomes = execute_spectrum(game, action) if action.has_chance() else execute_deterministic(game, action)
            value = sum(prob * expectimax(g_after, depth - 1, False) for (g_after, prob) in outcomes)
            best_value = max(best_value, value)
        return best_value
    else:
        # Chance node: expect
        outcomes = expand_spectrum(game, list_prunned_actions(game))
        return sum(prob * expectimax(g_after, depth - 1, True) for (g_after, prob) in outcomes)
```

---

### **Actionable Steps for the Coder**
1. **Implement the `expectimax` function** as described above.
2. **Modify the `decide` method** to use `expectimax` with a depth of 2.
3. **Test the player** against the AlphaBeta opponent and compare performance.
4. **Optimize** by adjusting depth, pruning, or caching as needed.

---
**Expected Outcome**:
This strategy will enable the player to evaluate deeper sequences of actions while accounting for uncertainty, leading to more robust decision-making and improved win rates.

END STRATEGIZER
================================== Ai 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.
================================ Human 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 =================================

GAME RESULTS:

{
  "Player Summary": {
    "AlphaBetaPlayer:RED(depth=2,value_fn=base_fn,prunning=False)": {
      "WINS": 21,
      "AVG VP": 8.866666666666667,
      "AVG SETTLES": 2.8,
      "AVG CITIES": 2.533333333333333,
      "AVG ROAD": 0.5,
      "AVG ARMY": 0.0,
      "AVG DEV VP": 0.0
    },
    "FooPlayer:BLUE": {
      "WINS": 9,
      "AVG VP": 7.166666666666667,
      "AVG SETTLES": 2.6333333333333333,
      "AVG CITIES": 1.7666666666666666,
      "AVG ROAD": 0.5,
      "AVG ARMY": 0.0,
      "AVG DEV VP": 0.0
    }
  },
  "Game Summary": {
    "AVG TICKS": 216.8,
    "AVG TURNS": 74.96666666666667,
    "AVG DURATION": 3.2687970797220864
  }
}
================================ Human Message =================================

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

---
### **PERFORMANCE SUMMARY**
- **Outcome**: **Poor** (9 wins, 21 losses).
- **VP Comparison**: FooPlayer:BLUE averaged **7.17 VP** vs. AlphaBetaPlayer:RED's **8.87 VP** (VP diff: **-1.7**).
- **Key Counts**:
  - Settlements: 2.63 (FooPlayer) vs. 2.80 (Opponent)
  - Cities: 1.77 (FooPlayer) vs. 2.53 (Opponent)
  - Roads: 0.50 (FooPlayer) vs. 0.50 (Opponent)
  - Dev Cards: 0.0 (FooPlayer) vs. 0.0 (Opponent)
  - Total Turns: 74.97

---
### **VERDICT**
**Poor**: The player is underperforming significantly, with a VP deficit of -1.7 and only 30% win rate.

---
### **LIKELY REASONS**
1. **Shallow Search Depth**:
   - The player uses a **2-ply Expectimax search**, which is insufficient for long-term strategic planning.
   - **Code Citation**: `MAX_DEPTH = 2` (Line 35, `foo_player.py`).
   - **Log Corroboration**: Actions are evaluated with limited foresight (e.g., "FooPlayer: Top-level Action #0 = Action(color=<Color.BLUE: 'BLUE'>, action_type=<ActionType.ROLL: 'ROLL'>, value=None) => expected value 1499999878736092.2").

2. **No Specialized Robber/Knight Strategy**:
   - The player evaluates robber actions but lacks a targeted strategy to disrupt the opponent or optimize resource denial.
   - **Log Corroboration**: Robber actions are chosen based on generic expected value (e.g., "FooPlayer: Chosen action = Action(color=<Color.BLUE: 'BLUE'>, action_type=<ActionType.MOVE_ROBBER: 'MOVE_ROBBER'>, value=((0, 0, 0), None, None)) with value 1500000005585555.0").

3. **No Development Card Optimization**:
   - The player does not prioritize or strategically use development cards (e.g., "MONOPOLY", "ROAD_BUILDING", "YEAR_OF_PLENTY").
   - **Log Corroboration**: No evidence of development card actions in logs.

4. **No Adaptive Heuristic**:
   - The player relies solely on the `base_fn` heuristic, which may not capture nuanced strategic opportunities (e.g., road placement, settlement expansion).
   - **Code Citation**: `value_fn = base_fn()` (Line 38, `foo_player.py`).

---
### **STRATEGIC FLAW**
The player's **2-ply search depth** and **generic heuristic** limit its ability to plan strategically, especially for long-term goals like city upgrades, road networks, and development card usage.

---
### **NEXT STEP**
Send to **Strategizer** to design a **deeper search strategy** (e.g., 3-4 ply) and **custom heuristics** for robber placement, development cards, and adaptive road/settlement expansion.

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

- META THOUGHTS:
The transition to a 2-ply Expectimax algorithm initially showed promise, but the latest results indicate a decline in performance (9 wins, 21 losses) compared to the previous 1-ply lookahead. The Analyzer has identified several critical issues:
1. **Shallow Search Depth**: The 2-ply depth is insufficient for long-term strategic planning in Catan.
2. **Lack of Specialized Strategies**: The player does not optimize for robber/knight usage, development cards, or adaptive heuristics.
3. **Generic Heuristic**: The reliance on `base_fn` may not capture nuanced opportunities like road networks or settlement expansion.

The current approach is too limited to compete effectively against the AlphaBeta opponent. A deeper search strategy (e.g., 3-4 ply) combined with custom heuristics for key actions (robber, development cards, road/settlement expansion) is necessary. Additionally, the Strategizer should explore ways to optimize the search space and improve the heuristic evaluation.

- META GOAL:
Develop a **deeper search strategy** (3-4 ply) with **custom heuristics** for robber placement, development cards, and adaptive road/settlement expansion to consistently outperform the AlphaBeta opponent.

- CHOSEN AGENT: STRATEGIZER

- AGENT OBJECTIVE:
Design a **deeper search strategy** (3-4 ply) for the Expectimax algorithm, incorporating the following:
1. **Increased Search Depth**: Propose a method to extend the search depth to 3-4 plies while managing computational complexity (e.g., iterative deepening, move ordering, or pruning).
2. **Custom Heuristics**: Develop specialized heuristics for:
   - Robber placement to disrupt opponents and optimize resource denial.
   - Development card usage (e.g., prioritizing "MONOPOLY" or "ROAD_BUILDING" based on game state).
   - Adaptive road and settlement expansion to maximize long-term value.
3. **Search Optimization**: Suggest techniques to reduce the search space (e.g., pruning non-promising actions, caching intermediate results).
4. **Adaptive Opponent Modeling**: If feasible, propose a way to model the opponent's strategy to improve decision-making.

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
To address the performance decline and improve the `foo_player.py`, we will implement a **3-4 ply Expectimax search** with **custom heuristics** and **search optimizations**. Below is the actionable plan:

---

### **Proposed Strategy: Deeper Expectimax with Custom Heuristics**
#### **Key Features**
1. **Increased Search Depth (3-4 Ply)**:
   - Use **iterative deepening** to gradually increase depth and manage computational cost.
   - Implement **move ordering** to prioritize high-value actions (e.g., building settlements, playing development cards).

2. **Custom Heuristics**:
   - **Robber Placement**: Use `prune_robber_actions` to focus on high-impact placements that disrupt opponents or secure resources.
   - **Development Cards**: Prioritize cards like "MONOPOLY" or "ROAD_BUILDING" based on the current game state and resource availability.
   - **Road/Settlement Expansion**: Use a heuristic that evaluates long-term value, such as resource diversity and expansion potential.

3. **Search Optimization**:
   - **Pruning**: Use `list_prunned_actions` and `prune_robber_actions` to reduce the action space.
   - **Caching**: Cache intermediate results of the `expectimax` function to avoid redundant computations.

4. **Adaptive Opponent Modeling**:
   - Assume the opponent will maximize their own value, and adjust the search tree accordingly.

---

### **Implementation Plan**
#### **Step 1: Extend Search Depth to 3-4 Ply**
- Modify the `expectimax` function to support **iterative deepening**:
  - Start with a depth of 1 and gradually increase to 3-4.
  - Use a time limit to ensure the search completes within a reasonable timeframe.

#### **Step 2: Implement Custom Heuristics**
- **Robber Placement**:
  - Use `prune_robber_actions` to filter robber actions to only the most impactful ones.
  - Prioritize placements that block opponents' high-value resource tiles.
- **Development Cards**:
  - Prioritize playing "MONOPOLY" or "ROAD_BUILDING" based on the current resource state.
  - Use `value_production` to evaluate the potential impact of each card.
- **Road/Settlement Expansion**:
  - Use a heuristic that evaluates the long-term value of building roads and settlements, such as access to diverse resources and expansion potential.

#### **Step 3: Optimize Search Space**
- **Pruning**:
  - Use `list_prunned_actions` to reduce the action space.
  - For robber actions, use `prune_robber_actions` to further reduce the search space.
- **Caching**:
  - Cache intermediate results of the `expectimax` function to avoid redundant computations.

#### **Step 4: Update the `decide` Method**
- Replace the current 2-ply search with the new 3-4 ply Expectimax.
- Integrate the custom heuristics and search optimizations.

---

### **Pseudocode for Updated Expectimax**
```python
def expectimax(node_game: Game, depth: int, alpha: float, beta: float, cache: dict) -> float:
    """Return the expectimax value of node_game from the perspective of self.color."""
    # Check cache for previously computed results
    cache_key = (node_game.state_hash(), depth)
    if cache_key in cache:
        return cache[cache_key]

    # Terminal check
    winner = node_game.winning_color()
    if winner is not None or depth == 0:
        value = custom_heuristic(node_game, self.color)
        cache[cache_key] = value
        return value

    # Get pruned actions
    node_actions = list_prunned_actions(node_game)
    if not node_actions:
        value = custom_heuristic(node_game, self.color)
        cache[cache_key] = value
        return value

    # Determine if this is a MAX or MIN node
    node_color = node_actions[0].color
    is_max_node = (node_color == self.color)

    if is_max_node:
        best_value = -math.inf
        for act in node_actions:
            # Apply move ordering to prioritize high-value actions
            if act.action_type == ActionType.PLAY_DEV_CARD:
                act = prioritize_dev_cards(act, node_game)

            outcomes = execute_spectrum(node_game, act)
            expected = 0.0
            for (g_after, prob) in outcomes:
                val = expectimax(g_after, depth - 1, alpha, beta, cache)
                expected += prob * val
                if expected > best_value:
                    best_value = expected
                    alpha = max(alpha, best_value)
                    if beta <= alpha:
                        break  # Alpha-beta pruning
        cache[cache_key] = best_value
        return best_value
    else:
        worst_value = math.inf
        for act in node_actions:
            outcomes = execute_spectrum(node_game, act)
            expected = 0.0
            for (g_after, prob) in outcomes:
                val = expectimax(g_after, depth - 1, alpha, beta, cache)
                expected += prob * val
                if expected < worst_value:
                    worst_value = expected
                    beta = min(beta, worst_value)
                    if beta <= alpha:
                        break  # Alpha-beta pruning
        cache[cache_key] = worst_value
        return worst_value

def custom_heuristic(game: Game, color: Color) -> float:
    """Evaluate the game state using custom heuristics."""
    base_value = base_fn()(game, color)
    robber_value = evaluate_robber_placement(game, color)
    dev_card_value = evaluate_dev_cards(game, color)
    expansion_value = evaluate_expansion(game, color)
    return base_value + robber_value + dev_card_value + expansion_value
```

---

### **Actionable Steps for the Coder**
1. **Implement Iterative Deepening**:
   - Modify the `decide` method to use iterative deepening for the Expectimax search.
   - Start with a depth of 1 and gradually increase to 3-4.

2. **Integrate Custom Heuristics**:
   - Implement `custom_heuristic` to evaluate robber placement, development cards, and 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.

4. **Test and Validate**:
   - Test the updated player against the AlphaBeta opponent.
   - Monitor performance and adjust heuristics or depth as needed.

---
**Expected Outcome**:
This strategy will enable the player to evaluate deeper sequences of actions while accounting for uncertainty and leveraging custom heuristics, leading to improved decision-making and higher win rates.

END STRATEGIZER
================================== Ai 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.
================================ Human 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 =================================

GAME RESULTS:

{
  "Player Summary": {
    "AlphaBetaPlayer:RED(depth=2,value_fn=base_fn,prunning=False)": {
      "WINS": 16,
      "AVG VP": 8.066666666666666,
      "AVG SETTLES": 2.4,
      "AVG CITIES": 2.2666666666666666,
      "AVG ROAD": 0.5666666666666667,
      "AVG ARMY": 0.0,
      "AVG DEV VP": 0.0
    },
    "FooPlayer:BLUE": {
      "WINS": 14,
      "AVG VP": 7.366666666666666,
      "AVG SETTLES": 2.6,
      "AVG CITIES": 1.9,
      "AVG ROAD": 0.43333333333333335,
      "AVG ARMY": 0.0,
      "AVG DEV VP": 0.1
    }
  },
  "Game Summary": {
    "AVG TICKS": 208.33333333333334,
    "AVG TURNS": 72.13333333333334,
    "AVG DURATION": 8.85061228275299
  }
}
================================ Human Message =================================

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

---
### **PERFORMANCE SUMMARY**
- **Outcome**: **Borderline** (14 wins, 16 losses).
- **VP Comparison**: FooPlayer:BLUE averaged **7.37 VP** vs. AlphaBetaPlayer:RED's **8.07 VP** (VP diff: **-0.7**).
- **Key Counts**:
  - Settlements: 2.60 (FooPlayer) vs. 2.40 (Opponent)
  - Cities: 1.90 (FooPlayer) vs. 2.27 (Opponent)
  - Roads: 0.43 (FooPlayer) vs. 0.57 (Opponent)
  - Dev Cards: 0.10 (FooPlayer) vs. 0.00 (Opponent)
  - Total Turns: 72.13

---
### **VERDICT**
**Borderline**: The player is competitive but still underperforms the opponent in VP and win rate.

---
### **LIKELY REASONS**
1. **Insufficient Search Depth and Heuristic**:
   - The player uses a **3-ply Expectimax search**, which is better than previous iterations but still lacks deeper strategic foresight.
   - **Code Citation**: `MAX_DEPTH = max(1, min(4, self.max_depth))` (Line 35, `foo_player.py`).
   - **Log Corroboration**: Actions are evaluated up to depth 3, but deeper planning is needed for long-term strategies (e.g., "FooPlayer.decide: Iterative deepening at depth 3").

2. **Generic Heuristic**:
   - The player relies on `base_fn` and a small production bonus (`custom_heuristic`), which may not capture nuanced strategic opportunities like road networks, settlement expansion, or opponent disruption.
   - **Code Citation**: `value_fn = base_fn()` (Line 38, `foo_player.py`).
   - **Log Corroboration**: Actions are chosen based on generic expected values (e.g., "FooPlayer: Chosen action = Action(color=<Color.BLUE: 'BLUE'>, action_type=<ActionType.PLAY_YEAR_OF_PLENTY: 'PLAY_YEAR_OF_PLENTY'>, value=('WOOD', 'SHEEP')) with value 1199999930809222.5").

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**: Robber actions are evaluated but lack strategic depth (e.g., "FooPlayer: Depth 3 Top-level Action #0 = Action(color=<Color.BLUE: 'BLUE'>, action_type=<ActionType.MOVE_ROBBER: 'MOVE_ROBBER'>, value=((0, 0, 0), None, None)) => expected value 899999944462766.1").

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**: Development card actions are considered but lack strategic prioritization (e.g., "FooPlayer: Depth 3 Top-level Action #0 = Action(color=<Color.BLUE: 'BLUE'>, action_type=<ActionType.PLAY_YEAR_OF_PLENTY: 'PLAY_YEAR_OF_PLENTY'>, value=('WOOD', 'SHEEP')) => expected value 1199999930809222.5").

---
### **STRATEGIC FLAW**
The player's **3-ply search depth** and **generic heuristic** limit its ability to plan strategically for long-term goals like city upgrades, road networks, and targeted opponent disruption.

---
### **NEXT STEP**
Send to **Strategizer** to design a **deeper search strategy** (e.g., 4-5 ply) and **custom heuristics** for adaptive road/settlement expansion, robber placement, and development card optimization.

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

- META THOUGHTS:
The latest iteration of the FooPlayer, with a 3-ply Expectimax search and custom heuristics, has shown improvement but remains borderline competitive (14 wins, 16 losses). The Analyzer has identified key areas for further enhancement:
1. **Insufficient Search Depth**: The current 3-ply depth is better but still lacks the foresight needed for long-term strategic planning.
2. **Generic Heuristic**: The reliance on `base_fn` and a small production bonus does not fully capture nuanced opportunities like road networks, settlement expansion, or opponent disruption.
3. **Limited Robber/Knight and Development Card Strategies**: These actions are evaluated but lack targeted, adaptive strategies.

The next step should focus on **deeper search** (4-5 ply) and **specialized heuristics** for road/settlement expansion, robber placement, and development card optimization. Additionally, the Strategizer should explore ways to further optimize the search space and improve the heuristic evaluation.

- META GOAL:
Develop 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: STRATEGIZER

- AGENT OBJECTIVE:
Design a **4-5 ply Expectimax search** with the following enhancements:
1. **Increased Search Depth**:
   - Propose a method to extend the search depth to 4-5 plies while managing computational complexity (e.g., iterative deepening, move ordering, or pruning).
2. **Specialized Heuristics**:
   - Develop heuristics for:
     - **Road/Settlement Expansion**: Evaluate long-term value based on resource diversity and expansion potential.
     - **Robber Placement**: Target high-impact placements to disrupt opponents or secure resources.
     - **Development Cards**: Prioritize cards like "MONOPOLY" or "ROAD_BUILDING" based on game state and resource availability.
3. **Search Optimization**:
   - Suggest techniques to reduce the search space (e.g., pruning non-promising actions, caching intermediate results).
4. **Adaptive Opponent Modeling**:
   - If feasible, propose a way to model the opponent's strategy to improve decision-making.

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 Performance History**
- **Evolution 0**: Baseline performance (0 wins, 2.0 avg score).
- **Evolution 1**: Significant improvement with 1-ply lookahead (14 wins, 8.23 avg score).
- **Evolutions 2-3**: Decline or stagnation with deeper Expectimax (9-14 wins, 7.16-7.36 avg score).
  - The current 3-ply Expectimax is **not leveraging depth effectively** due to:
    - Generic heuristics (`base_fn` + small production bonus).
    - Lack of specialized strategies for robber, development cards, and expansion.
    - Inefficient search space management.

---
### **Proposed Strategy: 4-5 Ply Expectimax with Specialized Heuristics**
#### **Key Enhancements**
1. **Increased Search Depth (4-5 Ply)**:
   - Use **iterative deepening** with a **node budget** to manage computational cost.
   - Implement **move ordering** to prioritize high-value actions (e.g., settlements, development cards).

2. **Specialized Heuristics**:
   - **Road/Settlement Expansion**:
     - Evaluate long-term value using `value_production` and resource diversity.
     - Prioritize actions that expand toward high-value tiles (e.g., ports, high-probability resources).
   - **Robber Placement**:
     - Use `prune_robber_actions` to focus on high-impact placements.
     - Target opponents with the most resources or critical infrastructure (e.g., cities).
   - **Development Cards**:
     - Prioritize "MONOPOLY" or "ROAD_BUILDING" based on resource availability and game phase.
     - Use `value_production` to assess the impact of playing a card.

3. **Search Optimization**:
   - **Pruning**:
     - Use `list_prunned_actions` and `prune_robber_actions` to reduce the action space.
   - **Caching**:
     - Cache intermediate results of `expectimax` to avoid redundant computations.

4. **Adaptive Opponent Modeling**:
   - Assume the opponent will maximize their own value, and adjust the search tree accordingly.

---
### **Implementation Plan**
#### **Step 1: Extend Search Depth to 4-5 Ply**
- Modify the `expectimax` function to support **iterative deepening** up to 5 plies.
- Use a **node budget** (e.g., 10,000 nodes) to cap computational cost.

#### **Step 2: Implement Specialized Heuristics**
- Replace the generic `custom_heuristic` with **three specialized components**:
  - **Expansion Heuristic**:
    ```python
    def expansion_heuristic(game: Game, color: Color) -> float:
        try:
            sample = getattr(game, 'state', game)
            prod = value_production(sample, getattr(self, 'name', 'P0'), include_variety=True)
            return 0.1 * float(prod)  # Scale to avoid overwhelming base_fn
        except Exception:
            return 0.0
    ```
  - **Robber Heuristic**:
    ```python
    def robber_heuristic(game: Game, color: Color) -> float:
        try:
            # Prioritize robbing opponents with high resource production
            opponents = [c for c in Color if c != color]
            max_opponent_prod = max(
                value_production(game.state, f"P{opponent.value}", include_variety=False)
                for opponent in opponents
            )
            return -0.2 * max_opponent_prod  # Negative to disrupt opponents
        except Exception:
            return 0.0
    ```
  - **Development Card Heuristic**:
    ```python
    def dev_card_heuristic(game: Game, color: Color) -> float:
        try:
            # Prioritize playing cards like MONOPOLY or ROAD_BUILDING
            dev_cards = getattr(game, 'dev_cards', {})
            if dev_cards.get(color, {}).get('MONOPOLY', 0) > 0:
                return 0.3  # Bonus for playing MONOPOLY
            if dev_cards.get(color, {}).get('ROAD_BUILDING', 0) > 0:
                return 0.2  # Bonus for playing ROAD_BUILDING
        except Exception:
            pass
        return 0.0
    ```

#### **Step 3: Combine Heuristics**
- Replace `custom_heuristic` with a **weighted sum** of the specialized heuristics:
  ```python
  def combined_heuristic(game: Game, color: Color) -> float:
      base_val = base_fn()(game, color)
      expansion_val = expansion_heuristic(game, color)
      robber_val = robber_heuristic(game, color)
      dev_card_val = dev_card_heuristic(game, color)
      return base_val + expansion_val + robber_val + dev_card_val
  ```

#### **Step 4: Optimize Search Space**
- **Pruning**:
  - Use `list_prunned_actions` and `prune_robber_actions` to reduce the action space.
- **Caching**:
  - Cache intermediate results of `expectimax` to avoid redundant computations.

#### **Step 5: 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.

---
### **Actionable Steps for the Coder**
1. **Implement Iterative Deepening**:
   - Modify the `decide` method to use iterative deepening up to 5 plies.
   - Enforce a node budget (e.g., 10,000 nodes) to cap computational cost.

2. **Integrate Specialized Heuristics**:
   - Replace `custom_heuristic` with `combined_heuristic`.
   - Implement `expansion_heuristic`, `robber_heuristic`, and `dev_card_heuristic`.

3. **Optimize Search Space**:
   - Use `list_prunned_actions` and `prune_robber_actions` to reduce the action space.
   - Cache intermediate results of `expectimax`.

4. **Test and Validate**:
   - Test the updated player against the AlphaBeta opponent.
   - Monitor performance and adjust heuristics or depth as needed.

---
**Expected Outcome**:
This strategy will enable the player to evaluate deeper sequences of actions while leveraging specialized heuristics for robber placement, development cards, and expansion, leading to improved decision-making and higher win rates.

END STRATEGIZER

--- Output from META at 20250925_053230 ---
================================== Ai 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.

