--- Input for META at 20250925_053910 ---
================================== 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
================================== 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.
================================ Human 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 =================================

GAME RESULTS:

{
  "Player Summary": {
    "AlphaBetaPlayer:RED(depth=2,value_fn=base_fn,prunning=False)": {
      "WINS": 22,
      "AVG VP": 8.466666666666667,
      "AVG SETTLES": 3.4,
      "AVG CITIES": 1.8666666666666667,
      "AVG ROAD": 0.6666666666666666,
      "AVG ARMY": 0.0,
      "AVG DEV VP": 0.0
    },
    "FooPlayer:BLUE": {
      "WINS": 8,
      "AVG VP": 6.466666666666667,
      "AVG SETTLES": 2.933333333333333,
      "AVG CITIES": 1.4,
      "AVG ROAD": 0.3333333333333333,
      "AVG ARMY": 0.0,
      "AVG DEV VP": 0.06666666666666667
    }
  },
  "Game Summary": {
    "AVG TICKS": 190.73333333333332,
    "AVG TURNS": 65.93333333333334,
    "AVG DURATION": 8.857848278681438
  }
}
================================ Human Message =================================

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

---
### **PERFORMANCE SUMMARY**
- **Outcome**: **Poor** (8 wins, 22 losses).
- **VP Comparison**: FooPlayer:BLUE averaged **6.47 VP** vs. AlphaBetaPlayer:RED's **8.47 VP** (VP diff: **-2.0**).
- **Key Counts**:
  - Settlements: 2.93 (FooPlayer) vs. 3.40 (Opponent)
  - Cities: 1.40 (FooPlayer) vs. 1.87 (Opponent)
  - Roads: 0.33 (FooPlayer) vs. 0.67 (Opponent)
  - Dev Cards: 0.07 (FooPlayer) vs. 0.00 (Opponent)
  - Total Turns: 65.93

---
### **VERDICT**
**Poor**: The player is significantly underperforming, with a VP deficit of -2.0 and only 27% win rate.

---
### **LIKELY REASONS**
1. **Insufficient Search Depth and Heuristic Weighting**:
   - The player uses a **3-ply Expectimax search** with iterative deepening, but the **heuristic weighting** (e.g., `expansion_heuristic`, `robber_heuristic`, `dev_card_heuristic`) is too modest to drive meaningful strategic decisions.
   - **Code Citation**: `return base_val + exp_v + rob_v + dev_v` (Line 180, `foo_player.py`).
   - **Log Corroboration**: Actions are chosen based on generic expected values, with no clear strategic prioritization (e.g., "FooPlayer: Chosen action = Action(color=<Color.BLUE: 'BLUE'>, action_type=<ActionType.END_TURN: 'END_TURN'>, value=None) with value 1199999932666971.5").

2. **Over-Reliance on `END_TURN` Actions**:
   - The player frequently defaults to `END_TURN` actions, even when other actions (e.g., `BUILD_ROAD`, `BUILD_SETTLEMENT`) are available and potentially more valuable.
   - **Log Corroboration**: Repeated "FooPlayer: Chosen action = Action(color=<Color.BLUE: 'BLUE'>, action_type=<ActionType.END_TURN: 'END_TURN'>, value=None)" in `game_output.txt`.

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 **modest heuristic weighting** limit its ability to prioritize strategic actions like road/settlement expansion, development card usage, and targeted opponent disruption.

---
### **NEXT STEP**
Send to **Strategizer** to **increase heuristic weights** and **design a deeper search strategy** (e.g., 4-5 ply) with **adaptive prioritization** for roads, settlements, and development cards.

---
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 specialized heuristics, has shown a decline in performance (8 wins, 22 losses). The Analyzer has identified critical issues:
1. **Insufficient Heuristic Weighting**: The current heuristic contributions are too modest to drive meaningful strategic decisions.
2. **Over-Reliance on `END_TURN` Actions**: The player frequently defaults to ending turns instead of pursuing strategic actions like building roads or settlements.
3. **Limited Robber/Knight and Development Card Strategies**: These actions are evaluated but lack targeted, adaptive strategies.

The next step should focus on **increasing heuristic weights** and **designing a deeper search strategy** (4-5 ply) with **adaptive prioritization** for roads, settlements, and development cards. The Strategizer must also address the over-reliance on `END_TURN` actions by ensuring the heuristics strongly favor strategic moves.

- META GOAL:
Develop a **4-5 ply Expectimax search** with **stronger heuristic weighting** and **adaptive prioritization** for roads, settlements, and development cards to consistently outperform the AlphaBeta opponent.

- CHOSEN AGENT: STRATEGIZER

- AGENT OBJECTIVE:
Design a **4-5 ply Expectimax search** with the following enhancements:
1. **Increased Heuristic Weighting**:
   - Propose **stronger weights** for the specialized heuristics (e.g., expansion, robber placement, development cards) to ensure they drive strategic decisions.
   - Ensure the heuristics strongly favor actions like `BUILD_ROAD`, `BUILD_SETTLEMENT`, and development card usage over `END_TURN`.
2. **Deeper Search Strategy**:
   - Extend the search depth to **4-5 plies** using iterative deepening and a node budget to manage computational cost.
3. **Adaptive Prioritization**:
   - Develop a mechanism to **prioritize strategic actions** (e.g., roads, settlements, development cards) over passive actions like `END_TURN`.
   - Use move ordering to explore high-value actions first.
4. **Search Optimization**:
   - Suggest techniques to reduce the search space (e.g., pruning non-promising actions, caching intermediate results).

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 Performance**
- **Evolution 4** shows a decline (8 wins, 6.47 avg score), indicating:
  - **Insufficient heuristic weighting**: Current heuristics are too modest to drive strategic decisions.
  - **Over-reliance on `END_TURN`**: The player defaults to passive actions instead of pursuing roads, settlements, or development cards.
  - **Limited robber/knight and development card strategies**: These actions are evaluated but lack targeted prioritization.

---
### **Proposed Strategy: 4-5 Ply Expectimax with Stronger Heuristics and Adaptive Prioritization**
#### **Key Enhancements**
1. **Increased Heuristic Weighting**:
   - **Expansion Heuristic**: Increase weight from `0.08` to **`0.25`** to strongly favor road/settlement expansion.
   - **Robber Heuristic**: Increase weight from `-0.12` to **`-0.35`** to aggressively disrupt opponents.
   - **Development Card Heuristic**: Increase weight for "MONOPOLY" from `0.25` to **`0.5`** and "ROAD_BUILDING" from `0.15` to **`0.4`**.

2. **Deeper Search (4-5 Ply)**:
   - Use **iterative deepening** with a **node budget of 15,000** to manage computational cost.
   - Prioritize **high-value actions** (e.g., `BUILD_ROAD`, `BUILD_SETTLEMENT`, development cards) in move ordering.

3. **Adaptive Prioritization**:
   - Modify `_action_priority` to **deprioritize `END_TURN`** and **prioritize strategic actions**:
     - `BUILD_SETTLEMENT`: **120.0** (up from 100.0)
     - `BUILD_ROAD`: **90.0** (up from 70.0)
     - `PLAY_DEV_CARD`: **130.0** (up from 120.0)
     - `END_TURN`: **-100.0** (down from implicit 0.0)

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.

---
### **Implementation Plan**
#### **Step 1: Update Heuristic Weights**
- Modify the `combined_heuristic` function to use the **new weights**:
  ```python
  def combined_heuristic(g: Game, color: Color) -> float:
      base_val = base_value_fn(g, color)
      exp_v = 0.25 * expansion_heuristic(g, color)  # Increased from 0.08
      rob_v = -0.35 * robber_heuristic(g, color)     # Increased from -0.12
      dev_v = dev_card_heuristic(g, color)            # Weights adjusted inside dev_card_heuristic
      return base_val + exp_v + rob_v + dev_v
  ```

#### **Step 2: Adjust `dev_card_heuristic` Weights**
- Update the `dev_card_heuristic` function to use the **new weights**:
  ```python
  def dev_card_heuristic(g: Game, color: Color) -> float:
      # ... (existing logic)
      if count > 0:
          return 0.5  # Increased from 0.25 for MONOPOLY
      if count_rb > 0:
          return 0.4  # Increased from 0.15 for ROAD_BUILDING
      return 0.0
  ```

#### **Step 3: Modify `_action_priority`**
- Update the move ordering to **prioritize strategic actions** and **deprioritize `END_TURN`**:
  ```python
  def _action_priority(act: Action) -> float:
      at = act.action_type
      if at == ActionType.PLAY_DEV_CARD:
          return 130.0
      if at == ActionType.BUILD_SETTLEMENT:
          return 120.0
      if at == ActionType.BUILD_CITY:
          return 110.0
      if at == ActionType.BUILD_ROAD:
          return 90.0
      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:
          return -100.0  # Strongly deprioritize
      return 0.0
  ```

#### **Step 4: Extend Search Depth to 4-5 Ply**
- Update the `decide` method to use **iterative deepening up to 5 plies** with a **node budget of 15,000**:
  ```python
  MAX_DEPTH = 5
  NODE_BUDGET = 15000
  ```

#### **Step 5: Test and Validate**
- Test the updated player against the AlphaBeta opponent.
- Monitor performance and adjust weights or depth as needed.

---
### **Actionable Steps for the Coder**
1. **Update Heuristic Weights**:
   - Modify `combined_heuristic` and `dev_card_heuristic` to use the **new weights**.
2. **Adjust Move Ordering**:
   - Update `_action_priority` to **prioritize strategic actions** and **deprioritize `END_TURN`**.
3. **Extend Search Depth**:
   - Set `MAX_DEPTH = 5` and `NODE_BUDGET = 15000` in the `decide` method.
4. **Test and Validate**:
   - Test the updated player and monitor performance.

---
**Expected Outcome**:
This strategy will ensure the player **prioritizes strategic actions** (roads, settlements, development cards) over passive moves, while leveraging deeper search and stronger heuristics to outperform the AlphaBeta opponent.

END STRATEGIZER

--- Output from META at 20250925_053928 ---
================================== Ai 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.

