--- Input for STRATEGIZER at 20250925_052409 ---
================================ Human 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.
================================== Ai 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
================================ Human Message =================================

This is the current performance history

{
  "Evolution 0": {
    "wins": 0,
    "avg_score": 2.0,
    "avg_turns": 59.8,
    "full_game_log_path": "game_20250925_051626_fg/game_output.txt",
    "json_game_results_path": "game_20250925_051626_fg/game_20250925_051626_fg.json",
    "cur_foo_player_path": "game_20250925_051626_fg/foo_player.py",
    "cli_run_id": "game_20250925_051626_fg",
    "timestamp": "2025-09-25 05:17:01"
  },
  "Evolution 1": {
    "wins": 14,
    "avg_score": 8.233333333333333,
    "avg_turns": 75.03333333333333,
    "full_game_log_path": "game_20250925_051839_fg/game_output.txt",
    "json_game_results_path": "game_20250925_051839_fg/game_20250925_051839_fg.json",
    "cur_foo_player_path": "game_20250925_051839_fg/foo_player.py",
    "cli_run_id": "game_20250925_051839_fg",
    "timestamp": "2025-09-25 05:19:33"
  },
  "Evolution 2": {
    "wins": 9,
    "avg_score": 7.166666666666667,
    "avg_turns": 74.96666666666667,
    "full_game_log_path": "game_20250925_052153_fg/game_output.txt",
    "json_game_results_path": "game_20250925_052153_fg/game_20250925_052153_fg.json",
    "cur_foo_player_path": "game_20250925_052153_fg/foo_player.py",
    "cli_run_id": "game_20250925_052153_fg",
    "timestamp": "2025-09-25 05:23:34"
  }
}
================================ Human Message =================================

This is the current foo_player.py file

from .adapters import (
    Game,
    Player,
    Color,
    copy_game,
    execute_deterministic,
    execute_spectrum,
    list_prunned_actions,
    base_fn,
)

import math
from typing import List


class FooPlayer(Player):
    def __init__(self, name=None):
        # Initialize as the BLUE player by default (keeps compatibility with previous versions)
        super().__init__(Color.BLUE, name)

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

        Strategy implemented:
        - Use an Expectimax search to a fixed depth (default 2 plies).
        - For each node, we consider the pruned action list returned by adapters.list_prunned_actions
          to reduce branching.
        - For each action we simulate all possible outcomes using execute_spectrum (which returns
          (game_after, probability) tuples). This naturally handles deterministic actions as a
          special case (single outcome with prob=1.0).
        - Chance outcomes are folded into the expected value computation for the action.
        - Nodes where the acting color equals this player's color are treated as MAX nodes;
          otherwise they are treated as MIN nodes (adversarial opponent).

        Notes & assumptions:
        - We rely only on the adapters surface (copy_game, execute_spectrum, execute_deterministic,
          list_prunned_actions, base_fn).
        - If playable_actions is empty, returns None.
        - Depth counts plies: depth=0 means evaluate the current state with the heuristic.
        - This implementation avoids additional hand-crafted heuristics and follows the
          expectimax structure proposed by the strategizer.

        Debugging:
        - Print statements emit evaluated expected values for top-level actions and any exceptions
          encountered during simulation.

        Args:
            game (Game): read-only current game state
            playable_actions (Iterable[Action]): legal actions to choose from
        Returns:
            Action | None: chosen action or None if no actions available
        """
        # Convert to a concrete list so we can iterate multiple times and index
        actions = list(playable_actions) if playable_actions is not None else []

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

        # Depth for Expectimax (plies). Start with 2 as recommended.
        MAX_DEPTH = 2

        # Build the base value function once. It returns a function f(game, color)->float
        value_fn = base_fn()

        # Recursive Expectimax implementation
        def expectimax(node_game: Game, depth: int) -> float:
            """Return the expectimax value of node_game from the perspective of self.color.

            - If depth == 0 or there are no legal actions, evaluate with value_fn.
            - Otherwise, for each pruned action, compute the expected value over its spectrum
              (execute_spectrum). Then either take max or min over actions depending on the
              acting color.
            """
            try:
                # Terminal check: if the game reports a winner, evaluate directly.
                winner = None
                try:
                    # Many Game implementations expose a winning_color() method per adapters comment.
                    winner = node_game.winning_color()
                except Exception:
                    # If winning_color isn't available or errors, fall back to continuing search.
                    winner = None

                if winner is not None:
                    # Terminal state: return heuristic value (value_fn may incorporate terminal logic)
                    return value_fn(node_game, self.color)

                if depth == 0:
                    return value_fn(node_game, self.color)

                # Get a pruned list of actions to reduce branching.
                node_actions = list_prunned_actions(node_game)

                if not node_actions:
                    # No legal actions -> evaluate heuristic
                    return value_fn(node_game, self.color)

                # Determine if this node is a MAX node (our player) or MIN node (opponent).
                # We infer the acting color from the first available action; list_prunned_actions
                # returns actions with an associated color field.
                node_color = node_actions[0].color
                is_max_node = (node_color == self.color)

                if is_max_node:
                    best_value = -math.inf
                    # For each action, compute expected value across possible outcomes
                    for act in node_actions:
                        try:
                            # Use spectrum expansion to handle chance outcomes. Deterministic actions
                            # will simply return a single outcome with prob=1.0.
                            outcomes = execute_spectrum(node_game, act)
                        except Exception as e:
                            print(f'FooPlayer.expectimax: execute_spectrum failed for action {act}: {e}')
                            continue

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

                        if expected > best_value:
                            best_value = expected

                    return best_value

                else:
                    # MIN node: assume adversary minimizes our value (adversarial opponent)
                    worst_value = math.inf
                    for act in node_actions:
                        try:
                            outcomes = execute_spectrum(node_game, act)
                        except Exception as e:
                            print(f'FooPlayer.expectimax: execute_spectrum failed for action {act}: {e}')
                            continue

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

                        if expected < worst_value:
                            worst_value = expected

                    return worst_value

            except Exception as e:
                # Any unexpected error during expectimax should yield a very low value so the action
                # won't be chosen at the top level.
                print(f'FooPlayer.expectimax: unexpected error: {e}')
                return -math.inf

        # Evaluate each top-level action using the expectimax search
        best_action = None
        best_value = -math.inf

        for idx, action in enumerate(actions):
            try:
                # Copy the game to avoid any in-place changes by adapters
                game_copy = copy_game(game)

                # Use execute_spectrum to capture all possible outcomes (handles deterministic as well)
                try:
                    outcomes = execute_spectrum(game_copy, action)
                except Exception as e:
                    # Fall back to deterministic execution if spectrum isn't supported for this action
                    print(f'FooPlayer.decide: execute_spectrum failed for top-level action {action}: {e}; trying deterministic')
                    try:
                        outcomes = execute_deterministic(game_copy, action)
                    except Exception as e2:
                        print(f'FooPlayer.decide: execute_deterministic also failed for action {action}: {e2}')
                        outcomes = []

                if not outcomes:
                    print(f'FooPlayer.decide: No outcomes for action {action}; skipping')
                    expected_value = -math.inf
                else:
                    expected_value = 0.0
                    for (g_after, prob) in outcomes:
                        try:
                            val = expectimax(g_after, MAX_DEPTH - 1)
                        except Exception as e:
                            print(f'FooPlayer.decide: expectimax error on outcome of action {action}: {e}')
                            val = -math.inf
                        expected_value += prob * val

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

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

            # Track the best action seen so far (we maximize at the root for our player)
            if expected_value > best_value:
                best_value = expected_value
                best_action = action

        # Fallback: if best_action is still None (all evaluations failed), pick the first action
        if best_action is None and len(actions) > 0:
            print('FooPlayer: All evaluations failed; defaulting to first action')
            best_action = actions[0]

        print(f'FooPlayer: Chosen action = {best_action} with value {best_value}')
        return best_action

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

This is the current adapters.py file

"""Unified adapter for Catanatron agents.

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

Everything here is a thin re-export or trivial wrapper from catanatron & friends.
"""

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

================================ Human 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.

--- Output from STRATEGIZER at 20250925_052445 ---
================================== Ai Message ==================================
Tool Calls:
  think_tool (DzEI8s9iP)
 Call ID: DzEI8s9iP
  Args:
    reflection: ### Analysis of Current Situation
1. **Performance Decline**: The transition from 1-ply to 2-ply Expectimax resulted in a performance drop (14 wins to 9 wins). This suggests that the increased depth is not being leveraged effectively, possibly due to:
   - Insufficient depth for meaningful strategic planning.
   - Over-reliance on the generic `base_fn` heuristic.
   - Lack of specialized handling for critical actions like robber placement, development cards, and road/settlement expansion.

2. **Search Depth Limitations**: A 2-ply search is too shallow for Catan's complexity. A deeper search (3-4 plies) is needed, but this must be balanced with computational feasibility.

3. **Heuristic Weaknesses**: The current `base_fn` heuristic is too generic. Custom heuristics for robber placement, development cards, and adaptive expansion are required to capture nuanced opportunities.

4. **Search Optimization**: The current implementation does not optimize the search space, leading to inefficiencies. Techniques like pruning, move ordering, and caching could significantly improve performance.

### Proposed Strategy
1. **Increase Search Depth to 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 for Key Actions**:
   - **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**:
   - If feasible, model the opponent's likely actions (e.g., assuming they will maximize their own value) and adjust the search tree accordingly.

### Next Steps
- Provide a clear, actionable plan for the Coder to implement a 3-4 ply Expectimax with custom heuristics and search optimizations.
- Focus on algorithmic solutions using the available `adapters.py` functions, avoiding simple rule-based heuristics.
================================= Tool Message =================================
Name: think_tool

Reflection recorded: ### Analysis of Current Situation
1. **Performance Decline**: The transition from 1-ply to 2-ply Expectimax resulted in a performance drop (14 wins to 9 wins). This suggests that the increased depth is not being leveraged effectively, possibly due to:
   - Insufficient depth for meaningful strategic planning.
   - Over-reliance on the generic `base_fn` heuristic.
   - Lack of specialized handling for critical actions like robber placement, development cards, and road/settlement expansion.

2. **Search Depth Limitations**: A 2-ply search is too shallow for Catan's complexity. A deeper search (3-4 plies) is needed, but this must be balanced with computational feasibility.

3. **Heuristic Weaknesses**: The current `base_fn` heuristic is too generic. Custom heuristics for robber placement, development cards, and adaptive expansion are required to capture nuanced opportunities.

4. **Search Optimization**: The current implementation does not optimize the search space, leading to inefficiencies. Techniques like pruning, move ordering, and caching could significantly improve performance.

### Proposed Strategy
1. **Increase Search Depth to 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 for Key Actions**:
   - **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**:
   - If feasible, model the opponent's likely actions (e.g., assuming they will maximize their own value) and adjust the search tree accordingly.

### Next Steps
- Provide a clear, actionable plan for the Coder to implement a 3-4 ply Expectimax with custom heuristics and search optimizations.
- Focus on algorithmic solutions using the available `adapters.py` functions, avoiding simple rule-based heuristics.
================================== Ai 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

