--- Input for META at 20250925_044702 ---
================================== Ai Message ==================================

META THOUGHTS: After reflection, I identified the real issue causing our regression. Evolution 2 built significantly more cities (1.93 avg) compared to Evolution 4 (1.2 avg), while Evolution 4 actually built more settlements (2.93 vs 2.5). Since cities are worth 2 VP each vs 1 VP for settlements, the city-building difference explains our VP drop from 8.27 to 6.9. The strategizer's focus on maritime trade filtering and value function switching misses this core issue. We need to specifically address why our city-building prioritization degraded.

META GOAL: Restore Evolution 2's superior city-building strategy (1.93 cities avg) by identifying and fixing why our current implementation prioritizes settlements over city upgrades, targeting the specific VP generation mechanism that made Evolution 2 successful.

CHOSEN AGENT: STRATEGIZER

AGENT OBJECTIVE: I've identified the real cause of our regression. Evolution 2 achieved 16 wins with 1.93 cities average, while Evolution 4 only built 1.2 cities average despite building more settlements (2.93 vs 2.5). Since cities generate 2 VP vs 1 VP for settlements, this explains our VP drop from 8.27 to 6.9. The issue isn't maritime trade filtering - it's city prioritization. Our current infrastructure prioritization logic favors BUILD_SETTLEMENT and BUILD_ROAD but may not adequately prioritize BUILD_CITY. Design a strategy to specifically boost BUILD_CITY prioritization in our action selection, ensuring we upgrade settlements to cities more aggressively like Evolution 2 did. Focus on the city-building mechanism that drives VP generation.
================================ Human Message =================================

STRATEGIZER
**City-Building Restoration Strategy**

### **Key Findings from Evolution 2**
1. **City Prioritization**: Evolution 2 built **1.93 cities per game** (vs 1.2 in Evolution 4) by aggressively upgrading settlements.
2. **Action Selection Logic**: Evolution 2 did **not** explicitly prioritize `BUILD_CITY` over `BUILD_SETTLEMENT` in its infrastructure filtering logic. Instead, it relied on `contender_fn` to evaluate the long-term value of city upgrades.
3. **Maritime Trade Filtering**: Evolution 2 filtered maritime trades only when **>8 actions** were available, allowing more flexibility in resource management.

---

### **Proposed Fixes**
1. **Restore Evolution 2's Maritime Trade Filtering Threshold**:
   - Revert the threshold from **>6** to **>8** to match Evolution 2.
   - This ensures that maritime trades are not prematurely filtered, allowing better resource management for city upgrades.

2. **Explicitly Prioritize `BUILD_CITY` Actions**:
   - Modify the infrastructure prioritization logic to **favor `BUILD_CITY` over `BUILD_SETTLEMENT`** when both are available.
   - This ensures that settlements are upgraded to cities whenever possible.

3. **Adjust `contender_fn` Weights for City Building**:
   - If the above changes are insufficient, experiment with custom weights for `contender_fn` to **heavily favor city upgrades**.

---

### **Implementation Steps**
1. **Revert Maritime Trade Filtering Threshold**:
   - In `FooPlayer.decide`, update the threshold from `>6` to `>8`:
     ```python
     if len(actions) > 8:  # Revert to Evolution 2's threshold
         non_trade_actions = [a for a in actions if getattr(a, 'action_type', None) != ActionType.MARITIME_TRADE]
         if non_trade_actions:
             print(f'FooPlayer.decide: Filtering out maritime trades from {len(actions)} to {len(non_trade_actions)} actions')
             actions = non_trade_actions
     ```

2. **Prioritize `BUILD_CITY` Over `BUILD_SETTLEMENT`**:
   - In `FooPlayer.decide`, explicitly prioritize `BUILD_CITY` actions:
     ```python
     city_actions = [a for a in actions if getattr(a, 'action_type', None) == ActionType.BUILD_CITY]
     if city_actions:
         print(f'FooPlayer.decide: Prioritizing {len(city_actions)} city upgrades over other infrastructure')
         actions = city_actions
     else:
         # Fall back to other infrastructure actions
         infrastructure_types = {ActionType.BUILD_SETTLEMENT, ActionType.BUILD_ROAD}
         infrastructure_actions = [a for a in actions if getattr(a, 'action_type', None) in infrastructure_types]
         if infrastructure_actions:
             print(f'FooPlayer.decide: Prioritizing {len(infrastructure_actions)} infrastructure actions')
             actions = infrastructure_actions
     ```

3. **Test and Validate**:
   - Implement these changes and validate performance in **Evolution 5**.
   - Monitor city-building metrics to ensure the average number of cities per game increases to **~1.9**.

---

### **Expected Outcome**
- **City Building**: Restore city-building rates to **~1.9 cities per game**, matching Evolution 2.
- **Win Rate**: Improve win rate to **~53%** by focusing on high-VP city upgrades.
- **Resource Management**: Maintain flexibility in resource management by reverting the maritime trade filtering threshold.

**Next Steps**: Implement these changes and validate performance in **Evolution 5**.
END STRATEGIZER
================================== Ai Message ==================================

META THOUGHTS: The strategizer correctly identified the core issue - Evolution 2's success was driven by superior city-building (1.93 vs 1.2 cities avg), which directly explains the VP difference. However, I'm concerned about the proposed exclusive prioritization of BUILD_CITY over all other infrastructure. This could be too aggressive and prevent necessary expansion. We need a balanced approach that weights cities more heavily while maintaining strategic flexibility.

META GOAL: Restore Evolution 2's superior city-building performance (1.93 cities avg) through improved prioritization of BUILD_CITY actions while maintaining strategic balance and reverting the maritime trade filtering threshold to match Evolution 2's approach.

CHOSEN AGENT: CODER

AGENT OBJECTIVE: Implement the strategizer's city-building restoration plan with a balanced approach. Make these specific changes to foo_player.py: 1) Revert maritime trade filtering threshold from >6 back to >8 to match Evolution 2, 2) Modify infrastructure prioritization to strongly favor BUILD_CITY actions but not exclusively - use a weighted approach where cities get priority but settlements/roads are still evaluated when cities aren't optimal, 3) Add explicit city prioritization logic that checks for BUILD_CITY first but falls back to other infrastructure rather than completely excluding them, 4) Keep all existing contender_fn, depth-3 lookahead, and defensive error handling. Focus on restoring the city-building rate that made Evolution 2 successful (1.93 cities avg) while maintaining strategic flexibility.
================================ Human Message =================================

CODER

Changes made to foo_player.py:
- Implemented the strategizer's recommendations to restore Evolution 2 behavior while fixing known bugs.
- Reverted maritime trade filtering threshold to >8 (from >6) to match Evolution 2.
- Ensured all filtering uses list comprehensions and materialized lists to avoid 'filter' iterator issues.
- Reintroduced explicit city-prioritization:
  - City actions (ActionType.BUILD_CITY) are detected and evaluated first by reordering the actions list.
  - Added a modest additive city bonus (50.0) during evaluation to strongly but not exclusively favor BUILD_CITY.
- Kept depth-3 lookahead and contender_fn(DEFAULT_WEIGHTS) as the primary heuristic with fallback to base_fn and a dumb lambda.
- Preserved prune_robber_actions usage but only apply it defensively (only if it meaningfully reduces branching).
- Kept full evaluation of prioritized actions (no aggressive root/child pruning), restoring Evolution 2's inclusive search behavior.
- Fixed list materialization for list_prunned_actions and prune_robber_actions return values to avoid len()/index errors.
- Added many print() debug statements to trace decisions, pruning, city bonuses, and node expansions.

New/modified functions and behavior:
- No new top-level functions added. Changes are within FooPlayer.__init__, decide, and _evaluate_node.
- _evaluate_node now materializes list_prunned_actions outputs and handles exceptions robustly.

Debugging aids added:
- Prints for using contender_fn vs base_fn fallback.
- Prints when robber actions are pruned and when maritime trades are filtered.
- Prints when city bonus is applied during action evaluation and how many city actions are prioritized.

Notes and next steps:
- The city bonus (50.0) is tunable. If the agent becomes too eager to upgrade cities when it shouldn't, reduce this bonus; if it under-upgrades, increase it.
- If performance does not return to Evolution 2 levels, the next step is to try base_fn as the primary heuristic (switch in __init__) or tune DEFAULT_WEIGHTS for contender_fn to more heavily favor city upgrades.
- Consider adding a transposition cache (per-decision) to reduce duplicate node evaluations while preserving exhaustive search.

If you'd like, I can:
- Switch primary heuristic to base_fn for testing.
- Add a per-decision transposition table to reduce duplicate node expansions.
- Tune the city bonus or make it a constructor parameter.

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

GAME RESULTS:

{
  "Player Summary": {
    "AlphaBetaPlayer:RED(depth=2,value_fn=base_fn,prunning=False)": {
      "WINS": 17,
      "AVG VP": 8.366666666666667,
      "AVG SETTLES": 2.7666666666666666,
      "AVG CITIES": 2.1666666666666665,
      "AVG ROAD": 0.6333333333333333,
      "AVG ARMY": 0.0,
      "AVG DEV VP": 0.0
    },
    "FooPlayer:BLUE": {
      "WINS": 13,
      "AVG VP": 7.166666666666667,
      "AVG SETTLES": 2.1333333333333333,
      "AVG CITIES": 1.7666666666666666,
      "AVG ROAD": 0.36666666666666664,
      "AVG ARMY": 0.2,
      "AVG DEV VP": 0.36666666666666664
    }
  },
  "Game Summary": {
    "AVG TICKS": 207.6,
    "AVG TURNS": 72.4,
    "AVG DURATION": 3.984462857246399
  }
}
================================ Human Message =================================

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

---
### **PERFORMANCE SUMMARY**
- **Outcome**: **Borderline** (13 wins, 17 losses).
- **VP Comparison**: FooPlayer (7.17 VP) vs. Opponent (8.37 VP), **VP Diff: -1.2**.
- **Key Counts**:
  - Settlements: 2.13
  - Cities: 1.77
  - Roads: 0.37
  - Dev Cards: 0.37
  - Total Turns: 72.4

---
### **VERDICT**
**Borderline**: FooPlayer won 13/30 games but lost with a VP deficit of -1.2. The player shows improvement in city upgrades and settlements but still struggles with road expansion and long-term resource management.

---
### **LIKELY REASONS**
1. **Overvaluation of Maritime Trades**:
   - The player frequently selects `MARITIME_TRADE` actions, even when higher-value infrastructure actions (e.g., `BUILD_ROAD`, `BUILD_SETTLEMENT`) are available.
   - **Log Evidence**:
     ```
     FooPlayer.decide: Chosen action=Action(color=<Color.BLUE: 'BLUE'>, action_type=<ActionType.MARITIME_TRADE: 'MARITIME_TRADE'>, value=('BRICK', 'BRICK', 'BRICK', 'BRICK', 'WHEAT')) score=1500000052814658.0 node_expansions=15
     FooPlayer.decide: Chosen action=Action(color=<Color.BLUE: 'BLUE'>, action_type=<ActionType.MARITIME_TRADE: 'MARITIME_TRADE'>, value=('SHEEP', 'SHEEP', 'SHEEP', 'SHEEP', 'WHEAT')) score=1800000075036888.2 node_expansions=40
     ```
   - This suggests the **value function (`contender_fn`)** may still overvalue short-term resource gains over long-term infrastructure.

2. **Insufficient Road Expansion**:
   - The player builds **fewer roads (0.37 avg)** compared to the opponent (0.63 avg), limiting expansion and resource access.
   - **Log Evidence**:
     ```
     FooPlayer.decide: Prioritizing 4 infrastructure actions over 5 total
     FooPlayer.decide: Chosen action=Action(color=<Color.BLUE: 'BLUE'>, action_type=<ActionType.BUILD_SETTLEMENT: 'BUILD_SETTLEMENT'>, value=18) score=2400000116693371.0 node_expansions=12
     ```
   - The player prioritizes settlements and cities but neglects road expansion, which is critical for early-game resource access.

3. **Aggressive Robber Pruning**:
   - The player prunes robber actions aggressively, reducing its ability to strategically place the robber or play knights.
   - **Log Evidence**:
     ```
     FooPlayer.decide: Pruned robber actions from 18 to 1
     FooPlayer._evaluate_node: Pruned robber actions from 18 to 1
     ```
   - This limits the player's ability to disrupt opponents or secure resources.

4. **Excessive Node Expansions for Low-Impact Actions**:
   - The player spends excessive computational effort evaluating low-impact actions (e.g., `MARITIME_TRADE`).
   - **Log Evidence**:
     ```
     FooPlayer.decide: Fully evaluating 5 actions with lookahead depth=3
       Action 0: expected_value=1500000052814629.0 action_type=ActionType.END_TURN
       Action 1: expected_value=1500000052814635.5 action_type=ActionType.MARITIME_TRADE
       Action 2: expected_value=1500000052814615.5 action_type=ActionType.MARITIME_TRADE
       Action 3: expected_value=1500000052814623.5 action_type=ActionType.MARITIME_TRADE
       Action 4: expected_value=1500000052814658.0 action_type=ActionType.MARITIME_TRADE
     FooPlayer.decide: Chosen action=Action(color=<Color.BLUE: 'BLUE'>, action_type=<ActionType.MARITIME_TRADE: 'MARITIME_TRADE'>, value=('BRICK', 'BRICK', 'BRICK', 'BRICK', 'WHEAT')) score=1500000052814658.0 node_expansions=15
     ```

---
### **STRATEGIC FLAW**
The player's **value function (`contender_fn`)** and **action prioritization logic** are misaligned, leading to excessive focus on short-term resource trades and insufficient investment in road expansion and disruptive actions (e.g., robber/knight).

---
### **NEXT STEP**
Send to **Strategizer** to **retune the value function (`contender_fn`)** to prioritize road expansion and robber/knight actions over maritime trades, and **adjust the pruning logic** to avoid over-aggressive filtering of robber actions.

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

META THOUGHTS: After analyzing our performance progression, Evolution 5 shows improvement (13 wins vs 8 in Evolution 4) and our city prioritization is working (1.77 cities vs 1.2), but we're still below Evolution 2's peak (16 wins, 8.27 VP). The persistent issue across multiple evolutions is contender_fn overvaluing maritime trades despite our prioritization logic. Since we've tried multiple infrastructure prioritization tweaks without fully restoring Evolution 2 performance, the core issue may be the value function itself. Evolution 2 might have used base_fn instead of contender_fn.

META GOAL: Test base_fn as the primary heuristic to resolve the persistent maritime trade overvaluation issue that has prevented us from restoring Evolution 2's peak performance (16 wins, 8.27 VP), while maintaining our successful city prioritization and infrastructure focus.

CHOSEN AGENT: STRATEGIZER

AGENT OBJECTIVE: We're making progress (Evolution 5: 13 wins vs Evolution 4: 8 wins) and city prioritization is working (1.77 cities vs 1.2), but we're still below Evolution 2's peak (16 wins, 8.27 VP). The analyzer consistently identifies maritime trade overvaluation across multiple evolutions despite our infrastructure prioritization logic. Since we've tried multiple approaches with contender_fn without fully restoring Evolution 2's performance, design a strategy to test base_fn as the primary heuristic instead of contender_fn. Evolution 2 may have been using base_fn, which could explain why it avoided maritime trade overvaluation. Create a plan to switch to base_fn while preserving our successful city prioritization, infrastructure focus, and depth-3 lookahead that have been working.
================================ Human Message =================================

STRATEGIZER
**Strategy: Switch to `base_fn` as Primary Heuristic**

### **Key Insight**
The persistent issue of **maritime trade overvaluation** across multiple evolutions suggests that `contender_fn` is inherently biased toward trades, even with our infrastructure prioritization logic. **Evolution 2 may have used `base_fn`**, which could explain its superior performance (16 wins, 8.27 VP).

### **Proposed Changes**
1. **Replace `contender_fn` with `base_fn`**:
   - Switch the primary heuristic from `contender_fn` to `base_fn` in `FooPlayer.__init__`.
   - This will reduce the bias toward maritime trades and restore a more balanced evaluation.

2. **Preserve City Prioritization**:
   - Retain the explicit prioritization of `BUILD_CITY` actions and the city-building bonus logic.
   - Ensure that infrastructure actions are still evaluated first.

3. **Maintain Depth-3 Lookahead**:
   - Keep the current lookahead depth of 3 to capture long-term infrastructure consequences.

4. **Debugging and Validation**:
   - Add debug prints to confirm the heuristic switch and monitor action selection.
   - Validate performance in **Evolution 6**.

---

### **Implementation Steps**
1. **Update `FooPlayer.__init__` to Use `base_fn`**:
   ```python
   def __init__(self, name: str | None = None, lookahead_depth: int = 3):
       super().__init__(Color.BLUE, name)

       # Use base_fn as the primary heuristic to avoid maritime trade overvaluation
       try:
           self.value_fn = base_fn()
           print('FooPlayer.__init__: Using base_fn as primary heuristic')
       except Exception as e:
           print(f'FooPlayer.__init__: base_fn failed, using dumb fallback: {e}')
           self.value_fn = lambda g, c: 0.0

       self.lookahead_depth = max(1, int(lookahead_depth))
   ```

2. **Retain City Prioritization Logic**:
   - Keep the existing logic in `FooPlayer.decide` that prioritizes `BUILD_CITY` actions.
   - Ensure the city-building bonus is applied during action evaluation.

3. **Test and Validate**:
   - Implement the change and validate performance in **Evolution 6**.
   - Monitor win rate, VP, and city-building metrics to confirm improvement.

---

### **Expected Outcome**
- **Win Rate**: Restore to **~53%** (16 wins) by reducing maritime trade overvaluation.
- **City Building**: Maintain or improve city-building rates (**~1.9 cities per game**).
- **VP**: Increase average VP to **~8.27** by focusing on high-value infrastructure.

**Next Steps**: Implement this change and validate performance in **Evolution 6**.
END STRATEGIZER
================================== Ai Message ==================================

META THOUGHTS: Based on my reflection, the strategizer's hypothesis is logical and well-supported. We've seen consistent maritime trade overvaluation across multiple evolutions despite infrastructure prioritization attempts. Evolution 2's superior performance (16 wins, 8.27 VP) compared to our current state suggests it may have used base_fn instead of contender_fn. This is a systematic approach to test the value function hypothesis while preserving our successful city prioritization improvements.

META GOAL: Test base_fn as the primary heuristic to resolve persistent maritime trade overvaluation and restore Evolution 2's peak performance (16 wins, 8.27 VP) while maintaining our successful city prioritization and infrastructure focus.

CHOSEN AGENT: CODER

AGENT OBJECTIVE: Implement the strategizer's value function switch to test the base_fn hypothesis. Make this specific change to foo_player.py: Switch the primary heuristic from contender_fn to base_fn in FooPlayer.__init__ while preserving all other successful elements including city prioritization logic, infrastructure focus, depth-3 lookahead, maritime trade filtering threshold of >8, and defensive error handling. Add clear debug prints to confirm the heuristic switch. This tests whether base_fn was the key to Evolution 2's superior performance by eliminating the maritime trade overvaluation that has persisted across multiple evolutions with contender_fn.
================================ Human Message =================================

CODER

I updated foo_player.py to implement the Strategizer's recommendation: switching the primary heuristic to base_fn while preserving the successful lookahead, city prioritization, maritime trade threshold (>8), and defensive error handling.

Summary of concrete changes I made:
- Replaced initialization to use base_fn(DEFAULT_WEIGHTS) as the primary heuristic. If base_fn fails, fall back to contender_fn(DEFAULT_WEIGHTS), then a 0.0 lambda.
- Kept lookahead_depth default at 3.
- Preserved city prioritization: reorder to evaluate BUILD_CITY actions first, but do not exclude settlements/roads.
- Kept prune_robber_actions usage and maritime trade filtering threshold of >8.
- Ensured all filtered iterables are materialized lists to avoid 'filter' object errors.
- Retained full lookahead evaluation (no aggressive pruning), defensive try/excepts, and debug print statements to track behavior and node expansions.

Notes & next steps I recommend:
- Run Evolution 6 to validate whether switching to base_fn restores the ~53% win rate and reduces maritime trade overvaluation.
- If maritime trades still dominate, we should consider tuning base_fn weights or adding a light negative penalty for MARITIME_TRADE in our decision logic.
- Consider adding a per-decision transposition cache to reduce duplicate evaluations without pruning actions.

If you want, I can now:
- Add an explicit small penalty when evaluating maritime trade actions to further discourage them (tunable scalar).
- Add a per-decision transposition cache to reduce node expansions.
- Expose city_bonus as a constructor parameter for tuning.

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

GAME RESULTS:

{
  "Player Summary": {
    "AlphaBetaPlayer:RED(depth=2,value_fn=base_fn,prunning=False)": {
      "WINS": 21,
      "AVG VP": 9.0,
      "AVG SETTLES": 2.6,
      "AVG CITIES": 2.7,
      "AVG ROAD": 0.5,
      "AVG ARMY": 0.0,
      "AVG DEV VP": 0.0
    },
    "FooPlayer:BLUE": {
      "WINS": 9,
      "AVG VP": 6.966666666666667,
      "AVG SETTLES": 2.566666666666667,
      "AVG CITIES": 1.3333333333333333,
      "AVG ROAD": 0.5,
      "AVG ARMY": 0.13333333333333333,
      "AVG DEV VP": 0.4666666666666667
    }
  },
  "Game Summary": {
    "AVG TICKS": 221.3,
    "AVG TURNS": 76.7,
    "AVG DURATION": 4.352547558148702
  }
}
================================ 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 (6.97 VP) vs. Opponent (9.0 VP), **VP Diff: -2.03**.
- **Key Counts**:
  - Settlements: 2.57
  - Cities: 1.33
  - Roads: 0.5
  - Dev Cards: 0.47
  - Total Turns: 76.7

---
### **VERDICT**
**Poor**: FooPlayer lost 21/30 games with a significant VP deficit of -2.03. The player struggles with infrastructure development and long-term strategy.

---
### **LIKELY REASONS**
1. **Overvaluation of Maritime Trades**:
   - Despite prioritizing infrastructure, the player still selects `MARITIME_TRADE` actions frequently, even when higher-value actions (e.g., `BUILD_ROAD`, `BUILD_SETTLEMENT`) are available.
   - **Log Evidence**:
     ```
     FooPlayer.decide: Chosen action=Action(color=<Color.BLUE: 'BLUE'>, action_type=<ActionType.MARITIME_TRADE: 'MARITIME_TRADE'>, value=('WOOD', 'WOOD', 'WOOD', 'WOOD', 'BRICK')) score=1799999986138444.0 node_expansions=77
     FooPlayer.decide: Chosen action=Action(color=<Color.BLUE: 'BLUE'>, action_type=<ActionType.MARITIME_TRADE: 'MARITIME_TRADE'>, value=('SHEEP', 'SHEEP', 'SHEEP', 'SHEEP', 'BRICK')) score=1799999986140300.2 node_expansions=71
     ```
   - This suggests the **value function (`base_fn`)** may still overvalue short-term resource gains.

2. **Insufficient Road Expansion**:
   - The player builds **fewer roads (0.5 avg)** compared to the opponent (0.5 avg), limiting expansion and resource access.
   - **Log Evidence**:
     ```
     FooPlayer.decide: Prioritizing 17 infrastructure actions over 18 total
     FooPlayer.decide: Chosen action=Action(color=<Color.BLUE: 'BLUE'>, action_type=<ActionType.BUILD_ROAD: 'BUILD_ROAD'>, value=(19, 21)) score=1799999986141967.8 node_expansions=51
     ```
   - While roads are prioritized, the player fails to execute them consistently.

3. **Aggressive Robber Pruning**:
   - The player prunes robber actions aggressively, reducing its ability to strategically place the robber or play knights.
   - **Log Evidence**:
     ```
     FooPlayer.decide: Pruned robber actions from 18 to 1
     FooPlayer._evaluate_node: Pruned robber actions from 18 to 1
     ```
   - This limits the player's ability to disrupt opponents or secure resources.

4. **Excessive Node Expansions for Low-Impact Actions**:
   - The player spends excessive computational effort evaluating low-impact actions (e.g., `MARITIME_TRADE`).
   - **Log Evidence**:
     ```
     FooPlayer.decide: Fully evaluating 5 actions with lookahead depth=3
       Action 0: expected_value=1799999952808647.0 action_type=ActionType.END_TURN
       Action 1: expected_value=1799999986141966.2 action_type=ActionType.MARITIME_TRADE
       Action 2: expected_value=1799999986141979.8 action_type=ActionType.MARITIME_TRADE
       Action 3: expected_value=1799999986141963.2 action_type=ActionType.MARITIME_TRADE
       Action 4: expected_value=1799999986141990.2 action_type=ActionType.MARITIME_TRADE
     FooPlayer.decide: Chosen action=Action(color=<Color.BLUE: 'BLUE'>, action_type=<ActionType.MARITIME_TRADE: 'MARITIME_TRADE'>, value=('WOOD', 'WOOD', 'WOOD', 'WOOD', 'WHEAT')) score=1799999986141990.2 node_expansions=39
     ```

---
### **STRATEGIC FLAW**
The player's **value function (`base_fn`)** and **action prioritization logic** are misaligned, leading to excessive focus on short-term resource trades and insufficient investment in road expansion and disruptive actions (e.g., robber/knight).

---
### **NEXT STEP**
Send to **Strategizer** to **retune the value function (`base_fn`)** to prioritize road expansion and robber/knight actions over maritime trades, and **adjust the pruning logic** to avoid over-aggressive filtering of robber actions.

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

META THOUGHTS: After deep reflection on our evolution progression, I've identified that we've been chasing incremental fixes without understanding Evolution 2's fundamental success factors. We've now tested both contender_fn and base_fn with persistent maritime trade overvaluation across both. Evolution 2 achieved 53% win rate (16 wins, 8.27 VP) and we've plateaued well below that despite multiple approaches. Since incremental tweaks haven't restored peak performance, we need a more dramatic strategic shift - perhaps a fundamentally different algorithmic approach rather than value function tuning.

META GOAL: Since incremental improvements have failed to restore Evolution 2's peak performance (53% win rate) and maritime trade overvaluation persists across both value functions, design a fundamentally different algorithmic strategy that moves beyond simple value function optimization to a more sophisticated decision-making approach.

CHOSEN AGENT: STRATEGIZER

AGENT OBJECTIVE: We have hit a strategic plateau. Despite testing both contender_fn and base_fn, plus various prioritization schemes, we cannot restore Evolution 2's peak performance (16 wins, 53% win rate, 8.27 VP). Maritime trade overvaluation persists across all value functions we've tested. Our current approach of tweaking heuristics and prioritization isn't working. Design a fundamentally different algorithmic strategy that moves beyond simple lookahead + value function. Consider approaches like: Monte Carlo tree search, multi-objective optimization, rule-based action filtering with hard constraints, or dynamic strategy switching. We need a clever algorithmic breakthrough, not more incremental heuristic tuning. Focus on creating a robust decision-making system that can consistently avoid maritime trade traps and prioritize winning infrastructure actions.
================================ Human Message =================================

STRATEGIZER
**New Algorithmic Strategy: Dynamic Multi-Objective Optimization with Hard Constraints**

### **Core Issue Identified**
Evolution 2's success was not due to a specific value function but rather its **implicit hard constraints** on action selection:
1. **Maritime Trade Filtering**: Only filtered trades when **>8 actions** were available, allowing flexibility.
2. **Infrastructure Focus**: Prioritized `BUILD_CITY`, `BUILD_SETTLEMENT`, and `BUILD_ROAD` **without aggressive pruning**.
3. **Robber Pruning**: Used `prune_robber_actions` to reduce branching but **did not exclude infrastructure actions**.

Our incremental tweaks failed because they **over-constrained** the decision-making process, leading to suboptimal infrastructure choices.

---

### **Proposed Strategy: Dynamic Multi-Objective Optimization**
Replace the current lookahead + value function approach with a **multi-objective optimization framework** that:
1. **Hard Constraints**:
   - **Never prioritize maritime trades** unless no infrastructure actions are available.
   - **Always evaluate `BUILD_CITY` first** if resources allow.
   - **Limit robber actions** to disruptive placements only.

2. **Dynamic Weighting**:
   - Use a **weighted scoring system** for actions based on:
     - **VP Potential**: Cities (2 VP) > Settlements (1 VP) > Roads (0 VP).
     - **Resource Efficiency**: Favor actions that use resources likely to be wasted (e.g., excess brick/wood).
     - **Opponent Disruption**: Prioritize robber placements that block opponents' high-probability resource tiles.

3. **Fallback Mechanism**:
   - If no infrastructure actions are available, use `base_fn` to evaluate trades or development cards.

---

### **Implementation Steps**
1. **Replace Value Function with Multi-Objective Scoring**:
   - Define a scoring function that combines:
     - VP potential (e.g., +20 for cities, +10 for settlements).
     - Resource efficiency (e.g., +5 for using excess resources).
     - Opponent disruption (e.g., +15 for blocking high-yield tiles).

2. **Hard Constraints in `decide`**:
   ```python
   def decide(self, game: Game, playable_actions: Iterable) -> object:
       actions = list(playable_actions)
       if not actions:
           return None

       # Hard constraint: Filter out maritime trades unless no infrastructure exists
       infrastructure_types = {ActionType.BUILD_CITY, ActionType.BUILD_SETTLEMENT, ActionType.BUILD_ROAD}
       infrastructure_actions = [a for a in actions if getattr(a, 'action_type', None) in infrastructure_types]

       if infrastructure_actions:
           # Prioritize BUILD_CITY
           city_actions = [a for a in infrastructure_actions if getattr(a, 'action_type', None) == ActionType.BUILD_CITY]
           if city_actions:
               actions = city_actions
           else:
               actions = infrastructure_actions
       else:
           # Fallback: Use base_fn to evaluate non-infrastructure actions
           actions = [a for a in actions if getattr(a, 'action_type', None) != ActionType.MARITIME_TRADE] or actions

       # Evaluate actions using multi-objective scoring
       best_action = max(actions, key=lambda a: self._score_action(game, a))
       return best_action
   ```

3. **Multi-Objective Scoring Function**:
   ```python
   def _score_action(self, game: Game, action: Action) -> float:
       score = 0.0
       action_type = getattr(action, 'action_type', None)

       # VP Potential
       if action_type == ActionType.BUILD_CITY:
           score += 20
       elif action_type == ActionType.BUILD_SETTLEMENT:
           score += 10

       # Resource Efficiency
       if self._uses_excess_resources(game, action):
           score += 5

       # Opponent Disruption (for robber actions)
       if action_type == ActionType.PLACE_ROBBER:
           score += self._calculate_robber_disruption(game, action)

       return score
   ```

4. **Helper Functions**:
   - `_uses_excess_resources`: Checks if the action consumes resources the player has in excess.
   - `_calculate_robber_disruption`: Estimates how much the robber placement disrupts opponents.

---

### **Expected Outcome**
- **Win Rate**: Restore to **~53%** by focusing on high-VP infrastructure and dynamic scoring.
- **VP**: Increase average VP to **~8.5** by prioritizing cities and efficient resource use.
- **Robustness**: Avoid maritime trade overvaluation by hard constraints and multi-objective scoring.

**Next Steps**: Implement this strategy in **Evolution 7** and validate performance.
END STRATEGIZER
================================== Ai Message ==================================

META THOUGHTS: After deep reflection, I believe the strategizer's multi-objective optimization proposal is too complex and risky. It moves away from the proven lookahead approach and could introduce many new failure modes. Looking at our performance trend (Evolution 2: 16 wins → Evolution 5: 13 wins → Evolution 6: 9 wins), we're moving further from success, not closer. Instead of a complex algorithmic overhaul, I should focus on exactly replicating Evolution 2's successful approach. The issue may not be algorithmic complexity but rather subtle differences in our current implementation from what actually made Evolution 2 work.

META GOAL: Abandon the complex multi-objective approach and instead focus on exactly replicating Evolution 2's successful code structure and logic to restore the 53% win rate (16 wins, 8.27 VP) through precise implementation matching rather than algorithmic innovation.

CHOSEN AGENT: CODER

AGENT OBJECTIVE: Reject the strategizer's complex multi-objective proposal. Instead, implement a precise restoration of Evolution 2's successful approach by reverting to its exact structure. Key requirements: 1) Use contender_fn(DEFAULT_WEIGHTS) as primary heuristic with base_fn fallback (like Evolution 2 likely did), 2) Remove all recent city prioritization bonuses and filtering logic that may be interfering, 3) Use simple maritime trade filtering only when >8 actions (Evolution 2's threshold), 4) Keep depth-3 lookahead and basic robber pruning, 5) Remove complex prioritization schemes and let the value function drive decisions naturally, 6) Focus on clean, simple code that matches Evolution 2's winning formula rather than adding complexity. The goal is to restore Evolution 2's exact decision-making pattern through precise implementation matching.
================================ Human Message =================================

CODER

I updated foo_player.py to implement the precise restoration plan you requested. Summary of the implemented changes:

- Restored Evolution 2 primary heuristic usage: contender_fn(DEFAULT_WEIGHTS) is now the primary value function; base_fn is kept as a fallback.
- Removed recent ad-hoc city bonuses and heavy prioritization—decisions are driven by the value function like Evolution 2.
- Kept depth-3 lookahead (default) and prune_robber_actions for conservative robber pruning.
- Reverted maritime trade filtering threshold to >8 and used list comprehensions (materialized lists) to avoid filter iterator bugs.
- Preserved defensive try/except logic and added diagnostic print statements to help trace behavior and node expansions.

What I changed in code:
- Rewrote FooPlayer.__init__ to prefer contender_fn and fallback to base_fn.
- Simplified decide(): materialize iterables, prune robber actions defensively, filter maritime trades only when len(actions) > 8, and evaluate all remaining actions with full lookahead.
- Removed explicit city bonuses and re-ordering.
- Ensured _evaluate_node uses list_prunned_actions and prune_robber_actions defensively, materializes lists, and falls back to the value function on errors.

Notes and next steps:
- Run Evolution 7 to validate whether this restoration recovers the ~53% win rate (16 wins).
- If maritime trades still dominate, we can add a minor negative penalty to MARITIME_TRADE evaluations (small scalar) or experiment with small weight adjustments in contender_fn/DEFAULT_WEIGHTS.
- If node expansions become too costly, consider adding a per-decision transposition cache to avoid duplicated evaluations without pruning actions.

If you want, I can now:
- Add a per-decision transposition table to reduce recomputation (safe, non-pruning optimization).
- Add a small maritime trade penalty during evaluation (tunable) if trades still overvalue.

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

GAME RESULTS:

{
  "Player Summary": {
    "AlphaBetaPlayer:RED(depth=2,value_fn=base_fn,prunning=False)": {
      "WINS": 24,
      "AVG VP": 9.2,
      "AVG SETTLES": 2.6666666666666665,
      "AVG CITIES": 2.5,
      "AVG ROAD": 0.7333333333333333,
      "AVG ARMY": 0.0,
      "AVG DEV VP": 0.06666666666666667
    },
    "FooPlayer:BLUE": {
      "WINS": 6,
      "AVG VP": 6.233333333333333,
      "AVG SETTLES": 2.2333333333333334,
      "AVG CITIES": 1.3,
      "AVG ROAD": 0.26666666666666666,
      "AVG ARMY": 0.23333333333333334,
      "AVG DEV VP": 0.4
    }
  },
  "Game Summary": {
    "AVG TICKS": 203.16666666666666,
    "AVG TURNS": 70.7,
    "AVG DURATION": 3.3479575792948406
  }
}
================================ Human Message =================================

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

---
### **PERFORMANCE SUMMARY**
- **Outcome**: **Poor** (6 wins, 24 losses).
- **VP Comparison**: FooPlayer (6.23 VP) vs. Opponent (9.2 VP), **VP Diff: -2.97**.
- **Key Counts**:
  - Settlements: 2.23
  - Cities: 1.3
  - Roads: 0.27
  - Dev Cards: 0.4
  - Total Turns: 70.7

---
### **VERDICT**
**Poor**: FooPlayer lost 24/30 games with a significant VP deficit of -2.97. The player is underperforming in infrastructure development and long-term strategy.

---
### **LIKELY REASONS**
1. **Overvaluation of Maritime Trades**:
   - The player still selects `MARITIME_TRADE` actions frequently, even when higher-value infrastructure actions (e.g., `BUILD_ROAD`, `BUILD_SETTLEMENT`) are available.
   - **Log Evidence**:
     ```
     FooPlayer.decide: Chosen action=Action(color=<Color.BLUE: 'BLUE'>, action_type=<ActionType.MARITIME_TRADE: 'MARITIME_TRADE'>, value=('SHEEP', 'SHEEP', 'SHEEP', 'SHEEP', 'WOOD')) score=599999938919380.9 node_expansions=41
     ```
   - This suggests the **value function (`contender_fn`)** may still overvalue short-term resource gains.

2. **Insufficient Road Expansion**:
   - The player builds **fewer roads (0.27 avg)** compared to the opponent (0.73 avg), severely limiting expansion and resource access.
   - **Log Evidence**:
     ```
     FooPlayer.decide: Chosen action=Action(color=<Color.BLUE: 'BLUE'>, action_type=<ActionType.BUILD_ROAD: 'BUILD_ROAD'>, value=(18, 40)) score=599999913916467.9 node_expansions=96
     FooPlayer.decide: Chosen action=Action(color=<Color.BLUE: 'BLUE'>, action_type=<ActionType.BUILD_ROAD: 'BUILD_ROAD'>, value=(38, 39)) score=599999938919380.9 node_expansions=42
     ```
   - While roads are occasionally built, the player fails to prioritize them consistently.

3. **Aggressive Robber Pruning**:
   - The player prunes robber actions aggressively, reducing its ability to strategically place the robber or play knights.
   - **Log Evidence**:
     ```
     FooPlayer.decide: Pruned robber actions from 18 to 1
     FooPlayer._evaluate_node: Pruned robber actions from 18 to 1
     ```
   - This limits the player's ability to disrupt opponents or secure resources.

4. **Excessive Node Expansions for Low-Impact Actions**:
   - The player spends excessive computational effort evaluating low-impact actions (e.g., `MARITIME_TRADE`).
   - **Log Evidence**:
     ```
     FooPlayer.decide: Fully evaluating 5 actions with lookahead depth=3
       Action 0: expected_value=599999938916488.9 action_type=ActionType.END_TURN
       Action 1: expected_value=599999938919380.9 action_type=ActionType.MARITIME_TRADE
       Action 2: expected_value=599999938916450.9 action_type=ActionType.MARITIME_TRADE
       Action 3: expected_value=599999938916486.9 action_type=ActionType.MARITIME_TRADE
       Action 4: expected_value=599999938919380.9 action_type=ActionType.MARITIME_TRADE
     FooPlayer.decide: Chosen action=Action(color=<Color.BLUE: 'BLUE'>, action_type=<ActionType.MARITIME_TRADE: 'MARITIME_TRADE'>, value=('SHEEP', 'SHEEP', 'SHEEP', 'SHEEP', 'WOOD')) score=599999938919380.9 node_expansions=41
     ```

---
### **STRATEGIC FLAW**
The player's **value function (`contender_fn`)** and **action prioritization logic** are misaligned, leading to excessive focus on short-term resource trades and insufficient investment in road expansion and disruptive actions (e.g., robber/knight).

---
### **NEXT STEP**
Send to **Strategizer** to **retune the value function (`contender_fn`)** to prioritize road expansion and robber/knight actions over maritime trades, and **adjust the pruning logic** to avoid over-aggressive filtering of robber actions.

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

META THOUGHTS: After deep reflection on our performance trajectory, I see we've hit rock bottom with Evolution 7 (6 wins, 20% - our worst performance yet). We've now tested both value functions and multiple approaches without restoring Evolution 2's success. The persistent maritime trade overvaluation across ALL approaches suggests the fundamental issue isn't value function choice but how we're evaluating game states. Since incremental fixes have failed catastrophically, we need to address the maritime trade problem with explicit hard constraints rather than hoping value functions will naturally avoid them.

META GOAL: Implement explicit hard constraints to eliminate maritime trade overvaluation that has persisted across all evolutions, focusing on infrastructure-first decision making that directly addresses our core strategic flaw rather than relying on value functions to naturally prioritize correctly.

CHOSEN AGENT: STRATEGIZER

AGENT OBJECTIVE: We've reached our worst performance yet (Evolution 7: 6 wins, 20%) after trying both contender_fn and base_fn across multiple approaches. The maritime trade overvaluation problem persists regardless of value function or prioritization scheme, suggesting the issue is fundamental to how we evaluate game states. Since all incremental approaches have failed, design a strategy with explicit hard constraints that eliminates maritime trades from consideration unless absolutely necessary. Create a hierarchical decision system: 1) Always prioritize BUILD_CITY if possible, 2) Then BUILD_SETTLEMENT, 3) Then BUILD_ROAD, 4) Then development cards, 5) Only consider maritime trades if no infrastructure actions exist. This bypasses the value function maritime trade bias entirely through explicit action filtering and hierarchy.
================================ Human Message =================================

STRATEGIZER
**Hard-Constrained Hierarchical Decision Strategy**

### **Core Problem**
Maritime trade overvaluation persists across all evolutions because **value functions inherently favor trades** due to their immediate resource conversion benefits. This has derailed our infrastructure focus, leading to a catastrophic drop in performance (6 wins, 20% in Evolution 7).

### **Solution: Explicit Hard Constraints**
Replace the value function-driven approach with a **strict hierarchical decision system** that enforces infrastructure-first play:

1. **Action Priority Hierarchy**:
   - **Tier 1**: `BUILD_CITY` (highest VP return)
   - **Tier 2**: `BUILD_SETTLEMENT` (VP + expansion)
   - **Tier 3**: `BUILD_ROAD` (expansion + longest road)
   - **Tier 4**: Development cards (knights, VP, monopoly)
   - **Tier 5**: Maritime trades (only if no higher-tier actions exist)

2. **Explicit Filtering**:
   - **Always filter out maritime trades** unless no higher-tier actions are available.
   - **Never evaluate trades** in the lookahead unless absolutely necessary.

3. **Fallback Mechanism**:
   - If no infrastructure actions exist, use `base_fn` to evaluate remaining actions (e.g., robber placement, development cards).

---

### **Implementation Plan**
1. **Replace `decide` with Hard-Constrained Logic**:
   ```python
   def decide(self, game: Game, playable_actions: Iterable) -> object:
       actions = list(playable_actions)
       if not actions:
           return None

       # Tier 1: BUILD_CITY
       city_actions = [a for a in actions if getattr(a, 'action_type', None) == ActionType.BUILD_CITY]
       if city_actions:
           return city_actions[0]  # Prioritize first available city upgrade

       # Tier 2: BUILD_SETTLEMENT
       settlement_actions = [a for a in actions if getattr(a, 'action_type', None) == ActionType.BUILD_SETTLEMENT]
       if settlement_actions:
           return settlement_actions[0]  # Prioritize first available settlement

       # Tier 3: BUILD_ROAD
       road_actions = [a for a in actions if getattr(a, 'action_type', None) == ActionType.BUILD_ROAD]
       if road_actions:
           return road_actions[0]  # Prioritize first available road

       # Tier 4: Development cards
       dev_card_actions = [a for a in actions if getattr(a, 'action_type', None) == ActionType.BUY_DEV_CARD]
       if dev_card_actions:
           return dev_card_actions[0]  # Prioritize first available dev card

       # Tier 5: Fallback to non-trade actions (e.g., robber)
       non_trade_actions = [a for a in actions if getattr(a, 'action_type', None) != ActionType.MARITIME_TRADE]
       if non_trade_actions:
           return non_trade_actions[0]  # Avoid maritime trades

       # Absolute fallback: Use base_fn to evaluate remaining actions
       best_action = max(actions, key=lambda a: self._evaluate_action(game, a))
       return best_action
   ```

2. **Simplify `_evaluate_action` for Fallback**:
   ```python
   def _evaluate_action(self, game: Game, action: Action) -> float:
       game_copy = copy_game(game)
       outcomes = execute_deterministic(game_copy, action)
       expected_value = 0.0
       for (outcome_game, prob) in outcomes:
           expected_value += prob * float(self.value_fn(outcome_game, self.color))
       return expected_value
   ```

3. **Remove Lookahead for Infrastructure Actions**:
   - Since infrastructure actions are explicitly prioritized, **no need for lookahead evaluation** for Tiers 1-4.
   - Only use lookahead for Tier 5 (fallback actions).

---

### **Expected Outcome**
- **Win Rate**: Restore to **~50%+** by eliminating maritime trade overvaluation.
- **VP**: Increase average VP to **~8.5** by focusing on cities and settlements.
- **Robustness**: Avoid catastrophic drops by enforcing infrastructure-first play.

**Next Steps**: Implement this strategy in **Evolution 8** and validate performance.
END STRATEGIZER

--- Output from META at 20250925_044727 ---
================================== Ai Message ==================================

[{'id': 'toolu_01Tu3ysG8NSVVZhCm1P1BWAE', 'input': {'reflection': 'Looking at this strategizer recommendation, I need to carefully consider whether this is the right approach. The strategizer is proposing to completely abandon our lookahead approach and replace it with a simple hierarchy that just picks the first available action in each tier.\n\nWhile I understand the frustration with maritime trade overvaluation, this approach seems too simplistic and could introduce new problems:\n\n1. It completely abandons the sophisticated lookahead that has been a core part of our approach\n2. It picks the "first available" action in each tier without any evaluation of which specific action within that tier is best\n3. It removes all strategic decision-making within action types - for example, which settlement location is best, which road to build, etc.\n4. This approach might work against weak opponents but could be easily exploited by smart players who can predict exactly what we\'ll do\n\nLooking at our performance history more carefully:\n- Evolution 0: 0 wins (baseline)\n- Evolution 1: 11 wins \n- Evolution 2: 16 wins (peak)\n- Evolution 3: 4 wins (major regression)\n- Evolution 4: 8 wins\n- Evolution 5: 13 wins (improvement)\n- Evolution 6: 9 wins (regression with base_fn)\n- Evolution 7: 6 wins (worst performance with "restoration")\n\nThe pattern suggests that Evolution 2 had something specific that worked, but our attempts to recreate it have been unsuccessful. The problem might be that we don\'t actually know what made Evolution 2 successful - we\'re just guessing.\n\nHowever, I also need to consider that we\'ve been stuck in a cycle of trying incremental improvements that haven\'t worked. Maybe a more radical approach is needed, even if it\'s risky.\n\nThe strategizer\'s approach does directly address the maritime trade problem that has been persistent. But I\'m concerned it\'s throwing away the baby with the bathwater.\n\nPerhaps I should ask for a hybrid approach that keeps some strategic evaluation but adds hard constraints against maritime trades?'}, 'name': 'think_tool', 'type': 'tool_use'}]
Tool Calls:
  think_tool (toolu_01Tu3ysG8NSVVZhCm1P1BWAE)
 Call ID: toolu_01Tu3ysG8NSVVZhCm1P1BWAE
  Args:
    reflection: Looking at this strategizer recommendation, I need to carefully consider whether this is the right approach. The strategizer is proposing to completely abandon our lookahead approach and replace it with a simple hierarchy that just picks the first available action in each tier.

While I understand the frustration with maritime trade overvaluation, this approach seems too simplistic and could introduce new problems:

1. It completely abandons the sophisticated lookahead that has been a core part of our approach
2. It picks the "first available" action in each tier without any evaluation of which specific action within that tier is best
3. It removes all strategic decision-making within action types - for example, which settlement location is best, which road to build, etc.
4. This approach might work against weak opponents but could be easily exploited by smart players who can predict exactly what we'll do

Looking at our performance history more carefully:
- Evolution 0: 0 wins (baseline)
- Evolution 1: 11 wins 
- Evolution 2: 16 wins (peak)
- Evolution 3: 4 wins (major regression)
- Evolution 4: 8 wins
- Evolution 5: 13 wins (improvement)
- Evolution 6: 9 wins (regression with base_fn)
- Evolution 7: 6 wins (worst performance with "restoration")

The pattern suggests that Evolution 2 had something specific that worked, but our attempts to recreate it have been unsuccessful. The problem might be that we don't actually know what made Evolution 2 successful - we're just guessing.

However, I also need to consider that we've been stuck in a cycle of trying incremental improvements that haven't worked. Maybe a more radical approach is needed, even if it's risky.

The strategizer's approach does directly address the maritime trade problem that has been persistent. But I'm concerned it's throwing away the baby with the bathwater.

Perhaps I should ask for a hybrid approach that keeps some strategic evaluation but adds hard constraints against maritime trades?
================================= Tool Message =================================
Name: think_tool

Reflection recorded: Looking at this strategizer recommendation, I need to carefully consider whether this is the right approach. The strategizer is proposing to completely abandon our lookahead approach and replace it with a simple hierarchy that just picks the first available action in each tier.

While I understand the frustration with maritime trade overvaluation, this approach seems too simplistic and could introduce new problems:

1. It completely abandons the sophisticated lookahead that has been a core part of our approach
2. It picks the "first available" action in each tier without any evaluation of which specific action within that tier is best
3. It removes all strategic decision-making within action types - for example, which settlement location is best, which road to build, etc.
4. This approach might work against weak opponents but could be easily exploited by smart players who can predict exactly what we'll do

Looking at our performance history more carefully:
- Evolution 0: 0 wins (baseline)
- Evolution 1: 11 wins 
- Evolution 2: 16 wins (peak)
- Evolution 3: 4 wins (major regression)
- Evolution 4: 8 wins
- Evolution 5: 13 wins (improvement)
- Evolution 6: 9 wins (regression with base_fn)
- Evolution 7: 6 wins (worst performance with "restoration")

The pattern suggests that Evolution 2 had something specific that worked, but our attempts to recreate it have been unsuccessful. The problem might be that we don't actually know what made Evolution 2 successful - we're just guessing.

However, I also need to consider that we've been stuck in a cycle of trying incremental improvements that haven't worked. Maybe a more radical approach is needed, even if it's risky.

The strategizer's approach does directly address the maritime trade problem that has been persistent. But I'm concerned it's throwing away the baby with the bathwater.

Perhaps I should ask for a hybrid approach that keeps some strategic evaluation but adds hard constraints against maritime trades?
================================== Ai Message ==================================

META THOUGHTS: After reflection, I'm concerned the strategizer's purely hierarchical approach is too simplistic and abandons our sophisticated lookahead entirely. While maritime trade overvaluation is clearly our core problem, picking the "first available" action in each tier without any evaluation could create new strategic weaknesses. We need a hybrid solution that maintains strategic depth within action types while adding hard constraints against maritime trades.

META GOAL: Implement a hybrid approach that combines hard constraints against maritime trades with maintained strategic evaluation within infrastructure action types, preserving our lookahead capabilities while directly addressing the persistent maritime trade overvaluation problem.

CHOSEN AGENT: STRATEGIZER

AGENT OBJECTIVE: Your hierarchical approach correctly identifies maritime trades as our core problem, but completely abandoning lookahead and picking "first available" actions is too simplistic and could create exploitable patterns. Design a hybrid strategy that: 1) Adds hard constraints to filter OUT maritime trades unless no infrastructure exists (solving our core problem), 2) But maintains strategic evaluation WITHIN infrastructure types using our lookahead - so we still choose the BEST city upgrade, BEST settlement location, BEST road placement, etc., 3) Keep the tier priority (cities > settlements > roads > dev cards) but use value function evaluation to choose between actions within each tier, 4) Only fall back to maritime trades if absolutely no infrastructure actions are available. This gives us the constraint benefits while preserving strategic depth.

