map-optimization-strategy: Data & Analytics for AI Agents | OpenAgentSkill

Overview

Strategy for solving constraint optimization problems on spatial maps. Use when you need to place items on a grid/map to maximize some objective while satisfying constraints.

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Map-Based Constraint Optimization Strategy

A systematic approach to solving placement optimization problems on spatial maps. This applies to any problem where you must place items on a grid to maximize an objective while respecting placement constraints.

Why Exhaustive Search Fails

Exhaustive search (brute-force enumeration of all possible placements) is the worst approach:

Combinatorial explosion: Placing N items on M valid tiles = O(M^N) combinations
Even small maps become intractable (e.g., 50 tiles, 5 items = 312 million combinations)
Most combinations are clearly suboptimal or invalid

The Three-Phase Strategy

Phase 1: Prune the Search Space

Goal: Eliminate tiles that cannot contribute to a good solution.

Remove tiles that are:

Invalid for any placement - Violate hard constraints (wrong terrain, out of range, blocked)
Dominated - Another tile is strictly better in all respects
Isolated - Too far from other valid tiles to form useful clusters

Before: 100 tiles in consideration
After pruning: 20-30 candidate tiles

This alone can reduce search space by 70-90%.

Phase 2: Identify High-Value Spots

Goal: Find tiles that offer exceptional value for your objective.

Score each remaining tile by:

Intrinsic value - What does this tile contribute on its own?
Adjacency potential - What bonuses from neighboring tiles?
Cluster potential - Can this tile anchor a high-value group?

Rank tiles and identify the top candidates. These are your priority tiles - any good solution likely includes several of them.

Example scoring:
- Tile A: +4 base, +3 adjacency potential = 7 points (HIGH)
- Tile B: +1 base, +1 adjacency potential = 2 points (LOW)

Phase 3: Anchor Point Search

Goal: Find placements that capture as many high-value spots as possible.

Select anchor candidates - Tiles that enable access to multiple high-value spots
Expand from anchors - Greedily add placements that maximize marginal value
Validate constraints - Ensure all placements satisfy requirements
Local search - Try swapping/moving placements to improve the solution

For problems with a "center" constraint (e.g., all placements within range of a central point):

The anchor IS the center - try different center positions
For each center, the reachable high-value tiles are fixed
Optimize placement within each center's reach

Algorithm Skeleton

def optimize_placements(map_tiles, constraints, num_placements):
    # Phase 1: Prune
    candidates = [t for t in map_tiles if is_valid_tile(t, constraints)]

    # Phase 2: Score and rank
    scored = [(tile, score_tile(tile, candidates)) for tile in candidates]
    scored.sort(key=lambda x: -x[1])  # Descending by score
    high_value = scored[:top_k]

    # Phase 3: Anchor search
    best_solution = None
    best_score = 0

    for anchor in get_anchor_candidates(high_value, constraints):
        solution = greedy_expand(anchor, candidates, num_placements, constraints)
        solution = local_search(solution, candidates, constraints)

        if solution.score > best_score:
            best_solution = solution
            best_score = solution.score

    return best_solution

Key Insights

Prune early, prune aggressively - Every tile removed saves exponential work later
High-value tiles cluster - Good placements tend to be near other good placements (adjacency bonuses compound)
Anchors constrain the search - Once you fix an anchor, many other decisions follow logically
Greedy + local search is often sufficient - You don't need the global optimum; a good local optimum found quickly beats a perfect solution found slowly
Constraint propagation - When you place one item, update what's valid for remaining items immediately

Common Pitfalls

Ignoring interactions - Placing item A may change the value of placing item B (adjacency effects, mutual exclusion)
Over-optimizing one metric - Balance intrinsic value with flexibility for remaining placements
Forgetting to validate - Always verify final solution satisfies ALL constraints

View source ↗

File metadata

name: map-optimization-strategy
description: Strategy for solving constraint optimization problems on spatial maps. Use when you need to place items on a grid/map to maximize some objective while satisfying constraints.

View original text

---
name: map-optimization-strategy
description: Strategy for solving constraint optimization problems on spatial maps. Use when you need to place items on a grid/map to maximize some objective while satisfying constraints.
---

# Map-Based Constraint Optimization Strategy

## Why Exhaustive Search Fails

Exhaustive search (brute-force enumeration of all possible placements) is the **worst approach**:

- Combinatorial explosion: Placing N items on M valid tiles = O(M^N) combinations
- Even small maps become intractable (e.g., 50 tiles, 5 items = 312 million combinations)
- Most combinations are clearly suboptimal or invalid

## The Three-Phase Strategy

### Phase 1: Prune the Search Space

**Goal:** Eliminate tiles that cannot contribute to a good solution.

Remove tiles that are:
1. **Invalid for any placement** - Violate hard constraints (wrong terrain, out of range, blocked)
2. **Dominated** - Another tile is strictly better in all respects
3. **Isolated** - Too far from other valid tiles to form useful clusters

```
Before: 100 tiles in consideration
After pruning: 20-30 candidate tiles
```

This alone can reduce search space by 70-90%.

### Phase 2: Identify High-Value Spots

**Goal:** Find tiles that offer exceptional value for your objective.

Score each remaining tile by:
1. **Intrinsic value** - What does this tile contribute on its own?
2. **Adjacency potential** - What bonuses from neighboring tiles?
3. **Cluster potential** - Can this tile anchor a high-value group?

Rank tiles and identify the top candidates. These are your **priority tiles** - any good solution likely includes several of them.

```
Example scoring:
- Tile A: +4 base, +3 adjacency potential = 7 points (HIGH)
- Tile B: +1 base, +1 adjacency potential = 2 points (LOW)
```

### Phase 3: Anchor Point Search

**Goal:** Find placements that capture as many high-value spots as possible.

1. **Select anchor candidates** - Tiles that enable access to multiple high-value spots
2. **Expand from anchors** - Greedily add placements that maximize marginal value
3. **Validate constraints** - Ensure all placements satisfy requirements
4. **Local search** - Try swapping/moving placements to improve the solution

For problems with a "center" constraint (e.g., all placements within range of a central point):
- The anchor IS the center - try different center positions
- For each center, the reachable high-value tiles are fixed
- Optimize placement within each center's reach

## Algorithm Skeleton

```python
def optimize_placements(map_tiles, constraints, num_placements):
# Phase 1: Prune
candidates = [t for t in map_tiles if is_valid_tile(t, constraints)]

# Phase 2: Score and rank
scored = [(tile, score_tile(tile, candidates)) for tile in candidates]
scored.sort(key=lambda x: -x[1]) # Descending by score
high_value = scored[:top_k]

# Phase 3: Anchor search
best_solution = None
best_score = 0

for anchor in get_anchor_candidates(high_value, constraints):
solution = greedy_expand(anchor, candidates, num_placements, constraints)
solution = local_search(solution, candidates, constraints)

if solution.score > best_score:
best_solution = solution
best_score = solution.score

return best_solution
```

## Key Insights

1. **Prune early, prune aggressively** - Every tile removed saves exponential work later

2. **High-value tiles cluster** - Good placements tend to be near other good placements (adjacency bonuses compound)

3. **Anchors constrain the search** - Once you fix an anchor, many other decisions follow logically

4. **Greedy + local search is often sufficient** - You don't need the global optimum; a good local optimum found quickly beats a perfect solution found slowly

5. **Constraint propagation** - When you place one item, update what's valid for remaining items immediately

## Common Pitfalls

- **Ignoring interactions** - Placing item A may change the value of placing item B (adjacency effects, mutual exclusion)
- **Over-optimizing one metric** - Balance intrinsic value with flexibility for remaining placements
- **Forgetting to validate** - Always verify final solution satisfies ALL constraints

Review the source

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Review before install: Avoid automatic install

License: MIT

Potential broker, wallet, exchange, or real-money execution surface; sandbox and explicit approval are required
AI review approval is missing
This skill may touch real-money trading, broker, wallet, or exchange operations; use only in a sandbox with explicit approval.
Quality score needs review
GitHub adoption: 55 GitHub stars
Stars/forks activity: 55 stars, 3 forks; issue activity unavailable in current metadata
Review status: AI review approval is missing

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Source repository: xuansenpa1/skillrevise
License: MIT
Version: 1.0.0
Last GitHub push: Sep 5, 2026
Registry updated: Sep 8, 2026
Instruction path: data/skillsbench/tasks/civ6-adjacency-optimizer/environment/skills/map-optimization-strategy/SKILL.md @ fb8042ac2415

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