// 1. 定义评分上下文
public struct DecisionContext {
    public Vector3 Origin; // 决策者位置
    public EntityType SelfType; // 决策者类型
    public List<string> PlayerTags; // 玩家当前的流派标签
    // ... 其他共享数据
}

// 2. 评分器接口
public interface IScorer<T> {
    float Evaluate(T candidate, DecisionContext context);
}

// 3. 具体评分器实现：距离评分
public class DistanceScorer : IScorer<Enemy> {
    private float _weight;
    public DistanceScorer(float weight) { _weight = weight; }

    public float Evaluate(Enemy target, DecisionContext context) {
        float dist = Vector3.Distance(context.Origin, target.Position);
        // 距离越近分越高，使用 1/x 曲线
        return (1f / Mathf.Max(dist, 0.1f)) * _weight;
    }
}

// 4. 决策引擎
public class DecisionEngine<T> {
    private List<IScorer<T>> _scorers = new List<IScorer<T>>();

    public void AddScorer(IScorer<T> scorer) { _scorers.Add(scorer); }

    // 模式 A: 选最好的 (用于 AI)
    public T SelectBest(List<T> candidates, DecisionContext context) {
        T bestCandidate = default;
        float bestScore = float.MinValue;

        foreach (var candidate in candidates) {
            float currentScore = 0f;
            foreach (var scorer in _scorers) {
                currentScore += scorer.Evaluate(candidate, context);
            }

            if (currentScore > bestScore) {
                bestScore = currentScore;
                bestCandidate = candidate;
            }
        }
        return bestCandidate;
    }

    // 模式 B: 加权随机 (用于抽卡)
    public T SelectRandom(List<T> candidates, DecisionContext context) {
        // 实现标准的加权随机算法 (Roulette Wheel Selection)
        // ...
        return default;
    }
}