静态名词系统：

# 传统方式：固定行为树
class StaticGameAI:
    def decide_action(self, game_state):
        if game_state['health'] < 0.3:
            return "flee"
        elif game_state['has_target']:
            return "attack"
        return "patrol"

动态动词系统

# 新方式：基于环境条件动态调整策略
class DynamicGameAI:
    def __init__(self):
        self.behavior_weights = {
            'aggressive': 0.5,
            'defensive': 0.3,
            'explorative': 0.2
        }
        self.adaptation_rate = 0.1
    
    def decide_dynamic_action(self, game_state, history):
        # 计算当前环境条件下的最优行为混合
        current_conditions = self.analyze_environmental_conditions(game_state)
        
        # 基于历史结果调整行为权重（学习/适应）
        self.adapt_behavior_weights(history, current_conditions)
        
        # 生成基于当前条件的行为概率分布
        action_probs = self.calculate_action_probabilities(current_conditions)
        
        # 选择最能适应当前条件的行为
        chosen_action = self.select_adaptive_action(action_probs, game_state)
        
        return {
            'action': chosen_action,
            'strategy_mix': self.behavior_weights,
            'adaptation_reason': current_conditions['dominant_factor']
        }
    
    def analyze_environmental_conditions(self, game_state):
        conditions = {}
        
        # 分析各种环境因素（条件）
        conditions['threat_level'] = self.calculate_threat_level(game_state)
        conditions['resource_availability'] = self.assess_resources(game_state)
        conditions['strategic_advantage'] = self.evaluate_advantage(game_state)
        
        # 找出主导条件
        dominant_factor = max(conditions, key=conditions.get)
        conditions['dominant_factor'] = dominant_factor
        
        return conditions
    
    def adapt_behavior_weights(self, history, current_conditions):
        # 基于历史结果调整行为：成功的策略权重增加
        recent_successes = self.analyze_recent_outcomes(history)
        
        for strategy, success_rate in recent_successes.items():
            adjustment = success_rate * self.adaptation_rate
            self.behavior_weights[strategy] += adjustment
        
        # 归一化权重
        total = sum(self.behavior_weights.values())
        self.behavior_weights = {k: v/total for k, v in self.behavior_weights.items()}