AI-Scientist：2025年全自动科学发现系统的革命性突破

# 观察模块核心代码示例
class ObservationModule:
    def __init__(self, knowledge_graph, data_sources):
        self.knowledge_graph = knowledge_graph
        self.data_sources = data_sources
        self.feature_extractor = MultiModalFeatureExtractor()
        self.anomaly_detector = AnomalyDetector()
    
    def observe(self, domain, time_range=None):
        # 1. 收集多源数据
        raw_data = self._collect_data(domain, time_range)
        
        # 2. 提取特征
        features = self.feature_extractor.extract(raw_data)
        
        # 3. 检测异常和新模式
        anomalies = self.anomaly_detector.detect(features)
        
        # 4. 与知识图谱关联
        insights = self._link_to_knowledge_graph(anomalies, features)
        
        return insights
    
    def _collect_data(self, domain, time_range):
        # 实现多源数据收集逻辑
        pass
    
    def _link_to_knowledge_graph(self, anomalies, features):
        # 实现知识图谱关联逻辑
        pass

# 假设生成模块核心代码示例
class HypothesisGenerator:
    def __init__(self, knowledge_graph, llm_model):
        self.knowledge_graph = knowledge_graph
        self.llm_model = llm_model
        self.symbolic_reasoner = SymbolicReasoner()
    
    def generate_hypotheses(self, insights, domain):
        # 1. 基于大语言模型生成初始假设
        initial_hypotheses = self._generate_initial_hypotheses(insights, domain)
        
        # 2. 符号推理验证假设逻辑性
        validated_hypotheses = self._validate_hypotheses(initial_hypotheses)
        
        # 3. 假设评分和排序
        scored_hypotheses = self._score_hypotheses(validated_hypotheses)
        
        return scored_hypotheses[:5]  # 返回Top 5假设
    
    def _generate_initial_hypotheses(self, insights, domain):
        # 使用大语言模型生成初始假设
        prompt = self._build_prompt(insights, domain)
        return self.llm_model.generate(prompt)
    
    def _validate_hypotheses(self, hypotheses):
        # 使用符号推理验证假设逻辑性
        validated = []
        for hyp in hypotheses:
            if self.symbolic_reasoner.is_logical(hyp):
                validated.append(hyp)
        return validated
    
    def _score_hypotheses(self, hypotheses):
        # 基于多个维度评分假设
        scored = []
        for hyp in hypotheses:
            score = {
                'novelty': self._calculate_novelty(hyp),
                'plausibility': self._calculate_plausibility(hyp),
                'testability': self._calculate_testability(hyp)
            }
            total_score = sum(score.values()) / len(score)
            scored.append((hyp, total_score))
        return sorted(scored, key=lambda x: x[1], reverse=True)

# 实验设计模块核心代码示例
class ExperimentDesigner:
    def __init__(self, experimental_space, resource_constraints):
        self.experimental_space = experimental_space
        self.resource_constraints = resource_constraints
        self.bayesian_optimizer = BayesianOptimizer()
        self.reinforcement_learner = ReinforcementLearner()
    
    def design_experiment(self, hypothesis, prior_results=None):
        # 1. 定义实验目标和指标
        objectives = self._define_objectives(hypothesis)
        metrics = self._define_metrics(hypothesis)
        
        # 2. 构建实验空间
        space = self._construct_experimental_space(hypothesis)
        
        # 3. 应用贝叶斯优化生成初始实验设计
        initial_design = self.bayesian_optimizer.optimize(
            objectives, space, prior_results
        )
        
        # 4. 考虑资源约束调整设计
        final_design = self._apply_constraints(initial_design)
        
        return final_design
    
    def _define_objectives(self, hypothesis):
        # 定义实验目标
        pass
    
    def _define_metrics(self, hypothesis):
        # 定义实验指标
        pass
    
    def _construct_experimental_space(self, hypothesis):
        # 构建实验空间
        pass
    
    def _apply_constraints(self, design):
        # 应用资源约束
        pass

# 克隆仓库
git clone https://github.com/GitHub_Trending/ai/AI-Scientist.git
cd AI-Scientist

# 创建虚拟环境
python -m venv venv
source venv/bin/activate  # Linux/macOS
venv\Scripts\activate  # Windows

# 安装依赖
pip install -r requirements.txt

# 配置环境变量
export AI_SCIENTIST_API_KEY="your-api-key"
export AI_SCIENTIST_DATA_DIR="./data"

# 启动服务
python -m ai_scientist serve