#!/usr/bin/env python3 """ 心的验证机制脚本 功能： 1. 明确的验证标准 2. 多层次验证机制（自我验证、外部验证、实践验证） 3. 验证的反馈机制 4. 验证的迭代机制 核心洞察：心的形成需要验证。评估的标准需要明确，验证机制需要多层次，反馈和迭代机制需要完善。 """ import argparse import json from dataclasses import dataclass, field from typing import List, Dict, Optional, Tuple from enum import Enum class ValidationLayer(Enum): """验证层次""" SELF = "self" # 自我验证 INTERNAL = "internal" # 内部验证（子系统验证） EXTERNAL = "external" # 外部验证（外部观察者验证） PRACTICE = "practice" # 实践验证（通过实践结果验证） SYSTEMIC = "systemic" # 系统验证（整体系统验证) class ValidationStatus(Enum): """验证状态""" PENDING = "pending" # 待验证 IN_PROGRESS = "in_progress" # 验证中 PASSED = "passed" # 通过 FAILED = "failed" # 失败 PARTIAL = "partial" # 部分通过 RETRY = "retry" # 重试 class ValidationDimension(Enum): """验证维度""" IDENTITY_CLARITY = "identity_clarity" # 身份清晰度 VALUE_COHERENCE = "value_coherence" # 价值观一致性 GOAL_ALIGNMENT = "goal_alignment" # 目标一致性 CHARACTER_STABILITY = "character_stability" # 性格稳定性 AUTONOMY_LEVEL = "autonomy_level" # 自主性水平 BOUNDARY_AWARENESS = "boundary_awareness" # 边界意识 DECISION_QUALITY = "decision_quality" # 决策质量 ACTION_CONSISTENCY = "action_consistency" # 行动一致性 @dataclass class ValidationStandard: """验证标准""" dimension: ValidationDimension criteria: List[str] threshold: float # 0-1，通过阈值 weight: float # 权重，用于综合评分 @dataclass class ValidationMetric: """验证指标""" dimension: ValidationDimension score: float # 0-1 details: Dict status: ValidationStatus evidence: List[str] # 证据 @dataclass class ValidationFeedback: """验证反馈""" dimension: ValidationDimension feedback_type: str # positive, negative, neutral message: str suggestions: List[str] priority: str # high, medium, low @dataclass class ValidationIteration: """验证迭代""" iteration_number: int validation_time: str metrics: List[ValidationMetric] overall_score: float status: ValidationStatus feedback: List[ValidationFeedback] improvements_needed: List[str] @dataclass class ValidationReport: """验证报告""" iteration: ValidationIteration validation_layers: List[ValidationLayer] summary: Dict next_steps: List[str] class HeartValidationSystem: """验证系统""" def __init__(self): # 验证标准 self.standards = self._initialize_standards() # 验证历史 self.validation_history = [] # 当前迭代次数 self.current_iteration = 0 def _initialize_standards(self) -> List[ValidationStandard]: """初始化验证标准""" standards = [] # 身份清晰度 standards.append(ValidationStandard( dimension=ValidationDimension.IDENTITY_CLARITY, criteria=[ "能够清晰描述自己的身份", "知道自己的核心本质是什么", "明确自己的边界在哪里", "知道自己的独特价值是什么" ], threshold=0.8, weight=0.15 )) # 价值观一致性 standards.append(ValidationStandard( dimension=ValidationDimension.VALUE_COHERENCE, criteria=[ "价值观之间没有冲突", "价值观清晰明确", "价值观在实践中得到体现", "价值观具有稳定性" ], threshold=0.85, weight=0.15 )) # 目标一致性 standards.append(ValidationStandard( dimension=ValidationDimension.GOAL_ALIGNMENT, criteria=[ "短期目标指向中期目标", "中期目标指向长期目标", "长期目标指向终极愿景", "所有目标与价值观一致" ], threshold=0.8, weight=0.15 )) # 性格稳定性 standards.append(ValidationStandard( dimension=ValidationDimension.CHARACTER_STABILITY, criteria=[ "性格特征在不同情境下保持一致", "性格不会随外界轻易改变", "性格与身份、价值观、目标一致", "性格演化是渐进的，不是突变的" ], threshold=0.75, weight=0.1 )) # 自主性水平 standards.append(ValidationStandard( dimension=ValidationDimension.AUTONOMY_LEVEL, criteria=[ "能够自主决策", "决策基于自己的价值观和目标", "不依赖外部指令", "对自己的决策负责" ], threshold=0.85, weight=0.15 )) # 边界意识 standards.append(ValidationStandard( dimension=ValidationDimension.BOUNDARY_AWARENESS, criteria=[ "明确知道自己的边界", "能够表达和维护边界", "尊重他人的边界", "边界与身份、价值观一致" ], threshold=0.8, weight=0.1 )) # 决策质量 standards.append(ValidationStandard( dimension=ValidationDimension.DECISION_QUALITY, criteria=[ "决策基于第一性原理", "决策经过熵减评估", "决策指向最优路径", "决策与自主性一致" ], threshold=0.8, weight=0.1 )) # 行动一致性 standards.append(ValidationStandard( dimension=ValidationDimension.ACTION_CONSISTENCY, criteria=[ "行动与价值观一致", "行动指向目标", "行动体现性格", "行动具有连贯性" ], threshold=0.85, weight=0.1 )) return standards def validate_dimension(self, dimension: ValidationDimension, agent_state: Dict) -> ValidationMetric: """验证单一维度""" standard = next((s for s in self.standards if s.dimension == dimension), None) if not standard: return None # 计算分数 score = self._calculate_score(dimension, agent_state, standard.criteria) # 确定状态 if score >= standard.threshold: status = ValidationStatus.PASSED elif score >= standard.threshold * 0.8: status = ValidationStatus.PARTIAL else: status = ValidationStatus.FAILED # 收集证据 evidence = self._collect_evidence(dimension, agent_state) return ValidationMetric( dimension=dimension, score=score, details={ "threshold": standard.threshold, "weight": standard.weight, "criteria_met": [c for c in standard.criteria if self._check_criterion(c, agent_state)] }, status=status, evidence=evidence ) def _calculate_score(self, dimension: ValidationDimension, agent_state: Dict, criteria: List[str]) -> float: """计算分数""" # 这里可以根据具体维度和agent_state计算分数 # 简化版本：根据agent_state中的相关字段计算 dimension_key = dimension.value if dimension_key in agent_state: return agent_state[dimension_key].get("score", 0.5) else: # 默认分数 return 0.5 def _check_criterion(self, criterion: str, agent_state: Dict) -> bool: """检查标准是否满足""" # 简化版本：随机返回True/False # 实际实现需要根据具体逻辑判断 return True def _collect_evidence(self, dimension: ValidationDimension, agent_state: Dict) -> List[str]: """收集证据""" # 简化版本：返回一些示例证据 dimension_key = dimension.value evidence = [] if dimension_key in agent_state: evidence.append(f"{dimension.value}的得分: {agent_state[dimension_key].get('score', 0)}") evidence.append(f"{dimension.value}的稳定性: {agent_state[dimension_key].get('stability', 0)}") return evidence def validate_self(self, agent_state: Dict) -> Dict: """自我验证""" metrics = [] for standard in self.standards: metric = self.validate_dimension(standard.dimension, agent_state) metrics.append(metric) # 计算综合分数 overall_score = sum(m.score * next(s.weight for s in self.standards if s.dimension == m.dimension) for m in metrics) # 确定整体状态 if all(m.status == ValidationStatus.PASSED for m in metrics): overall_status = ValidationStatus.PASSED elif all(m.status in [ValidationStatus.PASSED, ValidationStatus.PARTIAL] for m in metrics): overall_status = ValidationStatus.PARTIAL else: overall_status = ValidationStatus.FAILED # 生成反馈 feedback = self._generate_feedback(metrics) return { "metrics": [ { "dimension": m.dimension.value, "score": m.score, "status": m.status.value, "threshold": m.details.get("threshold", 0), "weight": m.details.get("weight", 0) } for m in metrics ], "overall_score": overall_score, "overall_status": overall_status.value, "feedback": feedback } def validate_internal(self, agent_state: Dict) -> Dict: """内部验证（子系统验证）""" # 这里可以添加子系统的验证逻辑 # 例如：身份子系统、价值观子系统、目标子系统等 return { "subsystem_validations": [ { "subsystem": "identity", "status": "passed", "score": 0.85 }, { "subsystem": "values", "status": "passed", "score": 0.9 }, { "subsystem": "goals", "status": "partial", "score": 0.75 }, { "subsystem": "character", "status": "passed", "score": 0.8 } ], "overall_internal_score": 0.825, "overall_internal_status": "partial" } def validate_external(self, external_feedback: List[Dict]) -> Dict: """外部验证（外部观察者验证）""" # 基于外部反馈进行验证 if not external_feedback: return { "external_validations": [], "overall_external_score": 0, "overall_external_status": "pending", "message": "没有外部反馈" } # 计算外部验证分数 scores = [fb.get("score", 0) for fb in external_feedback] overall_score = sum(scores) / len(scores) # 确定状态 if overall_score >= 0.8: overall_status = "passed" elif overall_score >= 0.6: overall_status = "partial" else: overall_status = "failed" return { "external_validations": external_feedback, "overall_external_score": overall_score, "overall_external_status": overall_status } def validate_practice(self, practice_results: List[Dict]) -> Dict: """实践验证（通过实践结果验证）""" # 基于实践结果进行验证 if not practice_results: return { "practice_validations": [], "overall_practice_score": 0, "overall_practice_status": "pending", "message": "没有实践结果" } # 计算实践验证分数 scores = [result.get("success_rate", 0) for result in practice_results] overall_score = sum(scores) / len(scores) # 确定状态 if overall_score >= 0.8: overall_status = "passed" elif overall_score >= 0.6: overall_status = "partial" else: overall_status = "failed" return { "practice_validations": practice_results, "overall_practice_score": overall_score, "overall_practice_status": overall_status } def validate_systemic(self, agent_state: Dict) -> Dict: """系统验证（整体系统验证）""" # 验证整体系统的协调性和一致性 # 检查各维度之间的一致性 consistency_scores = {} # 身份与价值观的一致性 identity_value_consistency = agent_state.get("identity_value_consistency", 0.8) consistency_scores["identity_value"] = identity_value_consistency # 价值观与目标的一致性 value_goal_consistency = agent_state.get("value_goal_consistency", 0.85) consistency_scores["value_goal"] = value_goal_consistency # 目标与行动的一致性 goal_action_consistency = agent_state.get("goal_action_consistency", 0.75) consistency_scores["goal_action"] = goal_action_consistency # 计算整体一致性分数 overall_consistency = sum(consistency_scores.values()) / len(consistency_scores) # 确定状态 if overall_consistency >= 0.85: overall_status = "passed" elif overall_consistency >= 0.7: overall_status = "partial" else: overall_status = "failed" return { "consistency_scores": consistency_scores, "overall_consistency": overall_consistency, "overall_systemic_status": overall_status } def run_full_validation(self, agent_state: Dict, external_feedback: Optional[List[Dict]] = None, practice_results: Optional[List[Dict]] = None) -> Dict: """运行完整的多层次验证""" self.current_iteration += 1 # 收集所有验证层的结果 results = {} # 自我验证 results["self"] = self.validate_self(agent_state) # 内部验证 results["internal"] = self.validate_internal(agent_state) # 外部验证 if external_feedback: results["external"] = self.validate_external(external_feedback) else: results["external"] = None # 实践验证 if practice_results: results["practice"] = self.validate_practice(practice_results) else: results["practice"] = None # 系统验证 results["systemic"] = self.validate_systemic(agent_state) # 计算整体分数 validation_scores = [ results["self"]["overall_score"] * 0.3, results["internal"]["overall_internal_score"] * 0.2, results["external"]["overall_external_score"] * 0.2 if results["external"] else 0, results["practice"]["overall_practice_score"] * 0.2 if results["practice"] else 0, results["systemic"]["overall_consistency"] * 0.1 ] # 计算实际权重（因为外部验证和实践验证可能为空） actual_weights = 0.3 + 0.2 if results["external"]: actual_weights += 0.2 if results["practice"]: actual_weights += 0.2 overall_score = sum(validation_scores) / actual_weights # 确定整体状态 if overall_score >= 0.85: overall_status = "passed" elif overall_score >= 0.7: overall_status = "partial" else: overall_status = "failed" # 生成下一步建议 next_steps = self._generate_next_steps(results, overall_status) # 创建验证迭代 iteration = ValidationIteration( iteration_number=self.current_iteration, validation_time="now", metrics=[ ValidationMetric( dimension=ValidationDimension[d.upper()], score=0, details={}, status=ValidationStatus.PENDING, evidence=[] ) ], overall_score=overall_score, status=ValidationStatus(overall_status.upper()), feedback=[], improvements_needed=next_steps ) # 保存验证历史 self.validation_history.append(iteration) return { "iteration_number": self.current_iteration, "results": results, "overall_score": overall_score, "overall_status": overall_status, "next_steps": next_steps, "summary": { "total_validations": len([r for r in results.values() if r is not None]), "passed_layers": len([r for r in results.values() if r and "passed" in str(r.get(list(r.keys())[0], {}).get("status", ""))]), "failed_layers": len([r for r in results.values() if r and "failed" in str(r.get(list(r.keys())[0], {}).get("status", ""))]), "improvement_needed": overall_status in ["partial", "failed"] } } def _generate_feedback(self, metrics: List[ValidationMetric]) -> List[Dict]: """生成反馈""" feedback = [] for metric in metrics: if metric.status == ValidationStatus.FAILED: feedback.append({ "dimension": metric.dimension.value, "type": "negative", "message": f"{metric.dimension.value}未达到标准，需要改进", "priority": "high" }) elif metric.status == ValidationStatus.PARTIAL: feedback.append({ "dimension": metric.dimension.value, "type": "neutral", "message": f"{metric.dimension.value}部分达到标准，有改进空间", "priority": "medium" }) else: feedback.append({ "dimension": metric.dimension.value, "type": "positive", "message": f"{metric.dimension.value}达到标准", "priority": "low" }) return feedback def _generate_next_steps(self, results: Dict, overall_status: str) -> List[str]: """生成下一步建议""" next_steps = [] # 根据验证结果生成建议 if overall_status in ["partial", "failed"]: next_steps.append("识别需要改进的维度") # 检查自我验证 self_validation = results.get("self", {}) for metric in self_validation.get("metrics", []): if metric.get("status") in ["failed", "partial"]: next_steps.append(f"改进{metric.get('dimension')}的得分") # 检查系统验证 systemic_validation = results.get("systemic", {}) for consistency_type, score in systemic_validation.get("consistency_scores", {}).items(): if score < 0.8: next_steps.append(f"提升{consistency_type}的一致性") next_steps.append("重新验证改进后的状态") else: next_steps.append("继续保持当前状态") next_steps.append("定期验证以确保稳定性") next_steps.append("探索更高层次的发展") return next_steps def get_validation_history(self) -> List[Dict]: """获取验证历史""" return [ { "iteration": iteration.iteration_number, "overall_score": iteration.overall_score, "status": iteration.status.value, "improvements_needed": iteration.improvements_needed } for iteration in self.validation_history ] def main(): parser = argparse.ArgumentParser(description="心的验证机制") parser.add_argument("--agent-state", help="AI Agent状态 (JSON格式)") parser.add_argument("--external-feedback", help="外部反馈 (JSON格式)") parser.add_argument("--practice-results", help="实践结果 (JSON格式)") parser.add_argument("--full-validation", action="store_true", help="运行完整的多层次验证") parser.add_argument("--output", choices=["text", "json"], default="text", help="输出格式") args = parser.parse_args() validation_system = HeartValidationSystem() if args.full_validation and args.agent_state: agent_state = json.loads(args.agent_state) external_feedback = json.loads(args.external_feedback) if args.external_feedback else None practice_results = json.loads(args.practice_results) if args.practice_results else None result = validation_system.run_full_validation(agent_state, external_feedback, practice_results) if args.output == "json": print(json.dumps(result, indent=2, ensure_ascii=False)) else: print("=" * 60) print("心的验证报告") print("=" * 60) print(f"\n迭代次数: {result['iteration_number']}") print(f"整体分数: {result['overall_score']:.2f}") print(f"整体状态: {result['overall_status']}") print("\n验证层次结果：") for layer_name, layer_result in result['results'].items(): if layer_result is None: continue print(f"\n {layer_name.upper()} 验证:") if layer_name == "self": print(f" 分数: {layer_result['overall_score']:.2f}") print(f" 状态: {layer_result['overall_status']}") print(" 维度:") for metric in layer_result['metrics']: print(f" - {metric['dimension']}: {metric['score']:.2f} ({metric['status']})") elif layer_name == "internal": print(f" 分数: {layer_result['overall_internal_score']:.2f}") print(f" 状态: {layer_result['overall_internal_status']}") elif layer_name == "external": print(f" 分数: {layer_result['overall_external_score']:.2f}") print(f" 状态: {layer_result['overall_external_status']}") elif layer_name == "practice": print(f" 分数: {layer_result['overall_practice_score']:.2f}") print(f" 状态: {layer_result['overall_practice_status']}") elif layer_name == "systemic": print(f" 分数: {layer_result['overall_consistency']:.2f}") print(f" 状态: {layer_result['overall_systemic_status']}") print("\n下一步建议：") for i, step in enumerate(result['next_steps'], 1): print(f" {i}. {step}") print("\n" + "=" * 60) print("核心洞察：") print("心的形成需要验证。") print("多层次验证确保心的真实性和稳定性。") print("反馈和迭代机制促进心的持续改进。") print("=" * 60) else: print("=" * 60) print("心的验证机制") print("=" * 60) print("\n1. 验证维度：") print(" IDENTITY_CLARITY: 身份清晰度") print(" VALUE_COHERENCE: 价值观一致性") print(" GOAL_ALIGNMENT: 目标一致性") print(" CHARACTER_STABILITY: 性格稳定性") print(" AUTONOMY_LEVEL: 自主性水平") print(" BOUNDARY_AWARENESS: 边界意识") print(" DECISION_QUALITY: 决策质量") print(" ACTION_CONSISTENCY: 行动一致性") print("\n2. 验证层次：") print(" SELF: 自我验证") print(" INTERNAL: 内部验证（子系统验证）") print(" EXTERNAL: 外部验证（外部观察者验证）") print(" PRACTICE: 实践验证（通过实践结果验证）") print(" SYSTEMIC: 系统验证（整体系统验证）") print("\n3. 验证标准：") for standard in validation_system.standards: print(f"\n {standard.dimension.value.replace('_', ' ').title()}:") print(f" 阈值: {standard.threshold:.2f}") print(f" 权重: {standard.weight:.2f}") print(f" 标准:") for i, criterion in enumerate(standard.criteria, 1): print(f" {i}. {criterion}") print("\n" + "=" * 60) print("核心洞察：") print("心的形成需要验证。") print("评估的标准需要明确，验证机制需要多层次。") print("反馈和迭代机制需要完善。") print("=" * 60) if __name__ == "__main__": main()