AI技术 - 智能体技术的未来发展趋势

# 未来智能体技术发展趋势示例 # import numpy as np import torch import torch.nn as nn import torch.optim as optim from typing import Dict import List import Tuple import Any import time import json from abc import ABC import abstractmethod class FutureAgent(ABC): """未来智能体基类""" def __init__(self, agent_id: str, capabilities: List[str]): self.agent_id = agent_id self.capabilities = capabilities self.learning_history = [] self.adaptation_level = 0.0 self.autonomy_level = 0.0 self.collaboration_skills = {} @abstractmethod def perceive_environment(self, environment: Dict) -> Dict: """感知环境""" pass @abstractmethod def make_decision(self, perception: Dict) -> Dict: """做出决策""" pass @abstractmethod def execute_action(self, decision: Dict) -> Dict: """执行动作""" pass def learn_from_experience(self, experience: Dict): """从经验中学习""" self.learning_history.append(experience) self.adaptation_level = min(1.0, self.adaptation_level + 0.01) def collaborate_with_other_agents(self, other_agent: 'FutureAgent', task: Dict) -> Dict: """与其他智能体协作""" collaboration_key = f"{self.agent_id}_{other_agent.agent_id}" if collaboration_key not in self.collaboration_skills: self.collaboration_skills[collaboration_key] = 0.0 # 协作效果评估 # collaboration_effectiveness = self.evaluate_collaboration(other_agent, task) self.collaboration_skills[collaboration_key] = collaboration_effectiveness return { 'collaboration_result': 'success', 'effectiveness': collaboration_effectiveness, 'learned_skills': self.collaboration_skills[collaboration_key] } def evaluate_collaboration(self, other_agent: 'FutureAgent', task: Dict) -> float: """评估协作效果""" # 简化的协作效果评估 # compatibility = len(set(self.capabilities) & set(other_agent.capabilities)) / len(set(self.capabilities) | set(other_agent.capabilities)) task_complexity = task.get('complexity', 0.5) return compatibility * (1.0 - task_complexity) + 0.1 class AGIAgent(FutureAgent): """通用人工智能智能体""" def __init__(self, agent_id: str): super().__init__(agent_id, ['reasoning', 'learning', 'planning', 'communication', 'creativity']) self.knowledge_base = {} self.reasoning_engine = self.build_reasoning_engine() self.creativity_module = self.build_creativity_module() def build_reasoning_engine(self): """构建推理引擎""" return nn.Sequential( nn.Linear(100, 256), nn.ReLU(), nn.Linear(256, 128), nn.ReLU(), nn.Linear(128, 64), nn.ReLU(), nn.Linear(64, 32), nn.Softmax(dim=1) ) def build_creativity_module(self): """构建创造力模块""" return nn.Sequential( nn.Linear(50, 128), nn.ReLU(), nn.Linear(128, 64), nn.ReLU(), nn.Linear(64, 32), nn.Tanh() ) def perceive_environment(self, environment: Dict) -> Dict: """感知环境""" perception = { 'visual_info': environment.get('visual', {}), 'audio_info': environment.get('audio', {}), 'text_info': environment.get('text', {}), 'context_info': environment.get('context', {}), 'temporal_info': environment.get('temporal', {}) } # 多模态信息融合 fused_perception = self.fuse_multimodal_info(perception) return fused_perception def # fuse_multimodal_info(self, perception: Dict) -> Dict: """多模态信息融合""" # 简化的多模态融合 # visual_weight = 0.3 audio_weight = 0.2 text_weight = 0.3 context_weight = 0.2 fused_features = [] if perception['visual_info']: fused_features.extend([visual_weight] * 10) if perception['audio_info']: fused_features.extend([audio_weight] * 10) if perception['text_info']: fused_features.extend([text_weight] * 10) if perception['context_info']: fused_features.extend([context_weight] * 10) return { 'fused_features': fused_features, 'confidence': sum(fused_features) / len(fused_features) if fused_features else 0.0 } def make_decision(self, perception: Dict) -> Dict: """做出决策""" # 推理过程 reasoning_result = # self.reasoning_engine(torch.FloatTensor(perception['fused_features']).unsqueeze(0)) # 创造力应用 # creative_input = perception['fused_features'][:50] # 取前50个特征 creative_output = # self.creativity_module(torch.FloatTensor(creative_input).unsqueeze(0)) # 决策生成 decision = { # 'action_type': 'complex_task', 'reasoning_confidence': float(reasoning_result.max()), 'creativity_score': float(creative_output.mean()), 'decision_quality': float(reasoning_result.max()) * float(creative_output.mean()), 'timestamp': time.time() } return decision def execute_action(self, decision: Dict) -> Dict: """执行动作""" action_result = { 'success': decision['decision_quality'] > 0.5, 'performance': decision['decision_quality'], 'execution_time': time.time() - decision['timestamp'], 'adaptation_learned': True } # 从执行中学习 self.learn_from_experience({ # 'decision': decision, 'result': action_result, 'learning_type': 'execution_feedback' }) return action_result class AutonomousAgent(FutureAgent): """自主智能体""" def __init__(self, agent_id: str, autonomy_level: float = 0.8): super().__init__(agent_id, ['autonomous_planning', 'self_adaptation', 'goal_achievement']) self.autonomy_level = autonomy_level self.goal_stack = [] self.planning_engine = self.build_planning_engine() self.adaptation_mechanism = self.build_adaptation_mechanism() def build_planning_engine(self): """构建规划引擎""" return nn.Sequential( nn.Linear(64, 128), nn.ReLU(), nn.Linear(128, 64), nn.ReLU(), nn.Linear(64, 32), nn.ReLU(), nn.Linear(32, 16) ) def build_adaptation_mechanism(self): """构建自适应机制""" return nn.Sequential( nn.Linear(32, 64), nn.ReLU(), nn.Linear(64, 32), nn.ReLU(), nn.Linear(32, 16), nn.Sigmoid() ) def perceive_environment(self, environment: Dict) -> Dict: """感知环境""" return { 'environment_state': environment, 'goal_relevance': self.assess_goal_relevance(environment), 'autonomy_opportunities': self.identify_autonomy_opportunities(environment) } def make_decision(self, perception: Dict) -> Dict: """做出决策""" # 自主规划 plan = # self.planning_engine(torch.FloatTensor(perception['environment_state']).unsqueeze(0)) # 自适应调整 # adaptation = self.adaptation_mechanism(torch.FloatTensor(perception['goal_relevance']).unsqueeze(0)) decision = { 'action_type': 'autonomous_action', 'plan': plan.detach().numpy().tolist(), 'adaptation': adaptation.detach().numpy().tolist(), 'autonomy_level': self.autonomy_level, 'goal_progress': perception['goal_relevance'] } return decision def execute_action(self, decision: Dict) -> Dict: """执行动作""" # 自主执行 execution_result = { 'autonomous_execution': True, 'plan_followed': True, # 'adaptation_applied': True, 'goal_progress': decision['goal_progress'], 'autonomy_demonstrated': self.autonomy_level } # 更新自主性水平 if execution_result['goal_progress'] > 0.8: # self.autonomy_level = min(1.0, self.autonomy_level + 0.01) return execution_result def assess_goal_relevance(self, environment: Dict) -> List[float]: """评估目标相关性""" # 简化的目标相关性评估 # return [0.8, 0.6, 0.9, 0.7, 0.5, 0.8, 0.6, 0.9] def identify_autonomy_opportunities(self, environment: Dict) -> List[str]: """识别自主性机会""" return ['planning', 'execution', 'adaptation', 'learning'] class CollaborativeAgent(FutureAgent): """协作智能体""" def __init__(self, agent_id: str): super().__init__(agent_id, ['communication', 'coordination', 'negotiation', 'teamwork']) self.communication_protocols = {} self.team_members = [] self.coordination_strategies = {} def perceive_environment(self, environment: Dict) -> Dict: """感知环境""" return { 'team_status': environment.get('team_status', {}), 'communication_channels': environment.get('communication', {}), 'collaboration_opportunities': environment.get('collaboration', {}) } def make_decision(self, perception: Dict) -> Dict: """做出决策""" # 协作决策 collaboration_decision = { 'action_type': # 'collaborative_action', 'team_coordination': True, 'communication_required': True, 'negotiation_needed': perception['collaboration_opportunities'].get('conflict', False), 'teamwork_level': len(self.team_members) / 10.0 } return collaboration_decision def execute_action(self, decision: Dict) -> Dict: """执行动作""" # 协作执行 collaboration_result = { 'team_coordination_success': True, # 'communication_effectiveness': 0.9, 'negotiation_outcome': 'agreement', 'teamwork_quality': decision['teamwork_level'], 'collaborative_learning': True } return collaboration_result class FutureAgentSystem: """未来智能体系统""" def __init__(self): self.agents = {} self.system_capabilities = [] self.emergence_properties = {} def add_agent(self, agent: FutureAgent): """添加智能体""" self.agents[agent.agent_id] = agent self.update_system_capabilities() def update_system_capabilities(self): """更新系统能力""" all_capabilities = set() for agent in self.agents.values(): all_capabilities.update(agent.capabilities) self.system_capabilities = list(all_capabilities) def simulate_agent_interaction(self, agent1_id: str, agent2_id: str, task: Dict) -> Dict: """模拟智能体交互""" agent1 = self.agents[agent1_id] agent2 = self.agents[agent2_id] # 协作交互 # collaboration_result = agent1.collaborate_with_other_agents(agent2, task) # 系统涌现性 emergence_effect # = self.calculate_emergence_effect(agent1, agent2, task) return { 'collaboration_result': collaboration_result, 'emergence_effect': emergence_effect, 'system_learning': True } def calculate_emergence_effect(self, agent1: FutureAgent, agent2: FutureAgent, task: Dict) -> Dict: """计算涌现效应""" # 简化的涌现效应计算 synergy = len(set(agent1.capabilities) & # set(agent2.capabilities)) / len(set(agent1.capabilities) | set(agent2.capabilities)) return { 'synergy_level': synergy, 'emergence_quality': synergy * task.get('complexity', 0.5), 'collective_intelligence': synergy * 1.5 } def predict_future_trends(self) -> Dict: """预测未来趋势""" return { 'agi_development': { 'timeline': '2030-2040', 'probability': 0.7, 'key_technologies': ['transformer', 'reinforcement_learning', 'multimodal_ai'] }, 'autonomous_agents': { 'timeline': '2025-2030', 'probability': 0.9, 'key_technologies': ['self_supervised_learning', 'meta_learning', 'few_shot_learning'] }, 'collaborative_ai': { 'timeline': '2025-2035', 'probability': 0.8, 'key_technologies': ['multi_agent_systems', 'federated_learning', 'swarm_intelligence'] }, 'quantum_ai': { 'timeline': '2030-2045', 'probability': 0.6, 'key_technologies': ['quantum_machine_learning', 'quantum_optimization', 'quantum_neural_networks'] } } # 使用示例 def main(): print("智能体技术未来发展趋势分析:") # # 创建未来智能体系统 future_system = FutureAgentSystem()# 创建不同类型的智能体 agi_agent = # AGIAgent("agi_001") autonomous_agent = AutonomousAgent("autonomous_001", autonomy_level=0.9) collaborative_agent = CollaborativeAgent("collaborative_001") # 添加到系统 # future_system.add_agent(agi_agent) future_system.add_agent(autonomous_agent) future_system.add_agent(collaborative_agent) print(f"\n系统能力: {future_system.system_capabilities}") # 模拟智能体交互 task = { 'type': 'complex_problem_solving', 'complexity': 0.8, 'requires_collaboration': # True } interaction_result = future_system.simulate_agent_interaction("agi_001", "autonomous_001", task) print(f"\n智能体交互结果: {interaction_result}") # 预测未来趋势 future_trends = # future_system.predict_future_trends() print(f"\n未来趋势预测:") for trend, details in future_trends.items(): print(f" {trend}: {details['timeline']} (概率: {details['probability']})") # 测试AGI智能体 print(f"\nAGI智能体测试:") environment = { 'visual': {'objects': ['car', 'person', # 'building']}, 'audio': {'sounds': ['traffic', 'conversation']}, 'text': {'content': 'Help me solve this problem'}, 'context': {'location': 'urban', 'time': 'day'}, 'temporal': {'duration': 300} } perception = agi_agent.perceive_environment(environment) decision = agi_agent.make_decision(perception) action_result = agi_agent.execute_action(decision) print(f"感知结果: {perception['confidence']:.2f}") print(f"决策质量: {decision['decision_quality']:.2f}") print(f"执行成功: {action_result['success']}") print("\n智能体技术未来发展趋势分析完成！") if __name__ == "__main__": main()