六大多Agent共识协议：从投票对决到涌现自组织全栈拆解

# 生产级多数投票实现
class MajorityVotingConsensus:
    def __init__(self, agents: list[Agent], quorum: float = 0.6):
        self.agents = agents
        self.quorum = quorum  # 通过阈值，默认60%

    async def decide(self, question: str) -> ConsensusResult:
        # 并行收集所有Agent的独立回答
        responses = await asyncio.gather(*[
            agent.answer(question) for agent in self.agents
        ])

        # 语义聚类而非精确匹配
        clusters = self._semantic_cluster(responses)

        # 选择最大簇
        winning_cluster = max(clusters, key=lambda c: len(c))
        confidence = len(winning_cluster) / len(responses)

        if confidence >= self.quorum:
            return ConsensusResult(
                answer=winning_cluster.representative,
                confidence=confidence,
                dissenting=[r for r in responses if r not in winning_cluster]
            )
        else:
            # 未达法定人数，降级到辩论协议
            return await self._escalate_to_debate(responses)

Round 1: 各Agent独立生成初始答案
Round 2: 每个Agent看到其他Agent的答案，生成反驳/修订
Round 3: 基于反驳进行第二轮修订
...
Round N: 最终投票或仲裁

# 辩论协议核心循环
class DebateConsensus:
    def __init__(self, agents, max_rounds=5, convergence_threshold=0.95):
        self.agents = agents
        self.max_rounds = max_rounds
        self.threshold = convergence_threshold

    async def debate(self, question: str):
        # Round 1: 独立初始回答
        positions = {i: await a.initial_answer(question)
                     for i, a in enumerate(self.agents)}

        history = [positions.copy()]

        for round_num in range(2, self.max_rounds + 1):
            new_positions = {}
            for i, agent in enumerate(self.agents):
                # 构建辩论上下文：其他Agent的观点
                others = {j: pos for j, pos in positions.items() if j != i}
                debate_ctx = self._format_debate_context(others, round_num)

                new_positions[i] = await agent.revise(
                    question, debate_ctx, positions[i]
                )

            # 检查收敛
            if self._has_converged(positions, new_positions):
                break

            positions = new_positions
            history.append(positions.copy())

        return self._final_vote(positions), history

# 结构化审议协议
class DeliberativeConsensus:
    def __init__(self, agents, moderator: Agent, diagnostician: Agent):
        self.agents = agents
        self.moderator = moderator    # 主席Agent
        self.diagnostician = diagnostician  # 分歧诊断Agent

    async def deliberate(self, question: str):
        # Phase 1: 立场陈述（每Agent限时发言）
        statements = await self._opening_statements(question)

        # Phase 2: 分歧诊断
        disagreement = await self.diagnostician.analyze(
            question=question,
            positions=statements,
            dimensions=["factual", "value", "interpretation"]
        )

        # Phase 3: 针对性审议
        for axis in disagreement.sorted_axes():
            if axis.type == "factual":
                # 事实分歧 → 引入外部知识源裁决
                await self._fact_check_round(axis)
            elif axis.type == "value":
                # 价值观分歧 → 构建价值排序共识
                await self._value_alignment_round(axis)
            else:
                # 理解分歧 → 术语对齐和语境澄清
                await self._clarification_round(axis)

        # Phase 4: 起草共识声明
        draft = await self.moderator.draft_consensus(statements)
        return await self._unanimous_approval(draft)

# 仲裁者裁决协议
class ArbiterConsensus:
    def __init__(self, proposers: list[Agent], arbiter: Agent):
        self.proposers = proposers
        self.arbiter = arbiter

    async def decide(self, question: str):
        # Step 1: 并行收集提案
        proposals = await asyncio.gather(*[
            p.propose(question) for p in self.proposers
        ])

        # Step 2: 仲裁者评估
        verdict = await self.arbiter.judge(
            question=question,
            proposals=[
                {"agent_id": i, "answer": p.answer, "reasoning": p.chain_of_thought}
                for i, p in enumerate(proposals)
            ],
            criteria=["accuracy", "completeness", "safety"]
        )

        return ConsensusResult(
            answer=verdict.selected_answer,
            confidence=verdict.confidence,
            reasoning=verdict.reasoning
        )

# 加权共识协议
class WeightedConsensus:
    def __init__(self, agents: list[Agent], meta_evaluator: Agent):
        self.agents = agents
        self.meta_evaluator = meta_evaluator

    async def decide(self, question: str):
        # Phase 1: 收集答案和置信度
        responses = await asyncio.gather(*[
            agent.answer_with_confidence(question) for agent in self.agents
        ])

        # Phase 2: 元评估器动态分配权重
        weights = await self.meta_evaluator.assign_weights(
            question=question,
            agent_profiles=[
                {
                    "id": i,
                    "specialties": a.specialties,
                    "self_confidence": r.confidence,
                    "reasoning_depth": r.reasoning_tokens
                }
                for i, (a, r) in enumerate(zip(self.agents, responses))
            ]
        )

        # Phase 3: 加权聚合
        weighted_scores = defaultdict(float)
        for response, weight in zip(responses, weights):
            cluster_id = self._find_cluster(response, weighted_scores)
            weighted_scores[cluster_id] += weight * response.confidence

        best_cluster = max(weighted_scores, key=weighted_scores.get)
        return ConsensusResult(
            answer=best_cluster.representative,
            confidence=weighted_scores[best_cluster] / sum(weights),
            weights=weights
        )

# 涌现自组织框架（概念实现）
class EmergentSelfOrganization:
    def __init__(self, agents, environment, emergence_detector):
        self.agents = agents
        self.environment = environment
        self.detector = emergence_detector

    async def run(self, task, max_steps=100):
        emergent_patterns = []

        for step in range(max_steps):
            # Agent自由交互，无中心控制
            interactions = await self._free_interaction(step)

            # 检测涌现行为
            patterns = self.detector.detect(interactions)
            if patterns.new_patterns:
                emergent_patterns.extend(patterns.new_patterns)
                # 关键：不干预涌现，只观察记录

            # 检查是否自发形成了共识
            if self._has_spontaneous_consensus():
                break

            self.environment.update(interactions)

        return EmergenceReport(
            emergent_patterns=emergent_patterns,
            consensus_reached=...,
            stability_metrics=...
        )

graph TB
    subgraph "多Agent共识协议全景"
        Q[决策需求] --> V[多数投票]
        Q --> D[多轮辩论]
        Q --> DC[结构化审议]
        Q --> A[仲裁者裁决]
        Q --> W[加权共识]
        Q --> E[涌现自组织]

        V --> |"简单快速"| P1[事实性问答<br/>代码生成]
        D --> |"深度推理"| P2[数学证明<br/>逻辑推理]
        DC --> |"高风险决策"| P3[医疗诊断<br/>法律判断]
        A --> |"效率优先"| P4[客服分流<br/>内容审核]
        W --> |"混合专才"| P5[跨域分析<br/>投资决策]
        E --> |"探索发现"| P6[研究前沿<br/>策略演化]
    end

    subgraph "复杂度递增"
        V --> |"+1.0x"| D
        D --> |"+1.5x"| A
        A --> |"+2.0x"| W
        W --> |"+3.0x"| DC
        DC --> |"不可控"| E
    end

flowchart LR
    Start[多Agent决策需求] --> Q1{问题有无<br/>客观标准?}

    Q1 --> |是| Q2{预算是否<br/>敏感?}
    Q1 --> |否| Q3{能容忍<br/>不确定性?}

    Q2 --> |是| V[多数投票]
    Q2 --> |否| Q4{需要可解释的<br/>决策过程?}

    Q4 --> |是| DC[结构化审议]
    Q4 --> |否| D[多轮辩论]

    Q3 --> |是| E[涌现自组织]
    Q3 --> |否| Q5{有无领域<br/>专家Agent?}

    Q5 --> |是| W[加权共识]
    Q5 --> |否| A[仲裁者裁决]

    style V fill:#4CAF50,color:#fff
    style D fill:#2196F3,color:#fff
    style DC fill:#FF9800,color:#fff
    style A fill:#9C27B0,color:#fff
    style W fill:#00BCD4,color:#fff
    style E fill:#F44336,color:#fff