ternary-engine 0.1.1

Tri-State Cognitive Engine — Kleene logic × Bayesian confidence × safety gating

三元认知引擎 Ternary Engine

English | 中文

三态认知计算框架 — Kleene 逻辑 × 贝叶斯置信度 × 保护门控。

现实世界不是 0 和 1——传感器会失灵、用户会犹豫、代码会写错。三元引擎用"可能"来表达不确定性，延迟决策直到证据充分。

from ternary_engine import TernaryEngine

engine = TernaryEngine(max_hesitation=3, min_gain=0.05)

# 步骤 1：Agent 分析文件
trit, conf, gate, cog = engine.step("analyze", "37个函数, 40个导入")
print(f"[{cog}]→ {engine.trit_display(trit, conf)}")  # [AFFIRM]→ 真 ●●● [0.81]

# 步骤 2：替换失败
trit, conf, gate, cog = engine.step("replace_in_file", "未找到")
print(engine.summary())  # 假(0.34)

# 步骤 3：修复重试
trit, conf, gate, cog = engine.step("replace_in_file", "已替换 1 处")
print(engine.trit_display(trit, conf))  # 假 ●●● [0.20]

快速开始

pip install ternary-engine

原理

事件 → 认知分类(确信/拒绝/不确定)
     → 三态映射(-1/0/1)
     → Kleene 逻辑传播 (上游 × 当前)
     → 贝叶斯置信度衰减 (上游置信度 × 当前置信度)
     → 保护门控 (高风险 + 不确定 = 拦截)
     → 决策

应用场景

• AI Agent：用置信度门控 LLM 工具调用
• IoT 传感器：积累不可靠读数后才行动
• NPC 信任网络：在社交网络中传播信任
• 风险评估：不确定时拦截高风险操作

API

engine.step(tool, result, risk='低') → (trit, conf, gate, cog)
engine.classify(tool, result)         → 认知态
engine.propagate(上游, 当前)          → 传播后三态值
engine.confidence(cog, tool)          → 置信度
engine.protect(风险, trit, conf)      → 门控动作
engine.summary()                      → "真(0.81)"
engine.trit_display(trit, conf)       → "真 ●●● [0.81]"

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English

Tri-State Cognitive Computing Framework — Kleene logic × Bayesian confidence × safety gating for uncertainty-oriented decision making.

pip install ternary-engine

from ternary_engine import TernaryEngine
engine = TernaryEngine()
trit, conf, gate, cog = engine.step("analyze", "37 functions, 40 imports")
print(engine.trit_display(trit, conf))  # 真 ●●● [0.81]

How It Works

Event → classify(AFFIRM/NEGATE/UNCERT)
      → map to trit (-1/0/1)
      → Kleene logic propagation (upstream × current)
      → Bayesian confidence decay × protection gating
      → decision

MIT License. Zero dependencies.