🟢 从“规则集合系统” → “规则语言 + 可解释执行内核”
你现在的问题已经出现了:
如果不做DSL:
❌ 规则会失控
❌ 无法维护
❌ 无法自动分析
🟢 Rule DSL(规则描述语言)
RULE risk_control:
PRIORITY: 100
WHEN:
risk_level > 0.7
THEN:
ACTION: BLOCK
SCORE: -1.0
RULE confidence_boost:
PRIORITY: 60
WHEN:
validator_score > 0.8
THEN:
SCORE: +0.3
RULE safe_preference:
PRIORITY: 80
WHEN:
risk_level < 0.3 AND state_match > 0.7
THEN:
SCORE: +0.5
🟢 统一规则解释执行器(Rule Engine Kernel)
FOR each candidate:
APPLY all rules
SUM score effects
OUTPUT final score
Score(ai)=∑kRulek(ai)+w1Sstate+w2Svalid−w3SriskScore(a_i)=\sum_k Rule_k(a_i) + w_1 S_{state} + w_2 S_{valid} – w_3 S_{risk}
👉 变化:
CONTROL KERNEL
├── Rule Engine (DSL解析执行)
├── Decision Engine (评分/概率)
├── Validation Engine
├── Execution Gate
INPUT
↓
WEB
↓
TSPR
↓
LLM (候选生成)
↓
CONTROL KERNEL
├── RULE DSL Engine
├── GPS Decision Engine
├── VALIDATOR
↓
HUMAN CORE
↓
ACTION
↓
FEEDBACK
↓
LEARNING + OPTIMIZATION ENGINE
↓
RULE DSL UPDATE(需审批)
建议“改规则”
🟢 生成DSL规则变更草案
SUGGESTION:
MODIFY RULE risk_control:
SCORE: -1.0 → -1.3
CONFIDENCE: 0.82
RULESET v1 → v2 → v3
半自动优化系统
🟢 规则语言驱动的AI控制内核系统
dlos/
├── kernel/
│ ├── rule_engine.py
│ ├── dsl_parser.py
│ ├── decision_engine.py
│ ├── validator.py
│
├── llm/
├── tspr/
├── feedback/
├── learning/
├── optimization/
├── human/
└── engine.py
🟢 “AI Control Kernel Prototype”
🟢 DLOS v0.5 通过引入规则DSL与控制内核,将原有的规则系统升级为可解析、可执行、可演化的统一决策语言体系,使整个AI系统从“流程控制”跃迁为“规则驱动的控制内核架构”。
你现在可以走两条路线:
如果你说:
👉 做v0.6(内核系统)
我可以直接帮你推进到:
🧠 接近“AI操作系统内核架构”的设计层 🚀