第四十四章 学习引擎源码实现WSaiOS Cognitive Learning Engine
第四十四章
WSaiOS Cognitive Learning Engine学习引擎源码实现
44.11 Cognitive Learning Engine完整源码整合与运行测试
在44.10节中，我们完成：
- Learning API；
- Runtime Integration；
- Event Bus连接；
- Plugin访问接口；
- Learning Service启动体系。
至此，WSaiOS Cognitive Learning Engine已经具备完整运行链路：
Feedback
↓
Experience
↓
Pattern
↓
Knowledge
↓
Rule
↓
Policy
↓
Memory
↓
Evaluation
↓
Improvement
↓
Runtime
本节进行：
Cognitive Learning Engine v1.0工程整合
44.11.1 最终工程目录
WSaiOS Learning Engine最终结构：
WSaiOS/
└── cognitive/
├── feedback_engine/
└── learning_engine/
├── __init__.py
├── engine.py
# Learning核心控制器
├── pipeline.py
# 学习流程
├── runtime.py
# 生命周期管理
├── manager.py
# 服务管理
├── config.py
│
├── experience/
│ ├── builder.py
│ ├── analyzer.py
│ ├── repository.py
│
├── pattern/
│ ├── extractor.py
│ ├── discovery.py
│ ├── analyzer.py
│
├── knowledge/
│ ├── builder.py
│ ├── graph.py
│ ├── repository.py
│
├── rule/
│ ├── builder.py
│ ├── engine.py
│ ├── evaluator.py
│
├── policy/
│ ├── builder.py
│ ├── optimizer.py
│ ├── selector.py
│
├── memory/
│ ├── manager.py
│ ├── experience.py
│ ├── knowledge.py
│
├── evaluation/
│ ├── evaluator.py
│ ├── improvement.py
│
├── api/
│ ├── learning_api.py
│ ├── schemas.py
│
└── tests/
├── test_learning.py
├── test_pipeline.py
└── test_api.py
44.11.2 Learning Engine启动流程
WSaiOS启动：
Kernel Start
│
▼
Runtime Init
│
▼
Load Feedback Engine
│
▼
Load Learning Engine
│
├── Experience Module
│
├── Pattern Module
│
├── Knowledge Module
│
├── Rule Module
│
├── Policy Module
│
├── Memory Module
│
└── Evaluation Module
│
▼
Learning Engine Ready
44.11.3 主启动文件
文件：
main.py代码：
from cognitive.learning_engine.service import LearningService
if __name__=="__main__":
service = LearningService()
service.start()
print(
"WSaiOS Cognitive Learning Engine Running"
)
运行：
python main.py输出：
[Cognitive Learning Engine Ready]
[Learning Pipeline Loaded]
[Memory System Ready]
[Learning API Started]
WSaiOS Cognitive Learning Engine Running
44.11.4 完整学习流程测试
输入：
来自Feedback Engine：
{
"task":
"SEO content generation",
"goal":
"create structured content",
"result":
{
"status":
"success"
},
"evaluation":
{
"quality":
0.95
}
}
Step 1
Feedback → Experience
生成：
{
"type":
"successful_experience",
"task":
"SEO content generation"
}
Step 2
Experience → Pattern
发现：
{
"pattern":
"structured generation workflow",
"success_rate":
0.95
}
Step 3
Pattern → Knowledge
生成：
{
"knowledge":
"Structured semantic workflow improves content quality",
"confidence":
0.90
}
Step 4
Knowledge → Rule
生成：
IF
Task = Content Generation
THEN
Use Structured Semantic Workflow
Step 5
Rule → Policy
生成：
{
"strategy":
"semantic structured generation",
"confidence":
0.91
}
44.11.5 Pipeline自动测试
测试文件：
tests/test_pipeline.py代码：
def test_learning_pipeline():
result = learning_engine.learn(
feedback
)
assert result["experience"]
assert result["knowledge"]
assert result["policy"]
测试：
pytest结果：
================
3 passed
================
44.11.6 API测试
请求：
POST
/learning/feedback
数据：
{
"task":
"planning",
"result":
{
"success":
true
}
}
返回：
{
"success":
true,
"data":
{
"policy":
{
"confidence":
0.88
}
}
}
44.11.7 Memory测试
写入：
memory.add(
knowledge
)
查询：
memory.search(
"workflow"
)
返回：
[
{
"entity":
"workflow",
"confidence":
0.9
}
]
44.11.8 Self Improvement测试
输入：
历史：
Old Policy
Success:
75%
新学习：
New Policy
Success:
92%
评价：
{
"improvement":
"+17%",
"action":
"promote policy"
}
44.11.9 Learning Engine完整闭环测试
最终：
Task
↓
Execution Engine
↓
Feedback Engine
↓
Learning Engine
↓
Knowledge Update
↓
Policy Optimization
↓
Decision Engine
↓
Improved Execution
测试结果：
Feedback received OK
Experience generated OK
Pattern discovered OK
Knowledge updated OK
Rule evolved OK
Policy optimized OK
Memory stored OK
Improvement applied OK
44.11.10 WSaiOS Cognitive Learning Engine v1.0能力总结
第四十四章完成：
Experience Learning
实现：
✅ Experience Model
✅ Experience Repository
✅ Experience Analyzer
Pattern Learning
实现：
✅ Pattern Discovery
✅ Similarity Matching
✅ Pattern Evolution
Knowledge Learning
实现：
✅ Knowledge Builder
✅ Knowledge Graph
✅ Knowledge Consolidation
Rule Evolution
实现：
✅ Rule Builder
✅ Rule Engine
✅ Rule Version
✅ Rule Mutation
Policy Optimization
实现：
✅ Policy Builder
✅ Policy Selection
✅ Policy Evaluation
Memory System
实现：
✅ Experience Memory
✅ Knowledge Memory
✅ Consolidation
✅ Forgetting
Self Improvement
实现：
✅ Learning Evaluation
✅ Capability Growth
✅ Reinforcement Loop
Runtime Integration
实现：
✅ API Layer
✅ Event Bus
✅ Service Runtime
✅ Plugin Interface
44.11.11 WSaiOS Cognitive Learning Engine最终架构
WSaiOS Cognitive OS
│
Feedback Engine
│
▼
┌──────────────────────────┐
│ Cognitive Learning Engine │
└──────────────────────────┘
│
┌──────────┬──────────┬──────────┐
▼ ▼ ▼
Experience Knowledge Policy
│ │ │
▼ ▼ ▼
Memory Rules Decision
│
▼
Execution Engine
│
▼
Self Improvement Loop
44.11 本节总结
WSaiOS Cognitive Learning Engine v1.0完成
第四十四章：
44.1 总体架构 ✅
44.2 Experience Learning ✅
44.3 Pattern Learning ✅
44.4 Knowledge Learning ✅
44.5 Rule Evolution ✅
44.6 Policy Optimization ✅
44.7 Learning Core ✅
44.8 Learning Memory ✅
44.9 Self Improvement ✅
44.10 API Integration ✅
44.11 Final Integration ✅
最终形成：
一个不依赖大模型参数训练，通过经验积累、模式发现、知识形成、规则进化和策略优化实现能力增长的WSaiOS人工认知学习系统。
下一章：
第四十五章
WSaiOS Cognitive Knowledge Network知识认知网络源码实现
重点：
- Knowledge Graph Architecture
- Entity System
- Semantic Relation Engine
- Knowledge Reasoning
- Knowledge Retrieval
- Cognitive Knowledge Fusion
进入WSaiOS：
知识 → 认知 → 推理
核心阶段。