Judgment_Enhancement_Engine
/install judgment-enhancement-engine
Judgment Enhancement Engine
Enhance AI agent decision-making under uncertainty through recursive Monte Carlo lookahead simulation, risk-adjusted utility, and historical reflection.
Quick Start
# Run built-in GridWorld demo
python skills/judgment-enhancement-engine/engine.py
# One-click setup
bash skills/judgment-enhancement-engine/scripts/setup.sh # Linux/macOS/WSL
skills\judgment-enhancement-engine\scripts\setup.bat # Windows
Core Usage
from engine import JudgmentEnhancementEngine, JudgmentResult
# 1. Define your world model (must implement WorldModel protocol)
class MyWorldModel:
def get_possible_outcomes(self, state, action):
# Returns [(next_state, probability, reward), ...]
...
def is_terminal(self, state):
...
def get_legal_actions(self, state):
...
# 2. Define objective function
class MyObjective:
def evaluate(self, state):
return float_score # higher = better
# 3. Create engine
engine = JudgmentEnhancementEngine(
world_model=MyWorldModel(),
objective=MyObjective(),
risk_tolerance=0.5, # 0=extreme risk-averse, 1=risk-neutral
lookahead_depth=3, # recursion depth
simulation_breadth=4, # max actions evaluated per level
use_greedy_rollout=True, # True=greedy (accurate), False=uniform (fast)
max_compute_time_sec=2.0 # timeout protection
)
# 4. Make a judgment
result = engine.enhance_judgment(current_state)
print(f"Best action: {result.best_action}")
print(f"Confidence: {result.confidence:.2f}")
print(f"Reasoning: {result.reasoning}")
# 5. Record actual outcome (for historical correction)
engine.record_outcome(state, action, actual_utility)
# 6. Optional: clear history
engine.clear_history()
Configuration
| Parameter | Default | Description |
|---|---|---|
| risk_tolerance | 0.5 | 0=extreme risk-averse, 1=risk-neutral |
| lookahead_depth | 2 | Recursive lookahead levels |
| simulation_breadth | 3 | Max actions evaluated per level |
| history_size | 100 | Historical records kept |
| max_compute_time_sec | 1.0 | Timeout protection (seconds) |
| use_greedy_rollout | True | True=greedy (accurate), False=uniform (fast) |
JudgmentResult Fields
| Field | Type | Description |
|---|---|---|
| best_action | Action | Selected best action |
| scores | dict | Risk-adjusted utility per action |
| raw_utilities | dict | Raw expected utility per action |
| risk_metrics | dict | Expectation/variance/std/VaR95 per action |
| reasoning | str | Human-readable decision reasoning |
| confidence | float | 0~1 confidence score |
Example: GridWorld
Built-in demo() shows a 5x5 grid world with obstacles and a goal. Run python engine.py to see it in action.
Installation
| Method | Command |
|---|---|
| One-click (Linux/macOS) | bash scripts/setup.sh |
| One-click (Windows) | scripts\setup.bat |
| Copy-only | Copy engine.py to your project |
| ClawHub | clawhub install judgment-enhancement-engine |
File Structure
judgment-enhancement-engine/
├── SKILL.md
├── engine.py # Core engine (~10KB)
├── index.js # Node.js bridge
├── package.json
├── assets/
│ └── icon.svg
├── references/
│ ├── API_SPEC.md
│ └── USE_GUIDE.md
└── scripts/
├── setup.sh
├── setup.bat
├── test-basic.py
└── test-client.js
License
MIT
- 确保已安装 OpenClaw(本地或 Docker 部署)
- 在对话框中输入安装命令:
/install judgment-enhancement-engine - 安装完成后,直接呼叫该 Skill 的名称或使用
/judgment-enhancement-engine触发 - 根据 Skill 的参数说明提供必要输入,即可获得结构化输出
Judgment_Enhancement_Engine 是什么?
AI Agent judgment enhancement via Monte Carlo lookahead, risk-adjusted utility, and historical reflection. Use when an agent needs to evaluate multi-step act... 它是一个面向 Claude Code / OpenClaw 的 AI Agent Skill 插件,目前累计下载 53 次。
如何安装 Judgment_Enhancement_Engine?
在 OpenClaw 或 Claude Code 对话框中运行命令「/install judgment-enhancement-engine」即可一键安装,无需额外配置。
Judgment_Enhancement_Engine 是免费的吗?
是的,Judgment_Enhancement_Engine 完全免费,采用 MIT-0 许可证,可自由下载、安装和使用。
Judgment_Enhancement_Engine 支持哪些平台?
Judgment_Enhancement_Engine 跨平台运行,可在任意部署了 OpenClaw / Claude Code 的环境中使用(cross-platform)。
谁开发了 Judgment_Enhancement_Engine?
由 CHEN-feng123(@chen-feng123)开发并维护,当前版本 v1.0.0。