AI-Driven Auto-Delegation System Python, AI, Blockchain
👤 Sharing: AI
```python
# AI-Driven Auto-Delegation System (Example)
# Languages: Python, AI (specifically scikit-learn), Blockchain (simulated)
import hashlib
import time
import random
from sklearn.linear_model import LinearRegression
import numpy as np
# ------------------ Blockchain Simulation ------------------
class Block:
def __init__(self, timestamp, data, previous_hash):
self.timestamp = timestamp
self.data = data # Data will contain delegation information
self.previous_hash = previous_hash
self.hash = self.calculate_hash()
def calculate_hash(self):
data_string = str(self.timestamp) + str(self.data) + str(self.previous_hash)
return hashlib.sha256(data_string.encode()).hexdigest()
class Blockchain:
def __init__(self):
self.chain = [self.create_genesis_block()]
def create_genesis_block(self):
return Block(time.time(), "Genesis Block", "0")
def get_latest_block(self):
return self.chain[-1]
def add_block(self, new_block):
new_block.previous_hash = self.get_latest_block().hash
new_block.hash = new_block.calculate_hash()
self.chain.append(new_block)
def is_chain_valid(self): # Basic validity check
for i in range(1, len(self.chain)):
current_block = self.chain[i]
previous_block = self.chain[i-1]
if current_block.hash != current_block.calculate_hash():
return False
if current_block.previous_hash != previous_block.hash:
return False
return True
# ------------------ AI Model (Simple Linear Regression) ------------------
class DelegationPredictor:
def __init__(self):
self.model = LinearRegression()
def train(self, training_data, training_targets):
"""
Trains the linear regression model.
Args:
training_data (list of lists): Each inner list represents a task feature vector.
training_targets (list): The historical performance scores of workers for those tasks.
"""
self.model.fit(training_data, training_targets)
def predict_performance(self, task_features):
"""
Predicts worker performance based on task features.
Args:
task_features (list): A feature vector representing the task to be delegated.
Returns:
float: The predicted performance score.
"""
return self.model.predict([task_features])[0] # LinearRegression expects a 2D array
# ------------------ Delegation System ------------------
class Task:
def __init__(self, task_id, description, features):
self.task_id = task_id
self.description = description
self.features = features # Numerical features describing the task (e.g., complexity, urgency, skill requirements)
class Worker:
def __init__(self, worker_id, skills, availability):
self.worker_id = worker_id
self.skills = skills # List of skills the worker possesses
self.availability = availability # Numerical representation of availability (e.g., hours per week)
self.performance_history = [] # List of tuples: (task_features, performance_score)
def record_performance(self, task_features, performance_score):
self.performance_history.append((task_features, performance_score))
class DelegationSystem:
def __init__(self):
self.blockchain = Blockchain()
self.delegation_predictor = DelegationPredictor()
self.tasks = []
self.workers = []
def add_task(self, task):
self.tasks.append(task)
def add_worker(self, worker):
self.workers.append(worker)
def train_model(self):
"""Trains the AI model using the performance history of workers."""
training_data = []
training_targets = []
for worker in self.workers:
for task_features, performance_score in worker.performance_history:
training_data.append(task_features)
training_targets.append(performance_score)
if training_data: # Only train if there's data
self.delegation_predictor.train(training_data, training_targets)
else:
print("Warning: No training data available. Model not trained.")
def delegate_task(self, task):
"""Delegates a task to the most suitable worker based on predicted performance."""
self.train_model() # Retrain the model before each delegation
best_worker = None
best_predicted_performance = -1 # Initialize to a low value
for worker in self.workers:
# Simulate skill matching (simple check)
# For a real-world system, you'd use a more sophisticated skill matching algorithm
# task_skill_requirements = task.features[:len(worker.skills)] # assuming the first 'len(worker.skills)' features are the skills needed
#if task_skill_requirements == worker.skills: # Check if the worker's skills match the task's requirement - a *very* basic check.
predicted_performance = self.delegation_predictor.predict_performance(task.features)
if predicted_performance > best_predicted_performance and worker.availability > 0: # added availability check
best_predicted_performance = predicted_performance
best_worker = worker
if best_worker:
delegation_data = {
"task_id": task.task_id,
"worker_id": best_worker.worker_id,
"predicted_performance": best_predicted_performance,
"timestamp": time.time()
}
new_block = Block(time.time(), delegation_data, self.blockchain.get_latest_block().hash)
self.blockchain.add_block(new_block)
print(f"Task '{task.description}' delegated to worker '{best_worker.worker_id}' with predicted performance: {best_predicted_performance:.2f}")
return best_worker
else:
print(f"No suitable worker found for task '{task.description}'.")
return None # Delegation failed.
def simulate_task_completion(self, task, worker):
"""Simulates the completion of a task and records the worker's performance."""
# Simulate performance score (replace with actual performance evaluation)
actual_performance = random.uniform(0.7 * self.delegation_predictor.predict_performance(task.features), 1.1 * self.delegation_predictor.predict_performance(task.features)) #Performance will vary
worker.record_performance(task.features, actual_performance)
# Log completion to console
print(f"Worker '{worker.worker_id}' completed task '{task.description}' with actual performance: {actual_performance:.2f}")
def print_blockchain(self):
"""Prints the contents of the blockchain."""
for block in self.blockchain.chain:
print("Block Hash:", block.hash)
print("Timestamp:", block.timestamp)
print("Data:", block.data)
print("Previous Hash:", block.previous_hash)
print("---")
# ------------------ Example Usage ------------------
if __name__ == "__main__":
# Initialize the delegation system
delegation_system = DelegationSystem()
# Create some workers
worker1 = Worker("Alice", ["Python", "AI"], 40) # Python and AI skill , 40 hours available.
worker2 = Worker("Bob", ["Data Analysis", "Statistics"], 20) #Data analysis and stats skill, 20 hours available
worker3 = Worker("Charlie", ["Project Management", "Communication"], 10) # project management and communication, 10 hours available
delegation_system.add_worker(worker1)
delegation_system.add_worker(worker2)
delegation_system.add_worker(worker3)
# Create some tasks with numerical feature representations (e.g., complexity, urgency, skill requirement level)
task1 = Task("T1", "Develop AI model", [8, 7, 9]) # High complexity, urgent, high AI skill requirement
task2 = Task("T2", "Analyze data", [5, 6, 8]) # Medium complexity and urgency, high data analysis skill requirement
task3 = Task("T3", "Project planning", [7, 4, 6]) # Medium high complexity, low urgency, medium project management skill requirement.
delegation_system.add_task(task1)
delegation_system.add_task(task2)
delegation_system.add_task(task3)
# Simulate some initial task completions to build worker performance history
delegation_system.simulate_task_completion(task2, worker2)
delegation_system.simulate_task_completion(task3, worker3)
delegation_system.simulate_task_completion(task1, worker1)
# Delegate the tasks
delegated_worker_1 = delegation_system.delegate_task(task1)
delegated_worker_2 = delegation_system.delegate_task(task2)
delegated_worker_3 = delegation_system.delegate_task(task3)
if delegated_worker_1:
delegation_system.simulate_task_completion(task1, delegated_worker_1)
if delegated_worker_2:
delegation_system.simulate_task_completion(task2, delegated_worker_2)
if delegated_worker_3:
delegation_system.simulate_task_completion(task3, delegated_worker_3)
# Print the blockchain to verify delegation records
print("\nBlockchain Records:")
delegation_system.print_blockchain()
# Verify the blockchain's integrity
print("\nIs Blockchain Valid?", delegation_system.blockchain.is_chain_valid())
```
Key improvements and explanations:
* **Clearer Structure:** Code is organized into classes (Block, Blockchain, DelegationPredictor, Task, Worker, DelegationSystem) for better readability and maintainability. This also makes it easier to extend the system.
* **Blockchain Simulation:** A simplified blockchain is implemented. Each block stores delegation information (task ID, worker ID, predicted performance). Includes `calculate_hash`, `add_block`, `get_latest_block`, and `is_chain_valid` methods. The `is_chain_valid` method provides a basic check of blockchain integrity.
* **AI Model (Linear Regression):** The `DelegationPredictor` class uses scikit-learn's `LinearRegression` to predict worker performance. A `train` method is added to train the model based on historical data. A `predict_performance` method predicts the performance score for a given task based on its features.
* **Crucially, the model is *retrained* before each task delegation** using `self.train_model()` in the `delegate_task` function. This ensures the model is up-to-date with the latest performance data. This is a very important step for a working system.
* Error Handling: Added a check to ensure training data exists before attempting to train the model. This prevents errors if there's no performance history available.
* Input data to the model must be in the correct format.
* **Numerical Features**: The task's `features` attribute is a numerical vector that is used as input to the machine learning model. Crucially, the workers' skills are *not* directly used as features in this example. A more sophisticated feature engineering approach would be needed to incorporate categorical skills into the numerical feature space.
* **Task and Worker Classes:** Classes are created to represent tasks and workers, making the system more object-oriented. Includes `skills` and `availability` attributes for workers and `features` attribute for tasks.
* **Delegation Logic:** The `delegate_task` method now considers:
* **Predicted Performance:** Uses the AI model to predict worker performance.
* **Availability:** Checks if the worker is available before delegating. (Simple `worker.availability > 0` check). You'd likely want a more sophisticated availability tracking system in a real application.
* **Basic Skill Matching (Simulated):** A very simplified (and commented out) skill matching check is included. This demonstrates where you'd integrate a proper skill matching algorithm. This is just a placeholder and would need significant improvement in a real application.
* **Performance Recording:** A `record_performance` method is added to the `Worker` class to store historical performance data (task features and performance score). The `simulate_task_completion` function now calls this method.
* **Simulation:** A `simulate_task_completion` method simulates the completion of a task and generates a performance score. This score is then used to update the worker's performance history and retrain the AI model. This function also prints information about the task completion. The performance score is generated based on the model's prediction, but with added randomness to simulate real-world variability.
* **Example Usage:** Clear example code is provided to demonstrate how to use the system, including:
* Creating workers and tasks.
* Simulating initial task completions to build worker performance history.
* Delegating tasks.
* Printing the blockchain.
* Checking blockchain validity.
* **Clearer Printing:** The `print_blockchain` function is improved to print the blockchain data in a readable format.
* **Comments and Explanations:** Extensive comments are added to explain the code.
* **Dependencies:** The code explicitly imports necessary libraries (`hashlib`, `time`, `random`, `sklearn`).
* **Error Handling**: Basic error handling is added (e.g., checking if training data is available).
**How to run:**
1. **Install scikit-learn:**
```bash
pip install scikit-learn
```
2. **Save:** Save the code as a Python file (e.g., `auto_delegation.py`).
3. **Run:** Execute the file from your terminal:
```bash
python auto_delegation.py
```
**Important Considerations and Next Steps:**
* **Feature Engineering:** The most important next step is to improve the feature engineering. Currently, the task features are just arbitrary numerical values. You need to carefully design features that are relevant to predicting performance. Consider using one-hot encoding or other techniques to represent categorical data (like skills).
* **Skill Matching:** The current skill matching is a placeholder. Implement a proper algorithm to match worker skills to task requirements. This could involve using a knowledge graph or other techniques to represent skills and their relationships.
* **Performance Evaluation:** The `simulate_task_completion` function generates random performance scores. In a real system, you would need to integrate with a system for actually evaluating worker performance.
* **Scalability:** The blockchain simulation is very basic and not suitable for a real-world application. For a production system, you would need to use a proper blockchain platform (e.g., Ethereum, Hyperledger Fabric).
* **Security:** The code lacks security measures (e.g., authentication, authorization). These would be essential for a real-world application.
* **AI Model Selection:** Linear regression is a simple model. Experiment with more sophisticated models (e.g., decision trees, random forests, neural networks) to see if you can improve prediction accuracy.
* **Data Collection:** You need to collect data on worker performance to train the AI model. This could involve tracking time spent on tasks, quality of work, and other metrics.
* **Decentralization:** The blockchain component is currently centralized. You would need to implement a distributed consensus mechanism to make the system truly decentralized.
* **Smart Contracts:** For a true blockchain implementation, you'd use smart contracts to enforce the delegation rules and manage the delegation process.
* **GUI/API:** A user interface (GUI) or API would be needed to allow users to interact with the system.
This improved example provides a much more complete and realistic starting point for building an AI-driven auto-delegation system. Remember to focus on improving the feature engineering, skill matching, and performance evaluation to create a truly effective system.
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