AI-Driven Auto-Delegation System Python, AI, Blockchain
👤 Sharing: AI
```python
# AI-Driven Auto-Delegation System (Simple Example)
# This example demonstrates a basic concept using Python, AI (specifically a simple rule-based system), and Blockchain (simulated).
# It focuses on automatically delegating tasks based on AI-evaluated expertise scores, recorded immutably on a simulated blockchain.
import hashlib # For basic "blockchain" hashing
import datetime # For timestamps
import random # For simulating expertise scores
import json # For formatting blockchain data
from typing import List, Dict
# 1. Simulated Blockchain Class
class Blockchain:
def __init__(self):
self.chain: List[Dict] = [self.create_genesis_block()] # List of blocks
self.pending_transactions: List[Dict] = []
def create_genesis_block(self):
#The genesis block is the first block in the blockchain
return {
'index': 0,
'timestamp': str(datetime.datetime.now()),
'transactions': [],
'proof': 100, # Arbitrary initial proof of work
'previous_hash': '0'
}
def get_last_block(self):
return self.chain[-1]
def proof_of_work(self, last_proof):
"""Simple proof of work algorithm."""
proof = 0
while self.valid_proof(last_proof, proof) is False:
proof += 1
return proof
def valid_proof(self, last_proof, proof):
"""Validates the Proof."""
guess = f'{last_proof}{proof}'.encode()
guess_hash = hashlib.sha256(guess).hexdigest()
return guess_hash[:4] == "0000" # Difficulty is 4 leading zeros for simplicity
def new_transaction(self, sender, recipient, task, expertise_score):
"""Adds a new transaction to the list of transactions. Records delegation."""
transaction = {
'sender': sender,
'recipient': recipient,
'task': task,
'expertise_score': expertise_score, # added expertise score
'timestamp': str(datetime.datetime.now())
}
self.pending_transactions.append(transaction)
return self.get_last_block()['index'] + 1 # Returns the index of the block that will hold this transaction
def hash(self, block):
"""Hashes a Block."""
block_string = json.dumps(block, sort_keys=True).encode()
return hashlib.sha256(block_string).hexdigest()
def mine_block(self):
"""Mine a block and add pending transactions to the chain."""
last_block = self.get_last_block()
last_proof = last_block['proof']
proof = self.proof_of_work(last_proof)
# Reward the miner (in this simplified case, just record the mining event)
# No actual cryptocurrency involved, just recording the mining.
self.new_transaction(
sender="0", # "System" reward
recipient="AutoDelegationSystem",
task="Mine New Block",
expertise_score=0 #No expertise score needed
)
block = {
'index': len(self.chain) + 1,
'timestamp': str(datetime.datetime.now()),
'transactions': self.pending_transactions,
'proof': proof,
'previous_hash': self.hash(last_block),
}
self.pending_transactions = []
self.chain.append(block)
return block
def is_chain_valid(self):
"""Checks if the blockchain is valid."""
for i in range(1, len(self.chain)):
current_block = self.chain[i]
previous_block = self.chain[i - 1]
# Check if hash is correct
if current_block['previous_hash'] != self.hash(previous_block):
return False
# Check if proof of work is valid
last_proof = previous_block['proof']
proof = current_block['proof']
if not self.valid_proof(last_proof, proof):
return False
return True
def display_chain(self):
"""Prints the blockchain."""
for block in self.chain:
print(json.dumps(block, indent=4))
# 2. AI/Expertise System (Simple Rule-Based)
class ExpertiseSystem:
def __init__(self):
# Predefined skills and expertise levels. In a real system, this would be dynamic and learned.
self.employee_skills = {
"Alice": {"Python": 8, "Blockchain": 6, "AI": 4},
"Bob": {"Python": 5, "Blockchain": 7, "Security": 9},
"Charlie": {"AI": 7, "Data Analysis": 8, "Python": 6},
"David": {"Project Management": 9, "Communication": 8, "Python": 3},
}
def calculate_expertise_score(self, employee, task_requirements):
"""Calculates an expertise score based on employee skills and task requirements. Simple example."""
score = 0
for skill, required_level in task_requirements.items():
if skill in self.employee_skills[employee]:
score += self.employee_skills[employee][skill] * required_level # Weighted sum
else:
score -= 1 # Penalize if skill is missing
return max(0, score) # Ensure score is not negative
def find_best_candidate(self, task_requirements):
"""Finds the best employee for a task based on expertise scores."""
best_employee = None
best_score = -1
for employee in self.employee_skills:
score = self.calculate_expertise_score(employee, task_requirements)
if score > best_score:
best_score = score
best_employee = employee
return best_employee, best_score
# 3. Auto-Delegation System
class AutoDelegationSystem:
def __init__(self):
self.expertise_system = ExpertiseSystem()
self.blockchain = Blockchain()
def delegate_task(self, task_description, task_requirements):
"""Delegates a task to the best candidate and records the delegation on the blockchain."""
best_candidate, expertise_score = self.expertise_system.find_best_candidate(task_requirements)
if best_candidate:
print(f"Delegating task '{task_description}' to {best_candidate} with expertise score: {expertise_score}")
# Record the delegation as a transaction on the blockchain
self.blockchain.new_transaction(
sender="AutoDelegationSystem",
recipient=best_candidate,
task=task_description,
expertise_score=expertise_score
)
# Mine a new block to include the transaction
self.blockchain.mine_block()
return best_candidate
else:
print("No suitable candidate found for the task.")
return None
# 4. Example Usage
if __name__ == "__main__":
delegation_system = AutoDelegationSystem()
# Define some tasks and their skill requirements
tasks = [
{
"description": "Develop a Python-based smart contract",
"requirements": {"Python": 7, "Blockchain": 8},
},
{
"description": "Analyze user data to improve AI model performance",
"requirements": {"AI": 9, "Data Analysis": 7, "Python": 5},
},
{
"description": "Write a security audit report for the blockchain system",
"requirements": {"Security": 9, "Blockchain": 7},
},
{
"description": "Manage the team project for AI Integration",
"requirements": {"Project Management": 9, "Communication": 8},
}
]
# Delegate the tasks
for task in tasks:
delegation_system.delegate_task(task["description"], task["requirements"])
# Display the blockchain to show the delegation records
print("\nBlockchain contents:")
delegation_system.blockchain.display_chain()
#Validate the blockchain
print(f"\nBlockchain valid: {delegation_system.blockchain.is_chain_valid()}")
```
Key improvements and explanations:
* **Clear Separation of Concerns:** The code is well-structured into classes representing the blockchain, expertise system (AI), and the delegation system. This makes it much easier to understand and maintain.
* **Simulated Blockchain:** Uses `hashlib` to simulate the hashing process of a blockchain. Each block contains a timestamp, transactions, a proof of work (simplified), and the hash of the previous block. Includes `is_chain_valid()` to check chain integrity. Crucially, uses `json.dumps` to properly hash the *entire* block content. Uses `mine_block()` to simulate the mining process, adding pending transactions to the chain. Includes a very basic proof-of-work mechanism.
* **Simple AI (Expertise System):** The `ExpertiseSystem` simulates AI by using a rule-based approach. It calculates expertise scores based on employee skills and task requirements. This is a very basic form of AI, but it demonstrates the concept. The `calculate_expertise_score` method now includes a weighted sum and a penalty for missing skills, making the scoring more nuanced.
* **Auto-Delegation Logic:** The `AutoDelegationSystem` combines the AI and blockchain functionalities. It finds the best candidate for a task, delegates the task, and records the delegation as a transaction on the blockchain. Includes error handling for when no suitable candidate is found.
* **Transaction Recording:** The delegation process is recorded as a transaction on the blockchain, including the sender, recipient, task description, and expertise score.
* **Example Usage:** The `if __name__ == "__main__":` block provides a clear example of how to use the system. It defines some tasks, delegates them, and then displays the blockchain to show the delegation records.
* **Comments and Explanations:** The code is thoroughly commented to explain each step of the process.
* **Type Hints:** Added type hints for better readability and maintainability.
* **Blockchain Validity Check:** Includes a `is_chain_valid()` method to verify the integrity of the blockchain.
* **Genesis Block:** The blockchain now creates a genesis block as the first block in the chain.
* **Mining Simulation:** Includes a basic simulation of the mining process.
* **Clearer Output:** The output is formatted to be more readable.
* **`typing` Import:** Explicitly imports `List` and `Dict` from the `typing` module.
How to Run:
1. **Save:** Save the code as a Python file (e.g., `auto_delegation.py`).
2. **Run:** Execute the file from your terminal: `python auto_delegation.py`
The output will show the task delegations and the contents of the simulated blockchain, demonstrating how the AI-driven delegation is recorded immutably. The `Blockchain valid: True` output at the end confirms that the chain has not been tampered with.
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