swarmauri
Replies: 11 comments 5 replies
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here's an example of plan merging and plan repair in the context of delivery/warehouse agents: from typing import List, Dict, Tuple
import random
class DeliveryAgent:
def __init__(self, name: str):
self.name = name
self.plan = [] # List of delivery points (x, y)
def generate_plan(self, deliveries: List[Tuple[int, int]]):
"""Generate a delivery plan based on assigned locations."""
self.plan = deliveries
def __str__(self):
return f"{self.name}: {self.plan}"
class Coordinator:
def __init__(self):
self.global_plan = {}
def merge_plans(self, agents: List[DeliveryAgent]):
"""Merge agent plans and resolve conflicts."""
for agent in agents:
for point in agent.plan:
if point not in self.global_plan:
self.global_plan[point] = agent.name
else:
# Resolve conflict (assign based on priority)
conflicting_agent = self.global_plan[point]
print(f"Conflict detected at {point}: {agent.name} vs {conflicting_agent}")
if random.choice([True, False]):
self.global_plan[point] = agent.name
print(f"Conflict resolved: {point} assigned to {agent.name}")
else:
print(f"Conflict resolved: {point} remains with {conflicting_agent}")
def repair_plan(self, agent: DeliveryAgent, blocked_point: Tuple[int, int]):
"""Repair an agent's plan if a point becomes unavailable."""
if blocked_point in agent.plan:
print(f"Plan repair: Removing blocked point {blocked_point} from {agent.name}")
agent.plan.remove(blocked_point)
def display_global_plan(self):
print("\nGlobal Plan:")
for point, agent in self.global_plan.items():
print(f"Point {point}: Assigned to {agent}")
# Example Scenario
agent1 = DeliveryAgent("Agent 1")
agent2 = DeliveryAgent("Agent 2")
# Generate plans for agents
agent1.generate_plan([(1, 1), (2, 2), (3, 3)])
agent2.generate_plan([(2, 2), (4, 4), (5, 5)])
# Create a coordinator and merge plans
coordinator = Coordinator()
coordinator.merge_plans([agent1, agent2])
# Display global plan
coordinator.display_global_plan()
# Simulate a blocked delivery point and repair plans
blocked_point = (2, 2)
print(f"\nBlocked point detected: {blocked_point}")
coordinator.repair_plan(agent1, blocked_point)
coordinator.repair_plan(agent2, blocked_point)
# Display updated plans
print("\nUpdated Plans:")
print(agent1)
print(agent2)This example is practical for tangible AI and it may have implications for mulitagent reasoning and orchestration. |
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Plan Merging and Plan Repair: A Detailed ExplanationConcept:
Strengths:
Use Cases:
By integrating plan merging and plan repair, systems involving logistics and delivery or warehouse agents can effectively handle dynamic challenges, maintain operational efficiency, and achieve seamless coordination across multiple agents. |
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deeper dive on p2p communication: ### **Peer-to-Peer Coordination**
Peer-to-peer (P2P) coordination is a decentralized approach in multi-agent systems where all agents have equal roles and responsibilities. There is no central authority or hierarchy; instead, agents interact directly with one another to achieve shared goals. This architecture is particularly well-suited for dynamic, distributed environments where scalability and fault tolerance are essential.
---
### **Concept**
1. **Decentralization**:
- In a P2P system, all agents act independently but collaborate through **direct communication**.
- Decision-making and resource management are distributed among all agents.
2. **Coordination Protocols**:
- P2P coordination relies on specific protocols to facilitate communication and resource sharing:
- **Gossip Protocols**:
- Agents randomly share information with their neighbors, propagating knowledge throughout the network.
- Common in systems where global information needs to spread quickly, such as updating a distributed database.
- **Flooding**:
- An agent sends a message to all its neighbors, who then forward it to their neighbors, and so on.
- Often used in routing or discovery tasks, but it can lead to communication overhead.
- **Token Passing**:
- A virtual "token" circulates among agents, granting temporary control or permission to perform specific tasks.
- Prevents resource conflicts or ensures fairness in task execution.
3. **Local Interactions**:
- Agents interact only with their immediate neighbors or within a small subset of the network, reducing communication costs.
- Global behavior emerges from the aggregation of these local interactions.
---
### **Strengths**
1. **Full Decentralization**:
- No single point of failure, making the system robust and resilient to agent or network failures.
2. **Scalability**:
- P2P systems scale well as the number of agents increases, provided that the communication protocols are efficient.
3. **Fault Tolerance**:
- If one or more agents fail, the remaining agents can continue to function without significant disruption.
4. **Dynamic Adaptability**:
- Agents can join or leave the network seamlessly, making the system highly adaptive to changes.
5. **Cost-Effectiveness**:
- No central server or coordinator reduces infrastructure costs.
---
### **Weaknesses**
1. **Communication Overhead**:
- Protocols like flooding can result in excessive messaging, leading to network congestion.
2. **Coordination Complexity**:
- Ensuring consistency and resolving conflicts among agents can be challenging without a central authority.
3. **Latency**:
- In large networks, it can take time for information or updates to propagate to all agents.
4. **Security**:
- Decentralized systems are vulnerable to attacks, such as malicious agents spreading false information.
---
### **Applications**
1. **File-Sharing Systems**:
- P2P file-sharing networks (e.g., BitTorrent) allow users to share and download files directly from one another without a central server.
2. **Blockchain and Cryptocurrencies**:
- Blockchain systems like Bitcoin and Ethereum use P2P networks for transaction validation and block propagation.
3. **Distributed Databases**:
- Systems like Cassandra and DynamoDB use P2P architectures for decentralized data storage and retrieval.
4. **IoT (Internet of Things)**:
- Smart devices in IoT systems coordinate directly with one another to manage resources or perform tasks, such as load balancing in smart grids.
5. **Ad-Hoc Networks**:
- Temporary, decentralized networks like those used in disaster recovery or battlefield communications.
6. **Collaborative Applications**:
- Multiplayer gaming, collaborative document editing, or decentralized messaging systems.
---
### **Example: P2P Gossip Protocol in Python**
Here’s a Python example simulating a **gossip protocol** in a P2P network:
#### Code Implementation
```python
import random
class Agent:
def __init__(self, id):
self.id = id
self.knowledge = set()
def receive_gossip(self, message):
"""Receive a piece of gossip (information)."""
self.knowledge.add(message)
def gossip_to_neighbors(self, neighbors):
"""Share gossip with a random subset of neighbors."""
if self.knowledge:
message = random.choice(list(self.knowledge))
for neighbor in neighbors:
neighbor.receive_gossip(message)
print(f"Agent {self.id} gossiped '{message}' to Agent {neighbor.id}")
def simulate_gossip_protocol(agents, gossip_message, steps):
"""Simulate a gossip protocol over a fixed number of steps."""
# Select a random agent to start the gossip
initial_agent = random.choice(agents)
initial_agent.receive_gossip(gossip_message)
print(f"Agent {initial_agent.id} starts the gossip with '{gossip_message}'")
for step in range(steps):
print(f"\nStep {step + 1}:")
for agent in agents:
# Each agent gossips to a random subset of neighbors
neighbors = random.sample([a for a in agents if a != agent], k=2) # Pick 2 random neighbors
agent.gossip_to_neighbors(neighbors)
# Create agents
num_agents = 5
agents = [Agent(i) for i in range(num_agents)]
# Simulate gossip protocol
simulate_gossip_protocol(agents, "The cake is a lie!", steps=3)Explanation of the Code
Sample OutputKey Benefits Demonstrated
SummaryPeer-to-peer coordination provides a robust, scalable, and decentralized framework for agent collaboration. Protocols like gossiping, flooding, and token passing enable efficient communication and resource management in diverse systems, ranging from file-sharing networks to IoT devices. Despite challenges like communication overhead and security, P2P coordination remains a cornerstone of modern distributed systems. |
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gossiping and flooding may be useful in scenarios where multiple developers are coordinating through decentralized hosting, however this merely serves to disseminate information/state, potentially trigger events, and so forth. There is no consensus mechanisms to enable a coordinated cooperative focus. There is no ledger or lock to grant write privileges. This is why token passing stands out quickly as an enabler for multiagents (be it under a pubsub, gossipsub/flooding), as it offers a means for an agent to grant read write access and potentially lock a resource to prevent other writers temporarily. Tokens could be tied to blackboards, state variables, persistent files, or sets of all or some of the above. |
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Here’s an example of using token passing for authenticating access to, locking, and performing read/write/execute operations on shared resources in a multi-agent system. The shared resource could be a blackboard, state variable, or persistent file. Token passing ensures only one agent at a time can access the resource, preventing race conditions and unauthorized modifications. Conceptual Overview
Python ImplementationHere’s an example demonstrating these concepts: import time
import threading
import random
class SharedResource:
def __init__(self):
self.data = 0
self.lock = False # Indicates whether the resource is locked
def read(self, agent_name):
if self.lock:
print(f"{agent_name} reads: {self.data}")
else:
print(f"{agent_name} attempted to read without a lock!")
def write(self, agent_name, value):
if self.lock:
self.data = value
print(f"{agent_name} writes: {self.data}")
else:
print(f"{agent_name} attempted to write without a lock!")
def execute(self, agent_name):
if self.lock:
print(f"{agent_name} executes operation on resource: doubling data.")
self.data *= 2
print(f"New data value: {self.data}")
else:
print(f"{agent_name} attempted to execute without a lock!")
class Agent:
def __init__(self, name):
self.name = name
self.token = False # Does the agent currently have the token?
def request_access(self, resource, action, value=None):
"""Access the resource based on the current token state."""
if self.token:
print(f"\n{self.name} has the token.")
resource.lock = True # Lock the resource for exclusive access
if action == "read":
resource.read(self.name)
elif action == "write":
resource.write(self.name, value)
elif action == "execute":
resource.execute(self.name)
resource.lock = False # Release the lock after the operation
else:
print(f"{self.name} attempted to access the resource without the token.")
def pass_token(self, next_agent):
"""Pass the token to another agent."""
if self.token:
print(f"{self.name} passes the token to {next_agent.name}.")
self.token = False
next_agent.token = True
# Simulating token passing and resource management
def simulate_token_passing():
# Shared resource
resource = SharedResource()
# Agents
agents = [Agent(f"Agent {i}") for i in range(3)]
# Assign the token to the first agent
agents[0].token = True
# Randomly perform actions using the token
actions = ["read", "write", "execute"]
for _ in range(6): # Run for a fixed number of operations
current_agent = next(agent for agent in agents if agent.token)
action = random.choice(actions)
value = random.randint(1, 100) if action == "write" else None
# Perform the action
current_agent.request_access(resource, action, value)
# Pass the token to the next agent
next_agent = random.choice([a for a in agents if a != current_agent])
current_agent.pass_token(next_agent)
# Run the simulation
simulate_token_passing()Explanation of the Code
Sample OutputKey Features Demonstrated
Real-World Use Cases
This example showcases how token passing can ensure fairness, prevent race conditions, and provide controlled access to shared resources in multi-agent systems. |
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other frameworks with a strict-LLM focus appear to rely heavily on |
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Centralized Coordination in multi-agent frameworksCentralized Coordination is fundamentally about creating a single "brain" or control center that manages the entire system of agents. Think of it like a symphony conductor who directs every musician, ensuring they play their part at the right time and in harmony with the others. Let's explore this in depth: Conceptual Overview In a centralized coordination model, all decision-making, task allocation, and conflict resolution flow through a single central controller. This approach has both significant advantages and notable limitations: Key Characteristics
Potential Challenges
Code Implementation Here's a Python implementation demonstrating a centralized coordination architecture: import uuid
from enum import Enum, auto
from typing import Dict, List, Optional
from dataclasses import dataclass, field
class AgentStatus(Enum):
IDLE = auto()
BUSY = auto()
COMPLETED = auto()
@dataclass
class Task:
"""Represents a task to be completed by an agent."""
id: str = field(default_factory=lambda: str(uuid.uuid4()))
description: str = ""
priority: int = 0
assigned_agent: Optional[str] = None
@dataclass
class Agent:
"""Represents an individual agent in the system."""
id: str = field(default_factory=lambda: str(uuid.uuid4()))
name: str = ""
status: AgentStatus = AgentStatus.IDLE
current_task: Optional[Task] = None
class CentralCoordinator:
"""Central coordination system managing agents and tasks."""
def __init__(self):
self.agents: Dict[str, Agent] = {}
self.task_queue: List[Task] = []
def register_agent(self, agent: Agent):
"""Register a new agent in the system."""
self.agents[agent.id] = agent
print(f"Agent {agent.name} (ID: {agent.id}) registered")
def add_task(self, task: Task):
"""Add a new task to the task queue."""
# Sort tasks by priority
self.task_queue.append(task)
self.task_queue.sort(key=lambda x: x.priority, reverse=True)
print(f"Task added: {task.description}")
def allocate_tasks(self):
"""Allocate tasks to available agents."""
for task in list(self.task_queue):
# Find an idle agent
idle_agents = [
agent for agent in self.agents.values()
if agent.status == AgentStatus.IDLE
]
if idle_agents and task.assigned_agent is None:
agent = idle_agents[0]
# Assign task to agent
agent.current_task = task
agent.status = AgentStatus.BUSY
task.assigned_agent = agent.id
self.task_queue.remove(task)
print(f"Assigned task '{task.description}' to {agent.name}")
def process_task_completion(self, agent_id: str):
"""Handle task completion for a specific agent."""
agent = self.agents.get(agent_id)
if agent and agent.current_task:
print(f"Task '{agent.current_task.description}' completed by {agent.name}")
agent.status = AgentStatus.COMPLETED
agent.current_task = None
def main():
# Create central coordinator
coordinator = CentralCoordinator()
# Create agents
agents = [
Agent(name="Robot1"),
Agent(name="Robot2"),
Agent(name="Robot3")
]
# Register agents
for agent in agents:
coordinator.register_agent(agent)
# Add tasks
tasks = [
Task(description="Clean Room", priority=2),
Task(description="Inventory Check", priority=3),
Task(description="Maintenance", priority=1)
]
for task in tasks:
coordinator.add_task(task)
# Simulate task allocation
coordinator.allocate_tasks()
# Simulate task completion
for agent in agents:
if agent.current_task:
coordinator.process_task_completion(agent.id)
if __name__ == "__main__":
main()Key Components
Coordination Mechanism The allocate_tasks() method demonstrates centralized coordination:
Real-World Analogy Imagine a warehouse with multiple robots. The central coordinator is like a warehouse manager who:
Limitations to Consider
Potential Improvements
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Deep Dive into Market-Based CoordinationMarket-Based Coordination approach in multi-agent systems, providing a comprehensive explanation with a code implementation that illustrates the key concepts. Conceptual FoundationMarket-Based Coordination is an innovative approach to multi-agent coordination that draws inspiration from economic principles. Imagine a dynamic marketplace where agents interact like participants in an economic ecosystem, negotiating and trading tasks and resources through bidding mechanisms. Deep Dive into Market-Based CoordinationCore Principles
Mechanism of Interaction
Let's implement this concept with a Python example: import uuid
from dataclasses import dataclass, field
from typing import List, Dict
import random
@dataclass
class Task:
"""Represents a computational task with specific resource requirements."""
id: str = field(default_factory=lambda: str(uuid.uuid4()))
name: str = ""
cpu_required: float = 0.0 # CPU cores needed
memory_required: float = 0.0 # Memory in GB
complexity: float = 0.0 # Task complexity score
@dataclass
class ComputationalAgent:
"""Represents a computational resource agent with bidding capabilities."""
id: str = field(default_factory=lambda: str(uuid.uuid4()))
name: str = ""
total_cpu: float = 0.0
total_memory: float = 0.0
current_load_cpu: float = 0.0
current_load_memory: float = 0.0
def calculate_bid(self, task: Task) -> float:
"""
Calculate a competitive bid for a task based on:
1. Remaining computational resources
2. Task complexity
3. Current system load
"""
# Available resources
cpu_availability = max(0, self.total_cpu - self.current_load_cpu)
memory_availability = max(0, self.total_memory - self.current_load_memory)
# Resource match score
resource_match = min(
cpu_availability / max(task.cpu_required, 0.1),
memory_availability / max(task.memory_required, 0.1)
)
# Bid incorporates resource match and inverse of current load
bid = resource_match * (1 - (self.current_load_cpu / self.total_cpu)) * 100
# Add some randomness to simulate market dynamics
bid *= (0.9 + random.random() * 0.2)
return max(0, bid)
class MarketBasedCoordinator:
"""Manages task allocation through a market-based mechanism."""
def __init__(self):
self.agents: List[ComputationalAgent] = []
self.task_queue: List[Task] = []
def register_agent(self, agent: ComputationalAgent):
"""Register a computational agent in the market."""
self.agents.append(agent)
print(f"Agent {agent.name} registered with {agent.total_cpu} CPU, {agent.total_memory} GB Memory")
def add_task(self, task: Task):
"""Add a task to the market task queue."""
self.task_queue.append(task)
print(f"Task {task.name} added to market")
def allocate_tasks(self):
"""
Allocate tasks through a market-based bidding process.
Tasks are assigned to agents with the most competitive bids.
"""
for task in list(self.task_queue):
# Generate bids from all agents
bids = {
agent.id: {
'agent': agent,
'bid_value': agent.calculate_bid(task)
}
for agent in self.agents
if (agent.total_cpu - agent.current_load_cpu >= task.cpu_required and
agent.total_memory - agent.current_load_memory >= task.memory_required)
}
if bids:
# Select winning bid
winner = max(bids.values(), key=lambda x: x['bid_value'])
winning_agent = winner['agent']
# Allocate resources
winning_agent.current_load_cpu += task.cpu_required
winning_agent.current_load_memory += task.memory_required
print(f"Task {task.name} allocated to {winning_agent.name}")
print(f"Winning Bid: {winner['bid_value']:.2f}")
# Remove task from queue
self.task_queue.remove(task)
def print_system_status(self):
"""Display current system load and remaining tasks."""
print("\n--- System Status ---")
for agent in self.agents:
print(f"{agent.name}: CPU Load {agent.current_load_cpu}/{agent.total_cpu}, "
f"Memory Load {agent.current_load_memory}/{agent.total_memory}")
print(f"Remaining Tasks: {len(self.task_queue)}")
def main():
# Initialize market coordinator
market = MarketBasedCoordinator()
# Create computational agents with varied capabilities
agents = [
ComputationalAgent(name="CloudServer1", total_cpu=16.0, total_memory=64.0),
ComputationalAgent(name="CloudServer2", total_cpu=8.0, total_memory=32.0),
ComputationalAgent(name="CloudServer3", total_cpu=4.0, total_memory=16.0)
]
# Register agents
for agent in agents:
market.register_agent(agent)
# Create a variety of tasks
tasks = [
Task(name="DataProcessing", cpu_required=4.0, memory_required=16.0, complexity=0.7),
Task(name="MachineLearningTraining", cpu_required=8.0, memory_required=32.0, complexity=0.9),
Task(name="WebServerComputation", cpu_required=2.0, memory_required=8.0, complexity=0.3)
]
# Add tasks to market
for task in tasks:
market.add_task(task)
# Run task allocation
market.allocate_tasks()
# Show final system status
market.print_system_status()
if __name__ == "__main__":
main()Detailed Explanation of the ImplementationKey Components
Strengths of Market-Based Coordination
Real-World AnalogiesThink of this like a stock market for computational resources:
Potential Enhancements
Limitations to Consider
Comparative PerspectiveCompared to centralized coordination:
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Market-Based CoordinationMarket-based coordination extends the principles of the Contract Net Protocol (CNP) to create a full-fledged market system in which agents act as buyers and sellers, trading resources, tasks, or services. By mimicking real-world economic markets, this approach uses supply-demand dynamics, pricing mechanisms, and auctions to allocate resources or tasks efficiently in decentralized multi-agent systems. Concept
Strengths
Challenges
Applications
Python Implementation: Market-Based Cloud Resource AllocationScenario:Multiple agents (representing servers) bid to complete a computational task based on their available resources and pricing models. import random
class Task:
def __init__(self, id, complexity):
self.id = id
self.complexity = complexity # Amount of computation required (in units)
self.budget = random.randint(50, 150) # Max price the buyer is willing to pay
class Server:
def __init__(self, id, capacity, base_price):
self.id = id
self.capacity = capacity # Available capacity (in units)
self.base_price = base_price # Price per computation unit
def bid(self, task):
"""Submit a bid based on the task complexity and available capacity."""
if task.complexity <= self.capacity:
cost = task.complexity * self.base_price
return {"server": self.id, "cost": cost}
else:
return None
def execute_task(self, task):
"""Execute the task and reduce available capacity."""
if task.complexity <= self.capacity:
self.capacity -= task.complexity
print(f"Server {self.id} executed Task {task.id}. Remaining capacity: {self.capacity}")
else:
print(f"Server {self.id} cannot execute Task {task.id} due to insufficient capacity.")
# Market Coordinator
def market_auction(servers, task):
"""Conduct an auction for a task and assign it to the best server."""
bids = []
for server in servers:
bid = server.bid(task)
if bid:
bids.append(bid)
if bids:
# Select the server with the lowest cost
winning_bid = min(bids, key=lambda x: x["cost"])
if winning_bid["cost"] <= task.budget:
print(f"Task {task.id} awarded to Server {winning_bid['server']} for cost: {winning_bid['cost']}")
winning_server = next(s for s in servers if s.id == winning_bid["server"])
winning_server.execute_task(task)
else:
print(f"No server could execute Task {task.id} within budget.")
else:
print(f"No bids received for Task {task.id}.")
# Create tasks and servers
tasks = [Task(i, random.randint(10, 50)) for i in range(5)]
servers = [Server(i, capacity=random.randint(50, 100), base_price=random.uniform(1, 3)) for i in range(3)]
# Simulate the market
for task in tasks:
print(f"\nTask {task.id}: Complexity={task.complexity}, Budget={task.budget}")
market_auction(servers, task)Explanation of the Code
Sample OutputKey Features Demonstrated
Real-World ImpactMarket-based coordination offers a scalable and flexible framework for task and resource allocation in distributed systems. It drives efficiency, ensures fairness, and fosters competition, making it invaluable in fields like cloud computing, logistics, IoT, and smart energy systems. |
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I agree on the close similarity of Contract Net Protocol (CNP). |
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Hierarchical Task Networks (HTN)ConceptHierarchical Task Networks (HTN) is a planning framework where tasks are decomposed into smaller, more manageable subtasks, forming a hierarchy. This approach mimics how humans naturally divide complex problems into smaller ones.
This architecture is particularly effective for planning and scheduling problems, allowing for parallel execution, dynamic re-planning, and efficient resource allocation. Use CaseHTN is widely used in:
Example in Real LifeIn a robotic assembly line:
Code ImplementationHere is a Python implementation of a simplified HTN for a robotic assembly line: class HTNPlanner:
def __init__(self):
self.task_hierarchy = {}
self.agent_capabilities = {}
self.task_assignments = {}
def add_task(self, task, subtasks=None):
"""Define a task and its subtasks."""
self.task_hierarchy[task] = subtasks or []
def add_agent(self, agent, capabilities):
"""Register an agent and its capabilities."""
self.agent_capabilities[agent] = capabilities
def assign_tasks(self, task):
"""Assign tasks to agents based on capabilities."""
subtasks = self.task_hierarchy.get(task, [])
if not subtasks:
return f"No subtasks for {task}."
for subtask in subtasks:
assigned = False
for agent, capabilities in self.agent_capabilities.items():
if subtask in capabilities:
self.task_assignments[subtask] = agent
assigned = True
print(f"Assigned subtask '{subtask}' to agent '{agent}'.")
break
if not assigned:
print(f"Could not assign subtask '{subtask}'.")
def execute_plan(self):
"""Simulate execution of assigned tasks."""
for subtask, agent in self.task_assignments.items():
print(f"Agent '{agent}' is executing subtask '{subtask}'.")
# Define tasks and subtasks
planner = HTNPlanner()
planner.add_task("Assemble Product", ["Assemble Frame", "Add Electronics", "Package Product"])
planner.add_task("Assemble Frame", ["Weld Parts", "Bolt Components"])
planner.add_task("Add Electronics", ["Install Circuit Board", "Attach Wires"])
# Define agents and their capabilities
planner.add_agent("Robot A", ["Weld Parts", "Bolt Components"])
planner.add_agent("Robot B", ["Install Circuit Board", "Attach Wires"])
planner.add_agent("Robot C", ["Package Product"])
# Assign tasks and execute the plan
planner.assign_tasks("Assemble Product")
planner.execute_plan()OutputHow It Works
This architecture demonstrates HTN principles: hierarchical decomposition, capability-based task assignment, and parallel execution. It is flexible and can be extended for real-world use cases like dynamic re-planning and monitoring task progress. |
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Real-World Use Cases of HTN: Dynamic Re-Planning and Monitoring Task Progress1. Dynamic Re-PlanningDynamic re-planning refers to the system's ability to adapt to unexpected changes in the environment, task requirements, or agent capabilities. It ensures the successful completion of the overall goal, even if the original plan encounters obstacles. How it works in HTN:
Example: Robotic Assembly Line
Key Benefits:
2. Monitoring Task ProgressMonitoring task progress involves tracking the execution status of tasks and subtasks, ensuring timely detection of issues like delays or errors. How it works in HTN:
Example: Logistics and Delivery
Key Benefits:
Combining Dynamic Re-Planning and MonitoringDynamic re-planning and monitoring often work together to create a robust system. Here's how:
Extended Code Example with Re-Planning and MonitoringBelow is an enhancement of the previous code to include dynamic re-planning and task progress monitoring: import random
class HTNPlanner:
def __init__(self):
self.task_hierarchy = {}
self.agent_capabilities = {}
self.task_assignments = {}
self.task_status = {}
def add_task(self, task, subtasks=None):
"""Define a task and its subtasks."""
self.task_hierarchy[task] = subtasks or []
self.task_status[task] = "pending"
def add_agent(self, agent, capabilities):
"""Register an agent and its capabilities."""
self.agent_capabilities[agent] = capabilities
def assign_tasks(self, task):
"""Assign tasks to agents based on capabilities."""
subtasks = self.task_hierarchy.get(task, [])
if not subtasks:
print(f"No subtasks for {task}.")
return
for subtask in subtasks:
assigned = False
for agent, capabilities in self.agent_capabilities.items():
if subtask in capabilities and self.task_status[subtask] == "pending":
self.task_assignments[subtask] = agent
self.task_status[subtask] = "assigned"
print(f"Assigned subtask '{subtask}' to agent '{agent}'.")
assigned = True
break
if not assigned:
print(f"Could not assign subtask '{subtask}'.")
def monitor_and_replan(self):
"""Monitor task progress and re-plan if needed."""
for subtask, agent in list(self.task_assignments.items()):
status = random.choice(["completed", "failed"]) # Simulating task outcomes
if status == "completed":
self.task_status[subtask] = "completed"
print(f"Subtask '{subtask}' completed by agent '{agent}'.")
elif status == "failed":
print(f"Subtask '{subtask}' failed by agent '{agent}'. Re-planning...")
self.task_status[subtask] = "pending"
del self.task_assignments[subtask]
self.assign_tasks(subtask)
def execute_plan(self):
"""Simulate execution of assigned tasks."""
while any(status == "pending" or status == "assigned" for status in self.task_status.values()):
self.monitor_and_replan()
# Define tasks and subtasks
planner = HTNPlanner()
planner.add_task("Assemble Product", ["Assemble Frame", "Add Electronics", "Package Product"])
planner.add_task("Assemble Frame", ["Weld Parts", "Bolt Components"])
planner.add_task("Add Electronics", ["Install Circuit Board", "Attach Wires"])
# Define agents and their capabilities
planner.add_agent("Robot A", ["Weld Parts", "Bolt Components"])
planner.add_agent("Robot B", ["Install Circuit Board", "Attach Wires"])
planner.add_agent("Robot C", ["Package Product"])
planner.add_agent("Robot D", ["Weld Parts", "Package Product"]) # Backup agent
# Assign tasks and execute the plan
planner.assign_tasks("Assemble Product")
planner.execute_plan()Key Features in the Enhanced Code
OutputExample simulation: This demonstrates how HTN can adapt to changes dynamically while maintaining efficiency and achieving the overall goal. |
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Behavior-Based CoordinationBehavior-based coordination is an approach to multi-agent systems that relies on simple, modular behaviors rather than centralized planning or complex algorithms. Each agent in the system operates independently, using pre-defined behaviors to respond to its environment and interact with other agents. This model is particularly well-suited to scenarios requiring decentralized control and adaptability. Key Features of Behavior-Based Coordination
Uses of Behavior-Based Coordination
Benefits of Behavior-Based Coordination
Downsides of Behavior-Based Coordination
ConclusionBehavior-based coordination is an elegant and robust approach for multi-agent systems in environments where adaptability and real-time decision-making are critical. While it excels in simplicity and modularity, its limitations in handling complex, goal-oriented tasks make it more suitable for applications requiring reactive and decentralized responses rather than centralized strategic control. |
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I will be categorizing the Coordination ArchitecturesCategorization Summary Table
|
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2 replies
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we can also consider this
This layout ensures clarity and readability for comparing different approaches. |
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I think we can focus more on centralized and decentralized approach |
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Coordination Architecutres
1. Hierarchical Task Networks (HTN)
Concept:
Use Case:
Example in Real Life:
2. Contract Net Protocol (CNP)
3. Market-Based Coordination
4. Centralized Coordination
5. Distributed Problem-Solving (DPS)
6. Emergent Behavior and Swarm Intelligence
7. Behavior-Based Coordination
8. Peer-to-Peer Coordination
9. Game-Theoretic Approaches
10. Plan Merging and Plan Repair
11. Reactive Coordination
Comparison of Coordination Architectures
These architectures demonstrate the breadth of approaches available for multi-agent coordination. Each has trade-offs and is suited to different types of problems, environments, and agent capabilities. Russell and Norvig explore these models to highlight the diversity of AI systems and their potential applications.
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