A lightweight multi-agent system

# MASlite

A multi-agent platform contrived by Bjorn Madsen

All right reserved © 2016-2021. MIT-license. All code has been written by the author in isolation and any similarity to other systems is purely coincidental.

New in version 2021.2.2+

• dropping support for python 3.5.

• subscribe now permits topic and agent id to limit the messages received.

• copy does no longer use deepcopy and will raise if you don't have a copy method!
Let me emphasise: All messages must have a copy() method.

• The Scheduler class must be initiated with one of two modes: real-time and simulated time.

• real-time uses the clock / time.time() (default)
• simulated time uses a jumping clock.
• Agent.set_alarm(..., relative=True, ....)
defaults to True as most use cases use the alarm as "check again in x seconds"

• Agent.list_alarms(receiver=None) returns a list of (time, alarm message) set for the receiver.

Allows, for example, an agent A to read whether an alarm has been set for agent B, and thereby refrain from setting an additional alarm.

• broadcasts are subscription based. To have broadcast-like methods, each agent must subscribe first. By default agents subscribe to their uuid and class name. But beware:

Some users have been surprised that the broadcasting method could overflow their memory usage, so this is a safeguard. In the auction demo, 1000 sellers sending 1 offer to each of 1000 buyers which generates 1,000,000 messages. Instead of keeping a pool of 1,000,000 messages, where most are irrelevant, the new subscribe system, encourages that the developer actively add and remove subscriptions, for example by subscribing to NewSeller announcements, and afterwards only subscribe to specific agents that have offered prices within budget (or some other criteria).

#### MASlite explained in 60 seconds:

MASlite is a simle python module for creating multi-agent simulations.

• Simple API: Only 3 modules to learn: Scheduler, Agent & Agent message
• Fast: Handles up to 270 million messages per second
• Lightweight: 52kB.

It only has 3 components:

• The scheduler (main loop)

• handles pause and proceed with a single call.
• assures repeatability in execution, which makes agents easy to debug.
• handles up to 270 million messages per second.
• Agent's

• are python classes that have setup(), update() and teardown() methods that can be customized.
• can exchange messages using send() and receive().
• can subscribe/unsubscribe to message classes.
• have clocks and can set alarms.
• can be tested individually.
• can have independent I/O/Database interaction.
• Messages

• that have sender and receiver enable direct communication
• that have topics and no receiver are treated as broadcasts, and sent to subscribers.

The are plenty of use-cases for MASlite:

• Prototyping MASSIVE™ type games.
• Creating data processing pipeline
• Optimisation Engine, for:
• Scheduling (using Bjorn Madsen's distributed scheduling method)
• Auctions (using Dimtry Bertsekas alternating iterative auction)

All the user needs to worry about are the protocols of interaction, which conveniently may be summarised as:

1. Design the messages that an agent will send or receive as regular python objects that inherit the necessary implementation details from a basic AgentMessage. The messages must have an unambiguous topic.
2. Write the functions that are supposed to execute once an agent receives one of the messages.
3. Update the agents operations (self.operations) with a dictionary that describes the relationship between topic and function.
4. Write the update function that maintains the inner state of the agent using send to send messages, and using receive to get messages.

The user can thereby create an agent using just:

class HelloMessage(AgentMessage):

class myAgent(Agent):
def __init__(self):
super().__init__()
self.operations[HelloMessage.__name__] = self.hello

def update(self):
while self.messages:
operation = self.operations.get(msg.topic))
if operation is not None:
operation(msg)
else:
self.logger.debug("%s: don't know what to do with: %s" % (self.uuid), str(msg)))

def hello(self, msg)
print(msg)


That simple!

The dictionary self.operations which is inherited from the Agent-class is updated with HelloMessage.__name__ pointing to the function self.hello. self.operations thereby acts as a pointer for when a HelloMessage arrives, so when the agents update function is called, it will get the topic from the message's and point to the function self.hello, where self.hello in this simple example just prints the content of the message.

More nuanced behaviour, can also be embedded without the user having to worry about any externals. For example if some messages take precedence over others (priority messages), the inbox should be emptied in the beginning of the update function for sorting.

Here is an example where some topics are treated with priority over others:

class AgentWithPriorityInbox(Agent):
def __init__(self):
super().__init__()
self.operations.update({"1": self.some_priority_function,
"2": self.some_function,
"3": self.some_function,  # Same function for 2 topics.!
"hello": self.hello, })
self.priority_topics = ["1","2","3"]
self.priority_messages = deque()  # from collections import deque
self.normal_messages = deque()    # deques append and popleft are threadsafe.

def update(self):
# 1. Empty the inbox and sort the messages using the topic:
while self.messages:
if msg.topic in self.priority_topics:
self.priority_messages.append(msg)
else:
self.normal_messages.append(msg)

# 2. We've now sorted the incoming messages and can now extend
# the priority message deque with the normal messages:
self.priority_messages.extend(normal_messages)

# 3. Next we process them as usual:
while self.priority_messages:
msg = self.priority_messages.popleft()
operation = self.operations.get(msg.topic)
if operation is not None:
operation(msg)
else:
...


The only thing which the user needs to worry about, is that the update function cannot depend on any externals. The agent is confined to sending (self.send(msg)) and receiving (msg = self.receive()) messages which must be processed within the function self.update. Any responses to sent messages will not happen until the agent runs update again.

If any state needs to be stored within the agent, such as for example memory of messages sent or received, then the agents __init__ should declare the variables as class variables and store the information. Calls to databases, files, etc. can of course happen, including the usage of self.setup() and self.teardown() which are called when the agent is, respectively, started or stopped. See the boiler-plate (below) for a more detailed description.

### Boilerplate

The following boiler-plate allows the user to manage the whole lifecycle of an agent, including:

1. add variables to __init__ which can store information between updates.
2. react to topics by extending self.operations
3. extend setup and teardown for start and end of the agents lifecycle.
4. use update with actions before(1), during(2) and after(3) reading messages.

There are no requirements, for using all functions. The boiler-plate merely seeks to illustrate typical usage.

There are also no requirements for the agent to be programmed in procedural, functional or object oriented manner. Doing that is completely up to the user of MASlite.

class Example(Agent):
def __init__(self, db_connection):
super().__init__()
self._is_setup = False
self.db_connection = db_connection

# remember to register topics and their functions:
self.operations.update({"topic x": self.x,
"topic y": self.y,
"topic ...": self....})

def update(self):
assert self._is_setup

# do something before reading messages
self.action_before_processing_messages()

while self.messages:

# react immediately to some messages:
operation = self.operations.get(msg.topic)
if operation is not None:
operation(msg)

# react after reading all messages:
self.action_after_processing_all_messages()

# Functions added by the user that are not inherited from the
# Agent-class. If the update function should react on these,
# the topic of the message must be in the self.operations dict.

def setup(self):
self._is_setup = True
# add own setup operations here.
self.subscribe(self.__class__.__name__)

def action_before_processing_messages(self)
# do something.

def action_after_processing_all_messages(self)
# do something. Perhaps send a message to somebody that update is done?
self.send(msg)

def x(msg):
# read msg and send a response
from_ = msg.sender
self.send(response)

def y(msg):
with db_connection as db.:
db.somefield.update(time.time())

def teardown(self):
# add own teardown operations here.
self.db_connection.close()


### Messages

Messages are objects and are required to use the base class AgentMessage.

When agents receive messages they should be interpreted by their topic, which should (by convention) also be the class name of the message. Practice has shown that there are no obvious reasons where this convention shouldn't apply, so messages which don't have a topic declared explicitly inherit the class name. An example is shown below:

>>> from maslite import AgentMessage
>>> class MyMsg(AgentMessage):
...

>>> m.topic

'MyMsg'


Adding functions to messages. Below is an example of a message with it's own function(s):

class DatabaseUpdateMessage(AgentMessage):
""" Description of the message """
def __init__(self, sender, senders_db_alias):
self.senders_db_alias
self._states = {1: 'new', 2: 'read'}
self._state = 1

def get_senders_alias(self):
return self.senders_db_alias

def __next__(self)
if self._state + 1 <= max(self._states.keys()):
self._state += 1

def state(self):
return self._states[self._state]


The class DatabaseUpdateMessage is subclassed from the AgentMessage so that the basic message handling properties are available for the DatabaseUpdateMessage. This helps the user as s/he doesn't need to know anything about how the message handling system works.

The init function requires a sender, which normally defaults to the agent's self. The AgentMessage knows that if it gets an agent in it's __init__ call, it will obtain the agents UUID and use that. Similar applies to a receiver, where the typical operation is based on that the local agent gets a message from the sender and only knows the sender based on msg.get_sender() which returns the sending agents UUID. If the sender might change UUID, in the course of multiple runs, the local agent should be instructed to use, for example, the senders_db_alias. For the purpose of illustration, the message above contains the function get_senders_alias which then can be persistent over multiple runs.

The message is also designed to be returned to save pythons garbage collector: When the DatabaseAgent receives the message, the __next__-function allows the agent to call next(msg) to progress it's self._state from '1' (new) to '2' (read) before returning it to the sender using 'self.send(msg)'. In such case it is important that the DatabaseAgent doesn't store the message in its variables, as the message must not have any open object pointers when sent. This is due to multiprocessing which uses multiprocessing.queues for exchanging messages, which require that Agents and AgentMessages can be pickled.

If an Agent can't be pickled when added to the Scheduler, the scheduler will raise an error explaining that the are open pointer references. Messages are a bit more tolerant as the mailman that manages the messages will try to send the message and hope that the shared pointer will not cause conflicts. If sharing of object pointers is required by the user (for example during prototyping) the scheduler must be set up with number_of_multiprocessors=0 which forces the scheduler to run single-process-single-threaded.

Message Conventions:

• Messages which have None as receiver are considered broadcasts. The logic is that if you don't know who exactly you are sending it to, send it it to None, and you might get a response if any other agent react on the topic of the message. The magic behind the scenes is handled by the schedulers mail manager which keeps track of all topics that any Agent subscribes to. By convention the topic of the message should be self.__class__.__name__.

• Messages which have a class.__name__ as receiver, will be received by all agents of that class. This is configured when the agent is added to the scheduler in s.add(agent)

• Messages which have a particular UUID as receiver, will be received by the agent holding that UUID. If anyone other agent is tracking that UUID, by subscribing to it, then the tracking agent will receive a deepcopy of the message, and not the original. If the message has a copy method, this will be used instead of deepcopy.

• To get the UUID of the sender the method msg.sender is available.

• To subscribe/unsubscribe during runtime the agents should use the subscribe function directly.

### How to load data from a database connection

When agents are added to the scheduler setup is run. When agents are removed from teardown is run.

if agents are added and removed iteratively, they should load their state during setup and store it during teardown from some database. It is not necessary to let the scheduler know where the database is. The agents can keep track of this themselves.

Though the user might find it attractive to use uuid to identify, a particular Agent the user should set the uuid in super().__init__(uuid="this"), as a the uuid otherwise will be given be the scheduler.

### Getting started

To get started only 3 steps are required:

Step 1. setup a scheduler

>>> from maslite import Agent, Scheduler
>>> s = Scheduler()


Step 2. create agents which have an update method and (optionally) a setup and teardown.

>>> class MyAgent(Agent):
...     def __init__(self):
...         super().__init__()
...     def setup(self):
...         pass
...     def teardown(self):
...         pass
...     def update(self):
...         pass

>>> m = MyAgent()


Step 3. run the scheduler (nothing happens here)

>>> s.run(pause_if_idle=True)


Other methods such as s.run(seconds=None, iterations=None, pause_if_idle=False) can be applied as the user finds it suitable.

Step 4. to stop the scheduler there are the following options:

1. Let it run until idle (most common)
2. Run for N seconds (suitable for real-time systems),
3. Run for N iterations (suitable for interrupt checking)

Then leave the scheduler (and all the agents) in their set state, for example to read the state of particular agents; and finally execute the teardown method, on all agents in a loop:

>>> for uid, agent in s.agents.items():
...     agent.teardown()


### Debugging with pdb or breakpoints (PyCharm)

Debugging is easily performed by putting breakpoint at the beginning of the update function. In that way you can watch what happens inside the agent during its state-update.

### Typical mistakes

The user constructs the agent correctly with:

1. the methods update, send, receive, setup and teardown,
2. adding the agent to the scheduler using scheduler.add(agent).
3. runs the scheduler using scheduler.run(),

...but...

Q: The agents don't seem to update?

A: The agents are not getting any messages and are therefore not updated. This is correct behaviour, as update only should run when there are new messages! To force agents to run update in every scheduling cycle, use the hidden method: agent.keep_awake=True. Doing this blindly however is a poor design choice if the agent merely is polling for data. For this purpose agent.set_alarm_clock(agent.now()+1) should be used, as this allows the agent to sleep for 1 second and the be "woken up" by the alarm message.

The reason it is recommended to use the alarm instead of setting keep_awake=True is that the workload of the system remains transparent at the level of message exchange. Remember that the internal state of the agents should always be hidden whilst the messages should be indicative of any activity.

...

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