From c44c1575441dd99761c340421d4a575601813158 Mon Sep 17 00:00:00 2001
From: Bruno Ribeiro <59573315+brunus-reberes@users.noreply.github.com>
Date: Thu, 20 Apr 2023 15:51:36 +0100
Subject: [PATCH] New version release v1.0.5 (#8)
* chore: fix files names in Makefile
* docs: change name of Glossary section to Main PEAK concepts
* chore: change makefile argument
* chore: formatted code with black and isort
* fix: DF gourp hierarchy behavior when removing nodes
* fix: cli not recognizing clones argument
* chore: fix graphs in dashboard in data_extraction example
* chore: bump version 1.0.2 -> 1.0.3
* chore: change random data to more realistic data in data_extration example
* fix: 500 http error when requesting the group data through api
* chore: add command to makefile
* refact: change behaviour execution method name
* chore: format with black and isort
* fix: group tagging
* chore: fix group tagging example
* chore: change file paths format from windows to linux
* chore: added env var to makefile
* refact: changed logger level from error to exception in bootloader
* fix: missing time atribute in message's metadata sent by Synchronizer
* fix: typo in logger and wrong graph name sent to DF
* fix: wait_for method in behaviors not allowing to add template
* docs: clarified some text and added recent academic publications
* chore: balck and isort
* chore: bump version 1.0.3 -> 1.0.4
* refact: simplified graph creation
* fix: drivers import
* fix: modbusdriver update
* refact: add CreateGraph behaviour to the agents
* refact: remove unnucessary behaviours
* docs: add and correct website pages
* docs: correct docstrings
* docs: correct peak module docstring
* refact: change agents structure
* refact: simplefied the PEAK's agent and behaviour structure
* refact: deleted unecessary module
* refect: remove properties model
* refact: remove properties from the agent creation process
* refact: correct agent structure from SyncAgent and Synchronizer
Corrected docs as well.
* fix: inexisting imports from init
* docs: fix example import
* docs: change 'single agent' example folder name
* fix: agent print statements writing in terminal
The stdout of each agent is redirected to the agent log file.
The main process of PEAK logs usefull information in the terminal.
* chores: isort and black
* docs: corrected examples and added images
* docs: corrected the code of the examples
* docs: correct example 4 and 5
* refact: the core agents and behaviours, and the cli commands
* refact: bootloader waits for agents asynchronously
* chore: format code with isort, autoflake and black
* chore: update pyproject.toml
* chore: update makefile
* chore: remove import headers from isort
* chore: format code using isort, black autoflake
* chore: bump version 1.0.4 -> 1.0.5
* chore: update makefile
* chore: format code
---
Makefile | 29 +-
docs/diagrams.excalidraw | 4210 +++++++++++++++++
docs/foreword.md | 2 +-
docs/how-to.md | 223 +-
docs/installation.md | 11 +-
docs/peak_communities.png | Bin 0 -> 241981 bytes
docs/prerequistites.md | 127 +-
docs/xmpp_config.md | 117 +
.../agent.py | 8 +-
examples/2_multiagent_system/mas.yaml | 7 +
examples/2_multiagent_system/receiver.py | 12 +
examples/2_multiagent_system/sender.py | 13 +
examples/3_simple_community/agent.py | 29 +
examples/4_community_tags/agent.py | 34 +
examples/5_community_hierarchy/agent.py | 29 +
.../agent_groups/group_hierarchy/agent.py | 38 -
examples/agent_groups/group_tags/agents.yaml | 7 -
.../agent_groups/group_tags/group_admin.py | 51 -
.../agent_groups/group_tags/group_searcher.py | 25 -
.../agent_groups/hello_world_group/agent.py | 25 -
.../hello_world_group/agents.yaml | 13 -
examples/chalenge_2/mas.yaml | 7 +
examples/chalenge_2/searcher.py | 19 +
examples/chalenge_2/tagger.py | 29 +
examples/data_extraction/agent.py | 27 +-
examples/data_extraction/graph_options.json | 19 +-
examples/data_providers/dataset/agent.py | 3 +-
pyproject.toml | 12 +-
src/peak/__init__.py | 19 +-
src/peak/__main__.py | 85 +-
src/peak/agents.py | 270 +-
src/peak/behaviours.py | 270 +-
src/peak/bootloader.py | 130 +-
src/peak/cli/df.py | 13 +-
src/peak/cli/mas.py | 121 +-
src/peak/core.py | 212 +-
src/peak/drivers.py | 76 -
src/peak/properties.py | 184 -
38 files changed, 5118 insertions(+), 1388 deletions(-)
create mode 100644 docs/diagrams.excalidraw
create mode 100644 docs/peak_communities.png
create mode 100644 docs/xmpp_config.md
rename examples/{hello_world_agent => 1_simple_agent}/agent.py (51%)
create mode 100644 examples/2_multiagent_system/mas.yaml
create mode 100644 examples/2_multiagent_system/receiver.py
create mode 100644 examples/2_multiagent_system/sender.py
create mode 100644 examples/3_simple_community/agent.py
create mode 100644 examples/4_community_tags/agent.py
create mode 100644 examples/5_community_hierarchy/agent.py
delete mode 100644 examples/agent_groups/group_hierarchy/agent.py
delete mode 100644 examples/agent_groups/group_tags/agents.yaml
delete mode 100644 examples/agent_groups/group_tags/group_admin.py
delete mode 100644 examples/agent_groups/group_tags/group_searcher.py
delete mode 100644 examples/agent_groups/hello_world_group/agent.py
delete mode 100644 examples/agent_groups/hello_world_group/agents.yaml
create mode 100644 examples/chalenge_2/mas.yaml
create mode 100644 examples/chalenge_2/searcher.py
create mode 100644 examples/chalenge_2/tagger.py
delete mode 100644 src/peak/drivers.py
delete mode 100644 src/peak/properties.py
diff --git a/Makefile b/Makefile
index 8691d74..59ddbed 100644
--- a/Makefile
+++ b/Makefile
@@ -1,31 +1,12 @@
DOMAIN?=localhost
-all: single_agent single_group group_hierarchy group_tags data_extraction dataset simulation
-
-single_agent:
- peak run examples/hello_world_agent/agent.py -j agent@$(DOMAIN)
-
-single_group:
- peak start examples/agent_groups/hello_world_group/agents.yaml
-
-group_hierarchy:
- peak run examples/agent_groups/group_hierarchy/agent.py -j agent@$(DOMAIN)
-
-group_tags:
- peak start examples/agent_groups/group_tags/agents.yaml
-
-data_extraction:
- peak run examples/data_extraction/agent.py -j agent@$(DOMAIN) -c 4
-
-dataset:
- peak run examples/data_providers/dataset/agent.py -j agent@$(DOMAIN) -p examples/data_providers/dataset/dataset.py
-
-simulation:
- peak start examples/simulation/start.yaml
+lint:
+ pylint src/peak/
format:
+ autoflake --remove-all-unused-imports --remove-unused-variables --expand-star-imports --ignore-init-module-imports -ri .
+ isort --profile black .
black .
- isort .
patch: format
python -m bumpver update --patch
@@ -39,4 +20,4 @@ major: format
publish: format
python -m build
twine check dist/*
- twine upload dist/*
\ No newline at end of file
+ twine upload -u gecad-group dist/*
\ No newline at end of file
diff --git a/docs/diagrams.excalidraw b/docs/diagrams.excalidraw
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diff --git a/docs/foreword.md b/docs/foreword.md
index 2317a5b..31e2bc1 100644
--- a/docs/foreword.md
+++ b/docs/foreword.md
@@ -1,7 +1,7 @@
# Foreword
## What is PEAK and why it was developed?
-PEAK is a framework for developing Multi-Agent Systems (MAS). But you may ask, why another one? Well, the fact is that there is very few frameworks in Python and most of them are for Agent Based Modeling, which are not the same thing. The most used MAS framework is Jade, but the problem with it is that is written in java. Nowadays Python is having a lot of attention when it comes to artificial intelligence algorithms, and unfortunately java is not seeing much improvement in this area. In Python, there are a lot of AI resources out there that can be easily implemented in projects. So we search for MAS frameworks in Python and the only one that filled most of or needs was SPADE. But SPADE lacks some functionalities that we think would be great to see implemented, that is why we developed PEAK. Instead of reinventing the wheel we used SPADE too build a more sophisticated MAS framework that is inspired by others frameworks, like Jade.
+PEAK is a framework for developing Multi-Agent Systems (MAS). But you may ask, why another one? Well, the fact is that there are very few frameworks in Python and most of them are for Agent Based Modeling, which is not the same thing. The most used MAS framework is Jade, but the problem is that it is written in java. Nowadays, Python is having a lot of attention when it comes to artificial intelligence algorithms, and unfortunately java is not seeing much improvement in this area. In Python, there are a lot of AI resources out there that can be easily implemented in projects. So we search for MAS frameworks in Python and the only one that filled most of or needs was SPADE. But SPADE lacks some functionalities that we think would be great to see implemented, that is why we developed PEAK. Instead of reinventing the wheel, we used SPADE to build a more sophisticated MAS framework that is inspired by others frameworks, like Jade.
## Recommendations before using PEAK
Before continuing with PEAK we highly recommend you too go through the SPADE's docs and tutorials so you can be accustomed with SPADE's workflow. Every SPADE's functionality is available in PEAK. PEAK's how to guide only shows the features that are new in the PEAK framework. You only need to know to to create an Agent, how to create and execute Behavior's and how to communicate between agents.
\ No newline at end of file
diff --git a/docs/how-to.md b/docs/how-to.md
index 034d2a1..c3e42c8 100644
--- a/docs/how-to.md
+++ b/docs/how-to.md
@@ -1,9 +1,7 @@
# How-to Guides
+This section will going to guide you through PEAK's fundamental ideas and functionalities. Every example given here is available in the [repository](https://github.com/gecad-group/peak-mas).
-In this section you will going through PEAK's functionalities. Every example given here is available in PEAK's repository.
-
-## Run and configure a MAS
-
+## PEAK Basic principles
You have two options to run PEAK agents. You can run the same way as in SPADE by using a python script or using PEAK's command line interface (CLI).
> **Note:**
@@ -16,18 +14,16 @@ You have two options to run PEAK agents. You can run the same way as in SPADE by
> positional arguments:
> {df,run,start}
> df Execute Directory Facilitator agent.
-> run Execute PEAK's agents.
+> run Execute PEAK agents.
> start Executes multiple agents using a YAML configuration
> file.
>
>optional arguments:
> -h, --help show this help message and exit
-> -version show program's version number and exit
-> -v Verbose. Turns on the debug info.
+> --version show programs version number and exit
> ```
-### Run a single agent
-
+### How to create and Agent ([Example 1](https://github.com/gecad-group/peak-mas/tree/main/examples/1_simple_agent))
Let's run a simple agent.
```python
@@ -61,7 +57,7 @@ The JID is the ID used in the XMPP protocol and is divided by three parts: the l
After you run the agent you will see a new folder appear in the same directory of the `agent.py`. That folder is the `logs` folder and will contain the log files created for each agent you run. You can change the logging level for each agent using the command line argument `-l`. This files come in handy when running complex systems with lots of behaviors. You can track everything the agent does and when it does with the logging functionality.
-### Run multiple agents
+### Communication between Agents ([Example 2](https://github.com/gecad-group/peak-mas/tree/main/examples/2_multiagent_system))
To run multiple agents at the same time you can use a configuration file in YAML format.
```python
@@ -70,13 +66,13 @@ from peak import Agent, OneShotBehaviour, Message
class sender(Agent):
class SendHelloWorld(OneShotBehaviour):
- async def run(self) -> None:
+ async def run(self):
msg = Message(to="harry@localhost")
msg.body = "Hello World"
await self.send(msg)
await self.agent.stop()
- async def setup(self) -> None:
+ async def setup(self):
self.add_behaviour(self.SendHelloWorld())
```
@@ -86,39 +82,45 @@ from peak import Agent, OneShotBehaviour
class receiver(Agent):
class ReceiveHelloWorld(OneShotBehaviour):
- async def run(self) -> None:
+ async def run(self):
while msg := await self.receive(10):
print(f"{msg.sender} sent me a message: '{msg.body}'")
await self.agent.stop()
- async def setup(self) -> None:
+ async def setup(self):
self.add_behaviour(self.ReceiveHelloWorld())
```
```yaml
-# multiagent.yaml
+# mas.yaml
defaults:
domain: localhost
- log_level: debug
agents:
john:
file: sender.py
harry:
file: receiver.py
- log_level: info
```
-Let's create two agents one that sends the a message, the `sender.py`, and one that receives the message, the `receiver.py`. In the same directory create the YAML file above with the name `multiagent.yaml`. After that, run the following command:
+Let's create two agents one that sends the a message, the `sender.py`, and one that receives the message, the `receiver.py`. In the same directory create the YAML file above with the name `mas.yaml`. After that, run the following command:
```bash
-$ peak start multiagent.yaml
+$ peak start mas.yaml
```
+<<<<<<< HEAD
+So, what happened? Two agents were created. One called `john@localhost` and the other called `harry@localhost`. `john` sent a `Hello World` to `harry` and `harry` printed it out.
+
+The way it works is simple. You can only define two root variables, the `defaults` and the `agents`. The `defaults` is used to define parameters to be applied to all agents. The `agents` variable defines the list of agents to be executed and their individual parameters. Type `peak start -h` on the terminal to see the list of available parameters.
+
+In this case we are defining, in the `defaults`, the default domain as `localhost` for all agents. In `agents` variable, we are defining two different types of agents, the `john` and the `harry`. In both agents we are defining their source file. The `agents` parameters will override the `defaults` parameters if they are the same.
+=======
So, what happened? Two agents were created. One called `john@localhost` and the other called `harry@localhost`. `john` sent a message `Hello World` to `harry` and `harry` printed it out. The log file of `jonh` was in logging level `DEBUG`, and `harry`'s file was in level `INFO`.
The way it works is simple. You can only define two root variables, the `defaults` and the `agents`. The `defaults` is used to define parameters to be applied to all agents. The `agents` variable defines the list of agents to be executed and their respective parameters. The parameters available in `defaults` and in the agents of the variable `agents` can be seen using the `-h` argument in the `peak run` command.
In this case we are defining, in the `defaults`, the default domain as `localhost` and the default logging level as `debug` for all agents. In `agents` variable, we are defining two different types of agents, the `john` and the `harry`. In `john` we are defining the agents source file. In `harry` we are defining the source file and the logging level, overriding the default value.
+>>>>>>> main
There is the list of options that you can define in the configuration file, inside each agent and in the `defaults` variable:
- `file` - source file of the agent
@@ -126,142 +128,189 @@ There is the list of options that you can define in the configuration file, insi
- `resource` - resource to be used in the JID
- `log_level` - logging level of the log file
- `clones` - number of clones to be executed
+<<<<<<< HEAD
+=======
- `properties` - source file of the agent's properties (more on that later)
+>>>>>>> main
- `verify_security` - if present verifies the SSL certificates
### Thread vs. Process
_In development_
+This section will talk about how to run agents as different threads of the same process or as multiple processes and the benefits and use cases for each approach.
+
+## PEAK Communities
+
+<<<<<<< HEAD
+In PEAK, communities can be seen as groups of agents that share similar goals. Communities are a very useful and efficient way to make communication between three or more agents. What makes this usefull is that for each message sent to the community every member will receive the message.
+
+
-## Create a group of agents
+For this examples you will need to execute a pre-defined PEAK agent called Directory Facilitator, for short DF. He is responsable to maintain the PEAK communities structures. More details about the DF [here]().
+### Creating a community ([Example 3](https://github.com/gecad-group/peak-mas/tree/main/examples/3_simple_community))
+
+To create a community is very simple. There is a pre defined behavior that enables the agent join communities. For only this functionality you don't need DF, but it is recommended.
+=======
The groups are a very useful way to make the communication between more than two agents. To create a group is very simple. There is a pre defined behavior that enables the agent to create and join groups.
+>>>>>>> main
```python
#agent.py
-from peak import Agent, JoinGroup, OneShotBehaviour, Message
from asyncio import sleep
+from peak import Agent, JoinCommunity, LeaveCommunity, Message, OneShotBehaviour
+
+
class agent(Agent):
class HelloWorld(OneShotBehaviour):
+<<<<<<< HEAD
+ async def on_start(self):
+ await self.wait_for(JoinCommunity("group1", f"conference.{self.agent.jid.domain}"))
+=======
async def on_start(self) -> None:
await self.wait_for(JoinGroup("group1", f"conference.{self.agent.jid.domain}"))
+>>>>>>> main
- async def run(self) -> None:
+ async def run(self):
msg = Message(to=f"group1@conference.{self.agent.jid.domain}")
msg.body = "Hello World"
- await self.send_to_group(msg)
+ await self.send_to_community(msg)
await sleep(5)
msg.body = "Goodbye World"
- await self.send_to_group(msg)
+ await self.send_to_community(msg)
+ self.kill()
+
+ async def on_end(self):
+ await self.wait_for(LeaveCommunity("group1", f"conference.{self.agent.jid.domain}"))
await self.agent.stop()
- async def setup(self) -> None:
+ async def setup(self):
self.add_behaviour(self.HelloWorld())
```
-As you can see in the example above, the agent has a behavior `HelloWorld`. This behavior will first use the `JoinGroup` behavior to join a group called 'group1@conference.localhost' (assuming the domain is ``localhost``). If the group does not exists it will create it. It waits until the agent joins the group. After that it will run the `run` function. The ``run`` function will send a `Hello World` message to the group, waits for 5 seconds and then sends a `Goodbye World` and exits. If you want to leave the group without terminating the agent you can import the ``LeaveGroup`` behavior from ``peak`` package and use it the same way as the `JoinGroup`.
+Then run the following command:
+```bash
+$ peak run agent.py -j dummy@localhost
+```
+As you can see in the example above, the agent has a behavior `HelloWorld`. This behavior will first use the `JoinCommunity` behavior to join a community called `group1@conference.localhost`. If the community does not exists it will create it automatically. It waits until the agent joins the community. After that it will send a `Hello World` message to the community, waits for 5 seconds and then sends a `Goodbye World` and leaves the community with the `LeaveCommunity` behaviour.
> **Note:**
-> For the this functionality to work the server must have Multi-User Chat functionality activated. You need to create a component in the server and give it a prefix, in this case is 'conference'.
+> For the this functionality to work the server must have Multi-User Chat functionality activated. You need to create a component in the server and give it a prefix, in this case is 'conference'. See [this](xmpp_config.md) server configuration file example.
> **Tip:**
> You can see the messages being sent if you use the XMPP client and enter in the same room as the agent. Is a good way to debug the multi-agent.
-### Group tagging
+> **Chalenge 1:**
+> Try to implement the [Communication between agents](#communication-between-agents-example-2) example using communities. ([Solution]())
+
+### Community tagging ([Example 4](https://github.com/gecad-group/peak-mas/tree/main/examples/4_community_tags))
-Group tagging, as the name suggests, is for tagging the groups. This allows the agents to identify the groups and then search for them using the tags to filter the groups. Let's see.
+Community tagging, as the name suggests, is for tagging communities. This allows the agents to identify the communities and then search for them using the tags to filter them. Let's see.
```python
-#group_searcher.py
-from peak import Agent, JoinGroup, SearchGroup
-import logging
-logger = logging.getLogger(self.__class__.__name__)
+#agent.py
+from peak import Agent, JoinCommunity, LeaveCommunity, OneShotBehaviour, SearchCommunity
-class group_searcher(Agent):
- async def setup(self) -> None:
- self.add_behaviour(
- JoinGroup("group1", "conference.localhost", ["test", "awesome", "cool"])
- )
- self.add_behaviour(
- JoinGroup("group2", "conference.localhost", ["test", "awesome"])
- )
- self.add_behaviour(
- JoinGroup("group3", "conference.localhost", ["test"],)
- )
- self.add_behaviour(JoinGroup("group4", "conference.localhost"))
-
- def print_groups(tags, groups):
- logger.info(str(tags), str(groups))
+
+class agent(Agent):
+ class TagCommunities(OneShotBehaviour):
+ async def run(self) -> None:
+ self.agent.add_behaviour(
+ JoinCommunity(
+ "group1",
+ f"conference.{self.agent.jid.domain}",
+ ["test", "awesome"],
+ )
+ )
+ self.agent.add_behaviour(
+ JoinCommunity(
+ "group2", f"conference.{self.agent.jid.domain}", ["test"]
+ )
+ )
+ def print_communities(tags, communities):
+ print(f"Communities ({tags}): {communities}")
+
+ await self.wait_for(SearchCommunity(["awesome"], print_communities))
+ await self.wait_for(SearchCommunity(["test"], print_communities))
- self.add_behaviour(SearchGroup(["test"], print_groups))
- self.add_behaviour(SearchGroup(["awesome"], print_groups))
- self.add_behaviour(SearchGroup(["awesome", "cool"], print_groups))
+ await self.wait_for(
+ LeaveCommunity("group1", f"conference.{self.agent.jid.domain}")
+ )
+ await self.wait_for(
+ LeaveCommunity("group2", f"conference.{self.agent.jid.domain}")
+ )
+ await self.agent.stop()
+
+ async def setup(self) -> None:
+ self.add_behaviour(self.TagCommunities())
```
-In the example above we create an agent called `group_searcher`. This agent will create 4 different groups: `group1@conference.localhost` with the tags `test`, `awesome` and `cool`; `group2@conference.localhost` with the tags `test` and `awesome`; `group3@conference.localhost` with the tag `test`; and finally the fourth group `group4@conference.localhost`. After that will search for groups using the `SearchGroup` behavior.
-Firstly, the `JoinGroup` is used to create the groups and tag them. The groups can have more than one tag. To search the groups it's used the `SearchGroup` behavior. You can search for more than one tag, but be careful because it used conjugation to search for them. In other words it will get you the list of groups that have all the tags the you mentioned.
+For this to work you need to execute the DF agent (more details [here]()). In the example above we create an agent that will create and tag two communities: `group1@conference.localhost` with tags `test` and `awesome`, and `group2@conference.localhost` with tag `test`. After that will search for communities using the `SearchCommunity` behavior. Note that for the `SearchCommunity` and for `LeaveCommunity` behaviours we do not add the behaviour directly to the agent, because the agent would stop before the behaviours would be accomplished. We use `wait_for` method, available in every behaviour, to wait for the completeness of the behaviour before continuing, making the behaviour synchrounous.
-### Group Hierarchy
+In simple words the tagging system will search for the communities that have the tags that you want. For instance, if you search for three tags it will give you every community that have all those three tags.
-The group hierarchy allows the user to create subgroups in different depths. This does not only allow you to organize your multiagent system (e.g., society of agent) but also to communicate more efficiently through the hierarchical branches. You can send a single message to every agent that is in a specific branch, or every agent bellow some specific node.
+> **Chalenge 2:**
+> Try to implement this example with two agents: one that creates and tags the communities and other that searches for them. ([Solution](https://github.com/gecad-group/peak-mas/tree/main/examples/chalenge_2))
-For this functionality you need to activate the Directory Facilitator (DF) agent. DF coordinates and monitors the hierarchical structure. To activate it you just need to run the following command:
+### Community hierarchy ([Example 5](https://github.com/gecad-group/peak-mas/tree/main/examples/5_community_hierarchy))
-```bash
-$ peak df
-```
+The community hierarchy allows the user to create different levels of communities associated with one another. This does not only allow your agents to organize themselves but also to communicate more efficiently through the hierarchical branches. For instance, consider the hirarchy bellow.
+
+_Image_
+
+For every community of the hierarchy two nodes are created: the actual node of the community, which only the members can interact, and a node which has all the members of that community all the way down to the roots. This special node that we call the `echo node` will work has an echoer for the whole branch beneaf. In other words, if you want to send a message from X to Y you just need to send a message to the X_down node.
+
+Let's see an example of the community hierarchy.
-To change the server domain use the `-d` argument.
-Let's see an example of the group hierarchy.
```python
#agent.py
+# Standard library imports
from asyncio import sleep
-from peak import Agent, JoinGroup, LeaveGroup, Message, OneShotBehaviour
+
+# Reader imports
+from peak import Agent, JoinCommunity, LeaveCommunity, Message, OneShotBehaviour
+
class agent(Agent):
class HelloWorld(OneShotBehaviour):
async def run(self):
- self.agent.add_behaviour(
- JoinGroup("mas/retina/teste", f"conference.{self.agent.jid.domain}")
- )
- msg = Message(
- to=f"retina@conference.{self.agent.jid.domain}"
- )
- msg.body = "Hello World"
- await self.send_to_group(msg)
+ groups_tree = [
+ "peak/A0/B0",
+ "peak/A0",
+ "peak/A1",
+ "peak/A2/B2/C0",
+ "peak/A1/B3/C1",
+ ]
+ for groups_branch in groups_tree:
+ await self.wait_for(
+ JoinCommunity(groups_branch, "conference." + self.agent.jid.domain)
+ )
+ await sleep(1)
+ for groups_branch in groups_tree:
+ await self.wait_for(
+ LeaveCommunity(groups_branch, "conference." + self.agent.jid.domain)
+ )
+ await sleep(1)
+ await self.agent.stop()
async def setup(self):
self.add_behaviour(self.HelloWorld())
```
-In the JoinGroup behavior you can specify a path of groups. What these does is create an hierarchy of groups, being the first group the root and the last group an intermediate node or a leaf. In this path we call the last node the target group, because you only enter in that group specifically. Another interesting thing about this functionality is that for each node behind the target node you enter in a specially group which the names ends in \_down. The ideia behind this is to send messages to the whole branch, so every agent that enters a node bellow enters in this group. So if we want to send a message to the whole multi agent system we only have to send a message to, in the case of this example, ``mas_down``. In the example above the agent entered in
+For this to work you need to execute the DF agent (more details [here]()). In the `JoinCommunity` behavior you can specify a path representing the hierarchy of the communities. For instance in path `peak/A0/B0`, `peak` is the root community, beneath is community `A0`, at level 1, and `B0` at level 2. The last node of the path does not need necessarilly to be a leaf. We call the last node the target onde, because you only enter in that community specifically.
> **Note:**
> In the Dashboard, the groups that end in \_down do not appear in the node graph.
-## Create a simulation environment
+## Simulation Environment
### Clock
### Dynamic clock
_In development_
-## Integrate data providers
-
-Normally a multi-agent system uses external data so it can process that data. In PEAK you can integrate different data providers like files from the file system of your machine, databases in the web and from real devices using some real-time communication protocol.
-
-### File System
-- Excel
-- CSV
-
-### Smart devices
-- ModBus/TCP
-- HTTPs
-- Extending
-
## PEAK Dashboard
-
The PEAK Dashboard is a separate project from PEAK. The Dashboard allows you to see in an interactive way the PEAK ecosystem using a web app. The Dashboard needs a Directory Facilitator (DF) agent in the XMPP server so it can monitor the system. The Dashboard communicates with the DF through its REST API.
To know how to activate the DF run the following command:
```bash
$ peak df -h
```
-### Ecosystem Insight
+### Ecosystem Overview
### Data Analysis
### Full Plot Customization
\ No newline at end of file
diff --git a/docs/installation.md b/docs/installation.md
index af4bddb..e6c2ac8 100644
--- a/docs/installation.md
+++ b/docs/installation.md
@@ -1,13 +1,18 @@
# Installing PEAK
Here we are going to teach you how to install PEAK.
-To install PEAK with [pip](https://pip.pypa.io/en/stable/) just type this in the terminal:
+Firstly, you need to install Python v3.9.6. After that, you can install PEAK by using [pip](https://pip.pypa.io/en/stable/) command like this:
```bash
$ pip install peak-mas
```
-To install via [conda](https://docs.conda.io/en/latest/miniconda.html) you need to download the environment.yml file available in the repository, and then just type this following commands:
+Another way to install PEAK is via [conda](https://docs.conda.io/en/latest/miniconda.html). For that, you can download the env.yml file from the repository, and type these commands in the terminal:
```bash
$ conda env create -f environment.yml
$ conda activate peak
```
-Now your ready to go!
\ No newline at end of file
+Test if it is well installed by using one of the commands bellow:
+```bash
+$ peak
+$ python -m peak
+```
+Now you are ready to go!
\ No newline at end of file
diff --git a/docs/peak_communities.png b/docs/peak_communities.png
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