💡 Knowledge and reasoning¶

Your robot includes a knowledge base called KnowledgeCore. Thanks to the knowledge base, you can store, query and reason on facts about the robot activities and environment.

Knowledge management on the robot¶

The knowledge base is a central component of the robot’s software architecture.

Your mission controller can use the knowledge base to:

store facts about the robot’s environment and activities
query the knowledge base to make decisions
subscribe to events to be notified when some conditions are met

KnowledgeCore stores OWL/RDF triples, and provides an API accessible via a simple Python wrapper or a ROS 2 API.

KnowledgeCore can also perform symbolic reasoning: it integrates with the reasonable OWL2 RL reasoner to provide OWL2 semantics and fast knowledge materialisation.

The OpenRobots ontology¶

You robot comes pre-loaded with the OpenRobots ontology (ORO). This ontology provides a common vocabulary to describe robots, their environment and activities.

Learn more about the OpenRobots ontology (ORO).

Run-time knowledge providers¶

Some components of the robot can provide knowledge to the knowledge base at run-time.

Learn more about the run-time knowledge providers.

Simple example of knowledge base usage¶

This example uses our simple Python wrapper to interact with the knowledge base:

import rclpy
from rclpy.node import Node
from knowledge_core.api import KB

rclpy.init()
node = Node("test_kb_client")
kb = KB(node)

def on_robot_entering_clara_property(evt):
  print("A robot entered Clara's %s: %s" % (evt[0]["place"], evt[0]["robot"]))

kb += "ari rdf:type Robot"
kb += ["clara looksAt ari", "ari isIn kitchen"]

kb.subscribe(["?robot isIn ?place", "?place belongsTo clara", "?robot rdf:type Robot"], on_robot_entering_clara_property)

# ...nothing happens yet, as we have not told the robot that the 'kitchen'
# belonged to 'clara'...

kb += "kitchen belongsTo clara"

rclpy.spin(node)
# the event is now triggered!


# try as well:
kb -= "clara looksAt ari" # to remove facts
print(kb["* rdf:type Robot"]) # to query the knowledge base

will print:

A robot entered Clara's kitchen: ari

KnowledgeCore features¶

In addition to storing and querying facts, KnowledgeCore has several advanced features.

Multi-models¶

KnowledgeCore is intended for dynamic environments, with possibly several contexts/agents requiring separate knowledge models.

New models can be created at any time and each operation (like knowledge addition/retractation/query) can operate on a specific subset of models.

Each models are also independently classified by the reasoner.

See KnowledgeCore models for details.

Event system¶

KnowledgeCore provides a mechanism to subscribe to some conditions (like: an instance of a given type is added to the knowledge base, some statement becomes true, etc.) and get notified back.

Reasoning¶

KnowledgeCore provides RDFS/OWL reasoning capabilities via the reasonable reasoner.

See reasonable README for the exact level of support of the different OWL2 RL rules.