KNOWROB - OVERVIEW
Present by: Nguyen Huu Quang
What is KNOWROB?
• KNOWROB is a knowledge processing system that combines
knowledge representation and reasoning methods with
techniques for acquiring knowledge and for grounding the
knowledge in a physical system.
• It can serve as a common semantic framework for integrating
information from different sources and is used in
ROBOEARTH as local knowledge base on the robot.
• Compared to RAPYUTA: RAPYUTA is the ROBOEARTH
Cloud Engine. It helps robots to off-load heavy computation
by providing secure customizable computing environments in
the cloud.
General concepts of KNOWROB
• The world as a virtual knowledge base:
– KNOWROB do not pre-compute all knowledge that could
be needed and push this information into the knowledge
base.
– KNOWROB give the knowledge base the ability to
compute knowledge on demand when it is actually needed,
and to ask other components if the required information is
not available.
General concepts of KNOWROB
• On-demand computation:
– One important application of computables is to load
information into the knowledge base. Computable classes
can for example generate object instances by asking the
vision system for the objects it has detected. Another
application is to compute qualitative relations between
objects. If the object poses are known, qualitative spatial
relations like “in”, “on”, or “next to” can easily be
computed on demand.
General concepts of KNOWROB
• Logic-based representation:
– In KNOWROB, we choose Description Logics (DL) as
formalism to represent the robot’s knowledge. Description
logics are a family of logical languages for knowledge
representation, consisting of several dialects with different
expressiveness, most of which are a decidable subset of
first-order logic. In particular, we use the Web Ontology
Language-OWL for storing Description Logic formulas in
an XML-based file format.
General concepts of KNOWROB
• Prolog-based inference:
– KNOWROB is implemented based on Prolog because
inference in Prolog is mainly a search procedure, and it is
very easy to add additional alternatives to each step by just
defining an additional predicate.
General concepts of KNOWROB
• Modular design:
– KNOWROB is designed to be usable on a wide range of
robot platforms with different capabilities (and even on
non-robotic systems).
– To flexibly add, remove, or exchange parts of its
functional-ity, it is implemented in a very modular way.
– Each module can provide two kinds of extensions: First, it
can contain additional knowledge as an extension of the
KNOWROB ontology, and second also additional
reasoning or computation capabilities, realized as new
computable classes or properties.
System architecture
• Functional overview of KNOWROB:
System architecture
• Functional overview of KNOWROB:
Description logic inference
• A knowledge processing system needs methods to store
knowledge, to query for it, and to combine pieces of
knowledge to perform logical inferences.
• There are several existing reasoners: Racer, Pellet, HermiT, but
unfortunately they are not well-suited to robotics applications.
• KNOWROB choose a solution based on Prolog: The
knowledge is internally represented in terms of Prolog
predicates to which the common Prolog inference methods can
be applied.
Description logic inference
• In KNOWROB, all knowledge is represented in the OWL
(Web Ontology Language).
• KnowRob is implemented in SWI Prolog.
– Prolog is used for loading, storing and reasoning on the
knowledge which is represented in OWL.
– Prolog is used as a programming language to implement
specialized reasoning modules and to interface the
knowledge base with external data.
Programming languages ?
• KNOWROB is implemented in a combination of OWL,
Prolog and Java.
• OWL:
– Description language, no programming language
– Class taxonomy of objects, actions, events,…
– Instances of these classes (e.g. environment models,
experiences)
– Robot capabilities/action requirements
Programming languages ?
• KNOWROB is implemented in a combination of OWL,
Prolog and Java.
• Prolog:
– Logical programming language: conceptually close to the
knowledge representation, good at searching and pattern
matching
– Useful for functionality interacting closely with the internal
representation (OWL gets parsed into Prolog triples)
– Wrapper predicates to simplify commonly used queries,
inference predicates (DL inference, computables,…)
– Functionality involving (recursive) search, graph matching,
reasoning about ontological structure, …
Programming languages ?
• KNOWROB is implemented in a combination of OWL,
Prolog and Java.
• Java:
– Object-oriented programming language that can interact
with Prolog via the JPL interface
– Conceptually further away from the internal knowledge
representation
– Good library support, therefore useful for external
interfaces (WWW, ROS,…) and for integrating libraries
(ProbCog, Weka, Visualization…)
Programming languages ?
• Introduction to Prolog:
– There are only three basic constructs in Prolog: facts, rules,
and queries. A collection of facts and rules is called a
knowledge base (or a database) and Prolog programming is
all about writing knowledge bases.
– Prolog programs simply are knowledge bases, collections
of facts and rules which describe some collection of
relationships.
– How do we use a Prolog program? By posing queries.
Programming languages ?
• Introduction to Prolog:
– Example: Let’s say we have knowledge base like this
woman(mia).
woman(jody).
woman(yolanda).
playsAirGuitar(jody).
party.
How can we use KB? By posing queries.
?- woman(mia).
Prolog will answer
yes
?- playsAirGuitar(mia).
Prolog will answer
no
Programming languages ?
• Introduction to Prolog:
– Example: Let’s say we have knowledge base like this
happy(yolanda).
listens2Music(mia).
listens2Music(yolanda):- happy(yolanda).
playsAirGuitar(mia):- listens2Music(mia).
playsAirGuitar(yolanda):- listens2Music(yolanda).
Installing and Launching
• Installation from binary packages:
sudo apt-get install ros-groovy-knowrob
• Installation from source (basic installation):
rosws merge https://raw.github.com/knowrob/knowrob/
master/rosinstall/knowrob-base.rosinstall
rosws update rosdep install knowrob
rosmake knowrob
Installing and Launching
• Launching a package:
rosrun rosprolog rosprolog <pkgname>
• Example:
rosrun rosprolog rosprolog mod_vis
KNOWROB package list
•
•
Starting and accessing Prolog
– rosprolog: Start an interactive Prolog shell
– json_prolog: Start ROS service that offers a query interface
Base system
– ias_knowledge_base: Core ontology and basic reasoning modules
– knowrob_common: Common utilities for handling OWL (import/export, OWL
reasoner interface, convenience query predicates), units of measure, and other
generic functionality
– knowrob_objects: Object-related functionality, supporting spatio-temporal
reasoning, generation of the internal object representation, representation and
conversion of coordinates
– knowrob_actions: Read action properties, project effects of actions and
processes, reason about transformations of objects induced by actions
– ias_semantic_map: Semantic environment map representation in OWL
– ias_prolog_addons: Prolog extensions such as the interface to the Weka and
Mallet classification libraries, Jython interface, computation of semantic
similarity measures between concepts
– semweb: Semantic Web library of SWI prolog with extensions, e.g. computable
properties
– thea: OWL parser library
KNOWROB package list
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Reasoning packages
– comp_spatial: Compute qualitative spatial relations from object poses
– comp_temporal: Compute temporal relations, e.g. Allen's interval algebra
– mod_vis: Visualization of the content of the knowledge base
– knowrob_cad_models: Load Collada models for visualization and reasoning
– mod_srdl: Description of robot components and capabilities and methods for
matching these descriptions against requirements of actions
– mod_probcog: Interface to the ProbCog statistical relational learning library
Interfaces to perception components
– prolog_perception: (deprecated) Integration of perception using a Prolog
module
– comp_cop: Interface to the CoP perception system
– knowrob_objects: Generic library for creating the KnowRob-internal object
representation
Probabilistic reasoning
– srldb: Package with the ProbCog library
Related packages:
– comp_ehow: Import of natural-language web instructions, removed
from KnowRob to reduce dependencies
Where to download the source code?
• KNOWROB is an open source platform. We can download
KNOWROB core packages and general issue tracker for it at
following address:
https://github.com/knowrob/knowrob