Knowledge Based Perceptual Anchoring: Grounding percepts to concepts in cognitive robots

120 Pages·2013·2.87 MB·English

Doctoral Dissertation
Knowledge Based Perceptual Anchoring: Grounding
percepts to concepts in cognitive robots
Marios Daoutis
Computer Science
ÖrebroStudiesinTechnology55
Örebro2013

Knowledge Based Perceptual Anchoring: Grounding
percepts to concepts in cognitive robots

Örebro Studies in Technology 55
Marios Daoutis
Knowledge Based Perceptual
Anchoring: Grounding percepts to
concepts in cognitive robots
This research has been supported by:
© Marios Daoutis, 2013
Title: KnowledgeBasedPerceptualAnchoring:Groundingperceptsto
conceptsincognitiverobots
Publisher:ÖrebroUniversity,2013
www.publications.oru.se
[email protected]
Printer:ÖrebroUniversity,Repro12/2012
TypesetwithLATEX.
ISSN1650-8580
ISBN978-91-7668-912-7
Statement of Original Authorship
This thesis is submitted to the School of Science and Technology, University of
O¨rebro,infulfilmentoftherequirementsforthedegreeofDoctorofPhilosophy
in Computer Science. I certify that I am the sole author of this thesis and the
work contained herein is entirely my own work, describes my own research and
has not been previously submitted to meet requirements for an award at this
or any other higher education institution. To the best of my knowledge and
belief, the thesis contains no ideas or material of others, previously published
or written by another person except where due reference is made. I acknowl-
edge that I have read and understood the University’s rules, requirements,
procedures and policy related to my higher degree research award and to the
present thesis, as well as I certify that I have complied with the policy of the
Good Research Practice guide [1] (can be found here) of the Swedish Research
Council (Vetenskapsr˚adet).
Marios Daoutis
O¨rebro, April 2013
[1] B.Gustafsson,G.Hermeren,andB.Petersson.Goodresearchpractice–whatisthat?
SwedishScienceResearchCouncil,2005.(Seep.g).
© 2013
All rights reserved

Abstract
A successful artificial cognitive agent needs to integrate its perception of the
environment with reasoning and actuation. A key aspect of this integration
is the perceptual-symbolic correspondence, which intends to give meaning to
the concepts the agent refers to – known as Anchoring. However, perceptual
representations alone (e.g., feature lists) cannot entirely provide sufficient
abstractionandenoughrichnesstodealwiththecomplexnatureoftheconcepts’
meanings. On the other hand, neither plain symbol manipulation appears
capable of attributing the desired intrinsic meaning.
Weapproachthisintegrationinthecontextofcognitiverobotswhichoperate
inthephysicalworld.Specificallyweinvestigatethechallengeofestablishingthe
connectionbetweenperceptsandconceptsreferringtoobjects,theirrelationsand
properties.Weexaminehowknowledgerepresentationcanbeusedtogetherwith
an anchoring framework, so as to complement the meaning of percepts while
supporting linguistic interaction. This implies that robots need to represent
both their perceptual and semantic knowledge, which is often expressed in
different abstraction levels and may originate from different modalities.
The solution proposed in this thesis concerns the specification, design and
implementation of a hybrid cognitive computational model, which extends a
classicalanchoringframework,inordertoaddressthecreationandmaintenance
of the perceptual-symbolic correspondences. The model is based on four main
aspects: (a) robust perception, by relying on state-of-the-art techniques from
computervisionandmobilerobotlocalisation;(b)symbolgrounding,usingtop-
down and bottom-up information acquisition processes as well as multi-modal
representations;(c)knowledgerepresentationandreasoningtechniquesinorder
toestablishacommonlanguageandsemanticsregardingphysicalobjects,their
properties and relations, that are to be used between heterogeneous robotic
agents and humans; and (d) commonsense information in order to enable
high-level reasoning as well as to enhance the semantic descriptions of objects.
The resulting system and the proposed integration has the potential to
strengthen and expand the knowledge of a cognitive robot. Specifically, by
providing more robust percepts it is possible to cope better with the ambiguity
and uncertainty of the perceptual data. In addition, the framework is able
to exploit mutual interaction between different levels of representation while
integrating different sources of information. By modelling and using semantic
& perceptual knowledge, the robot can: acquire, exchange and reason formally
about concepts, while prior knowledge can become a cognitive bias in the
acquisition of novel concepts.
Keywords: anchoring, knowledge representation, cognitive perception,
symbol grounding, common-sense information.