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Knowledge based perceptual anchoring: grounding percepts to concepts in cognitive robots
Örebro University, School of Science and Technology.
2013 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

A successful articial 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 sucient abstraction and enough richness to deal with the complex nature of the concepts' meanings. On the other hand, neither plain symbol manipulation appears capable of attributing the desired intrinsic meaning.

We approach this integration in the context of cognitive robots which operate in the physical world. Specically we investigate the challenge of establishing the connection between percepts and concepts referring to objects, their relations and properties.We examine how knowledge representation can be used together with 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 dierent abstraction levels and may originate from dierent modalities.

The solution proposed in this thesis concerns the specication, design and implementation ofa hybrid cognitive computational model, which extends a classical anchoring framework, in order to address the creation and maintenance 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 computer vision and mobile robot localisation; (b) symbol grounding, using topdown and bottom-up information acquisition processes as well as multi-modal representations; (c) knowledge representation and reasoning techniques in order to establish a common language and semantics regarding physical objects, 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. Specically, 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 dierent levels of representation while integrating dierent 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.

Place, publisher, year, edition, pages
Örebro: Örebro universitet , 2013. , p. 99
Series
Örebro Studies in Technology, ISSN 1650-8580 ; 55
Keywords [en]
anchoring, knowledge representation, cognitive perception, symbol grounding, common-sense information
National Category
Computer Sciences
Research subject
Computer and Systems Science
Identifiers
URN: urn:nbn:se:oru:diva-26510ISBN: 978-91-7668-912-7 (print)OAI: oai:DiVA.org:oru-26510DiVA, id: diva2:571925
Public defence
2013-01-17, 10:36 (English)
Opponent
Supervisors
Available from: 2012-11-26 Created: 2012-11-26 Last updated: 2018-01-12Bibliographically approved
List of papers
1. Using Knowledge Representation for Perceptual Anchoring in a Robotic System
Open this publication in new window or tab >>Using Knowledge Representation for Perceptual Anchoring in a Robotic System
2008 (English)In: International Journal on Artificial Intelligence Tools, ISSN 0218-2130, Vol. 17, no 5, p. 925-944Article in journal (Refereed) Published
Abstract [en]

In this work we introduce symbolic knowledge representation and reasoning capabilities to enrich perceptual anchoring. The idea that encompasses perceptual anchoring is the creation and maintenance of a connection between the symbolic and perceptual description that refer to the same object in the environment. In this work we further extend the symbolic layer by combining a knowledge representation and reasoning (KRR) system with the anchoring module to exploit a knowledge inference mechanisms. We implemented a prototype of this novel approach to explore through initial experimentation the advantages of integrating a symbolic knowledge system to the anchoring framework in the context of an intelligent home. Our results show that using the KRR we are better able to cope with ambiguities in the anchoring module through exploitation of human robot interaction.

National Category
Engineering and Technology Computer and Information Sciences
Research subject
Computer and Systems Science
Identifiers
urn:nbn:se:oru:diva-5175 (URN)
Available from: 2009-02-24 Created: 2009-01-29 Last updated: 2018-01-13Bibliographically approved
2. Grounding commonsense knowledge in intelligent systems
Open this publication in new window or tab >>Grounding commonsense knowledge in intelligent systems
2009 (English)In: Journal of Ambient Intelligence and Smart Environments, ISSN 1876-1364, E-ISSN 1876-1372, Vol. 1, no 4, p. 311-321Article in journal (Refereed) Published
Abstract [en]

Ambient environments which integrate a number of sensing devices and actuators intended for use by human users need to be able to express knowledge about objects, their functions and their properties to assist in the performance of everyday tasks. For this to occur perceptual data must be grounded to symbolic information that in its turn can be used in the communication with the human. For symbolic information to be meaningful it should be part of a rich knowledge base that includes an ontology of concepts and common sense. In this work we present an integration between ResearchCyc and an anchoring framework that mediates the connection between the perceptual information in an intelligent home environment and the reasoning system. Through simple dialogues we validate how objects placed in the home environment are grounded by a network of sensors and made available to a larger KB where reasoning is exploited. This first integration work is a step towards integrating the richness of a KRR system developed over many years in isolation, with a physically embedded intelligent system.

Place, publisher, year, edition, pages
Amsterdam: IOS Press, 2009
Keywords
Physical Symbol Grounding, Commonsense Knowledge Representation, Human Robot Interaction, Intelligent Home
National Category
Computer Sciences
Research subject
Computer Science; Information technology
Identifiers
urn:nbn:se:oru:diva-8485 (URN)10.3233/AIS-2009-0040 (DOI)000207842000002 ()2-s2.0-78651496919 (Scopus ID)
Available from: 2009-11-09 Created: 2009-11-09 Last updated: 2018-01-12Bibliographically approved
3. Cooperative knowledge based perceptual anchoring
Open this publication in new window or tab >>Cooperative knowledge based perceptual anchoring
2012 (English)In: International journal on artificial intelligence tools, ISSN 0218-2130, Vol. 21, no 3, article id 1250012Article in journal (Refereed) Published
Abstract [en]

In settings where heterogenous robotic systems interact with humans, information from the environment must be systematically captured, organized and maintained in time. In this work, we propose a model for connecting perceptual information to semantic information in a multi-agent setting. In particular, we present semantic cooperative perceptual anchoring, that captures collectively acquired perceptual information and connects it to semantically expressed commonsense knowledge. We describe how we implemented the proposed model in a smart environment, using different modern perceptual and knowledge representation techniques. We present the results of the systemand investigate different scenarios in which we use the common sense together with perceptual knowledge, for communication, reasoning and exchange of information.

Place, publisher, year, edition, pages
World Scientific, 2012
Keywords
Cognitive robotics; physical symbol grounding; commonsense information; multi-agent perception; object recognition
National Category
Computer Sciences
Research subject
Computer and Systems Science
Identifiers
urn:nbn:se:oru:diva-24226 (URN)10.1142/S0218213012500121 (DOI)000305795900008 ()2-s2.0-84863086324 (Scopus ID)
Funder
Swedish Research Council
Available from: 2012-08-06 Created: 2012-08-05 Last updated: 2018-01-12Bibliographically approved
4. Towards concept anchoring for cognitive robots
Open this publication in new window or tab >>Towards concept anchoring for cognitive robots
2012 (English)In: Intelligent Service Robotics, ISSN 1861-2784, Vol. 5, no 4, p. 213-228Article in journal (Refereed) Published
Abstract [en]

We present a model for anchoring categorical conceptual information which originates from physical perception and the web. The model is an extension of the anchoring framework which is used to create and maintain over time semantically grounded sensor information. Using the augmented anchoring framework that employs complex symbolic knowledge from a commonsense knowledge base, we attempt to ground and integrate symbolic and perceptual data that are available on the web. We introduce conceptual anchors which are representations of general, concrete conceptual terms. We show in an example scenario how conceptual anchors can be coherently integrated with perceptual anchors and commonsense information for the acquisition of novel concepts.

Place, publisher, year, edition, pages
Springer Berlin/Heidelberg, 2012
Keywords
Anchoring; Categorical perception; Near sets; Knowledge representation; Commonsense information
National Category
Robotics Computer Vision and Robotics (Autonomous Systems) Computer Sciences
Research subject
Computer Science
Identifiers
urn:nbn:se:oru:diva-26831 (URN)10.1007/s11370-012-0117-z (DOI)000208947900002 ()2-s2.0-84867580722 (Scopus ID)
Funder
Swedish Research Council
Available from: 2013-01-10 Created: 2013-01-10 Last updated: 2018-01-11Bibliographically approved

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