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  • 1.
    Broxvall, Mathias
    et al.
    Örebro University, School of Science and Technology.
    Daoutis, Marios
    Örebro University, School of Science and Technology.
    Developing Medical Image Processing Algorithms for GPU assisted parallel computation2013In: Computer Vision in Medical Imaging / [ed] C H Chen, World Scientific, 2013, p. 245-270Chapter in book (Refereed)
    Abstract [en]

    GPU’s have recently emerged as a significantly more powerful computing plat-form, capable of several orders of magnitude faster computations compared toCPU based approaches. However, they require significant changes in the algorithmic design compared to traditional programming paradigms. In this chapter we specifically introduce the reader to an overview of GPGPU development tools and the potential algorithmic pitfalls and bottlenecks when developing medical imaging algorithms for the GPU. We present a few general methodologies and building blocks for implementing fast image processing on GPUs. More specifically they include: methods for performing fast image convolutions and filtering;line detection, and bandwidth and memory considerations when processing volumetric datasets. Finally we conclude with a discourse on numerical precision as well as on mixing single floating-point versus double floating-point code.

  • 2.
    Daoutis, Marios
    Örebro University, School of Science and Technology.
    Knowledge based perceptual anchoring: grounding percepts to concepts in cognitive robots2013In: Künstliche Intelligenz, ISSN 0933-1875, E-ISSN 1610-1987, p. 1-4Article in journal (Refereed)
    Abstract [en]

    Perceptual anchoring is the process of creating and maintaining a connection between the sensor data corresponding to a physical object and its symbolic description. It is a subset of the symbol grounding problem, introduced by Harnad (Phys. D, Nonlinear Phenom. 42(1–3):335–346, 1990) and investigated over the past years in several disciplines including robotics. This PhD dissertation focuses on a method for grounding sensor data of physical objects to the corresponding semantic descriptions, in the context of cognitive robots where the challenge is to establish 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 better linguistic interaction with the use of the corresponding concepts. The proposed method addresses the need to represent and process both perceptual and semantic knowledge, often expressed in different abstraction levels, while originating from different modalities. We then focus on the integration of anchoring with a large scale knowledge base system and with perceptual routines. This integration is applied in a number of studies, where in the context of a smart home, several evaluations spanning from spatial and commonsense reasoning to linguistic interaction and concept acquisition.

  • 3.
    Daoutis, Marios
    Örebro University, School of Science and Technology.
    Knowledge based perceptual anchoring: grounding percepts to concepts in cognitive robots2013Doctoral 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.

    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
  • 4.
    Daoutis, Marios
    et al.
    Örebro University, School of Science and Technology.
    Coradeschi, Silvia
    Örebro University, School of Science and Technology.
    Loutfi, Amy
    Örebro University, School of Science and Technology.
    Cooperative knowledge based perceptual anchoring2012In: International journal on artificial intelligence tools, ISSN 0218-2130, Vol. 21, no 3, article id 1250012Article in journal (Refereed)
    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.

  • 5.
    Daoutis, Marios
    et al.
    Örebro University, School of Science and Technology.
    Coradeschi, Silvia
    Örebro University, School of Science and Technology.
    Loutfi, Amy
    Örebro University, School of Science and Technology.
    Grounding commonsense knowledge in intelligent systems2009In: Journal of Ambient Intelligence and Smart Environments, ISSN 1876-1364, E-ISSN 1876-1372, Vol. 1, no 4, p. 311-321Article in journal (Refereed)
    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.

  • 6.
    Daoutis, Marios
    et al.
    Örebro University, School of Science and Technology.
    Coradeschi, Silvia
    Örebro University, School of Science and Technology.
    Loutfi, Amy
    Örebro University, School of Science and Technology.
    Integrating common sense in physically embedded intelligent systems2009In: Intelligent environments 2009 / [ed] V. Callaghan, A. Kameas, A. Reyes, D. Royo, M. Weber, Amsterdam: IOS Press , 2009, p. 212-219Conference paper (Refereed)
    Abstract [en]

    In this paper we describe an implemented framework that integrates knowledge representation and reasoning in a symbiotic system. In such systems a number of heterogeneous sensors pervasively embedded in the environment, mobile robots and humans co-exist and communicate. In this work, the integration is mediated through perceptual anchoring, which creates and maintains the correspondences between the symbol system and the perceptual data that refer to the same physical object. The overall framework is evaluated using ResearchCyc as the knowledge representation and reasoning system, within the context of a physical testbed, which consists of a small apartment-like home.

  • 7.
    Daoutis, Marios
    et al.
    Örebro University, School of Science and Technology.
    Coradeschi, Silvia
    Örebro University, School of Science and Technology.
    Loutfi, Amy
    Örebro University, School of Science and Technology.
    Knowledge representation for anchoring symbolic concepts to perceptual data2012In: Bridges between the Methodological and Practical Work of the Robotics and Cognitive Systems Communities - From Sensors to Concepts / [ed] Springet Publishing, Springer Publishing Company, 2012Chapter in book (Refereed)
  • 8.
    Daoutis, Marios
    et al.
    Örebro University, School of Science and Technology.
    Coradeschi, Silvia
    Örebro University, School of Science and Technology.
    Loutfi, Amy
    Örebro University, School of Science and Technology.
    Towards concept anchoring for cognitive robots2012In: Intelligent Service Robotics, ISSN 1861-2784, Vol. 5, no 4, p. 213-228Article in journal (Refereed)
    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.

  • 9.
    Daoutis, Marios
    et al.
    Örebro University, School of Science and Technology.
    Mavridis, Nikolaos
    Towards a Model for Grounding Semantic Composition2014Conference paper (Refereed)
  • 10.
    Dofs Sundin, Monica
    Högskolan i Gävle, Ämnesavdelningen för medier, kommunikation och film.
    Elfströms reklamfilmer i Gävle: hygien och genus2006In: Medierade offentligheter och identitet / [ed] Hammar, Björn, Gävle: Institutionen för humaniora och samhällsvetenskap, Högskolan i Gävle , 2006, p. 171-198Chapter in book (Other academic)
  • 11.
    Loutfi, Amy
    et al.
    Örebro University, School of Science and Technology.
    Coradeschi, Silvia
    Örebro University, School of Science and Technology.
    Daoutis, Marios
    Örebro University, School of Science and Technology.
    Melchert, Jonas
    Örebro University, School of Science and Technology.
    Using Knowledge Representation for Perceptual Anchoring in a Robotic System2008In: International Journal on Artificial Intelligence Tools, ISSN 0218-2130, Vol. 17, no 5, p. 925-944Article in journal (Refereed)
    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.

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