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All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year Sort by creator [A → Z]' creator [Z → A]' publishing year ↑ (asc)' publishing year ↓ (desc)' title [A → Z]' title [Z → A]' Simple Search Hits 1 – 13 of 13 1 A novel image-based approach for interactive characterization of rock fracture spacing in a tunnel face Chen, J; Chen, Y; Cohn, AG. - : Elsevier, 2022 BASE Show details 2 Categorisation, Typicality & Object-Specific Features in Spatial Referring Expressions Richard-Bollans, A; Gómez Álvarez, L; Cohn, AG. - : Association for Computational Linguistics, 2020 BASE Show details 3 Modelling the Polysemy of Spatial Prepositions in Referring Expressions Richard-Bollans, A; Gomez Alvarez, L; Cohn, AG. - : IJCAI Organization, 2020 BASE Show details 4 Investigating the Dimensions of Spatial Language Richard-Bollans, A; Gómez Álvarez, L; Bennett, B. - 2019 BASE Show details 5 The Role of Pragmatics in Solving the Winograd Schema Challenge Richard-Bollans, AL; Gomez Alvarez, L; Cohn, AG. - : CEUR Workshop Proceedings, 2018 BASE Show details 6 Learning of Object Properties, Spatial Relations, and Actions for Embodied Agents from Language and Vision Alomari, M; Duckworth, P; Hogg, DC. - : AAAI Press, 2017 BASE Show details 7 Natural Language Grounding and Grammar Induction for Robotic Manipulation Commands Alomari, M; Duckworth, P; Hawasly, M. - : The Association for Computational Linguistics, 2017 BASE Show details 8 Natural Language Acquisition and Grounding for Embodied Robotic Systems Duckworth, P; Al-Omari, M; Hogg, DC; Cohn, AG. - : Association for the Advancement of Artificial Intelligence, 2017 Abstract: We present a cognitively plausible novel framework capable of learning the grounding in visual semantics and the grammar of natural language commands given to a robot in a table top environment. The input to the system consists of video clips of a manually controlled robot arm, paired with natural language commands describing the action. No prior knowledge is assumed about the meaning of words, or the structure of the language, except that there are different classes of words (corresponding to observable actions, spatial relations, and objects and their observable properties). The learning process automatically clusters the continuous perceptual spaces into concepts corresponding to linguistic input. A novel relational graph representation is used to build connections between language and vision. As well as the grounding of language to perception, the system also induces a set of probabilistic grammar rules. The knowledge learned is used to parse new commands involving previously unseen objects. URL: https://eprints.whiterose.ac.uk/109107/ https://ojs.aaai.org/index.php/AAAI/article/view/11161 BASE Hide details 9 Grounding language in perception for scene conceptualization in autonomous robots Dubba, KSR; De Oliveira, MR; Lim, GH. - : AI Access Foundation, 2014 BASE Show details 10 Interactive semantic feedback for intuitive ontology authoring Denaux, R; Thakker, DA; Dimitrova, V. - : IOS Press, 2012 BASE Show details 11 From Video to RCC8: Exploiting a Distance Based Semantics to Stabilise the Interpretation of Mereotopological Relations Sridhar, M; Cohn, AG; Hogg, DC. - : Springer, 2011 BASE Show details 12 The automated evaluation of inferred word classifications Hughes, J; Atwell, E. - : John Wiley & Sons, 1994 BASE Show details 13 Online perceptual learning and natural language acquisition for autonomous robots Alomari, M; Li, F; Hogg, DC. - : Elsevier, 1479 BASE Show details

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