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Towards Affordance-Based Robot Control [electronic resource] : International Seminar, Dagstuhl Castle, Germany, June 5-9, 2006, Revised Papers / edited by Erich Rome, Joachim Hertzberg, Georg Dorffner.

Contributor(s): Rome, Erich [editor.] | Hertzberg, Joachim [editor.] | Dorffner, Georg [editor.] | SpringerLink (Online service).
Material type: materialTypeLabelBookSeries: Lecture Notes in Artificial Intelligence: 4760Publisher: Berlin, Heidelberg : Springer Berlin Heidelberg : Imprint: Springer, 2008Edition: 1st ed. 2008.Description: IX, 214 p. online resource.Content type: text Media type: computer Carrier type: online resourceISBN: 9783540779155.Subject(s): Artificial intelligence | Computer vision | Pattern recognition systems | Artificial Intelligence | Computer Vision | Automated Pattern RecognitionAdditional physical formats: Printed edition:: No title; Printed edition:: No titleDDC classification: 006.3 Online resources: Click here to access online
Contents:
Interpersonal Maps: How to Map Affordances for Interaction Behaviour -- Does It Help a Robot Navigate to Call Navigability an Affordance? -- Learning Causality and Intentional Actions -- GrAM: Reasoning with Grounded Action Models by Combining Knowledge Representation and Data Mining -- Affordance-Based Human-Robot Interaction -- Reinforcement Learning of Predictive Features in Affordance Perception -- A Functional Model for Affordance-Based Agents -- Affordances in an Ecology of Physically Embedded Intelligent Systems -- Use of Affordances in Geospatial Ontologies -- Learning the Affordances of Tools Using a Behavior-Grounded Approach -- Function-Based Reasoning for Goal-Oriented Image Segmentation -- The MACS Project: An Approach to Affordance-Inspired Robot Control.
In: Springer Nature eBook
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Interpersonal Maps: How to Map Affordances for Interaction Behaviour -- Does It Help a Robot Navigate to Call Navigability an Affordance? -- Learning Causality and Intentional Actions -- GrAM: Reasoning with Grounded Action Models by Combining Knowledge Representation and Data Mining -- Affordance-Based Human-Robot Interaction -- Reinforcement Learning of Predictive Features in Affordance Perception -- A Functional Model for Affordance-Based Agents -- Affordances in an Ecology of Physically Embedded Intelligent Systems -- Use of Affordances in Geospatial Ontologies -- Learning the Affordances of Tools Using a Behavior-Grounded Approach -- Function-Based Reasoning for Goal-Oriented Image Segmentation -- The MACS Project: An Approach to Affordance-Inspired Robot Control.

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