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: TextTextSeries: Lecture Notes in Computer Science ; 4760Publisher: Berlin, Heidelberg : Springer Berlin Heidelberg, 2008Description: IX, 214 p. online resourceContent type: text Media type: computer Carrier type: online resourceISBN: 9783540779155Subject(s): Computer science | Artificial intelligence | Computer vision | Optical pattern recognition | Computer Science | Artificial Intelligence (incl. Robotics) | Image Processing and Computer Vision | Pattern RecognitionAdditional physical formats: Printed edition:: No titleDDC classification: 006.3 LOC classification: Q334-342TJ210.2-211.495Online 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 eBooks
Item type: E-BOOKS
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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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