KR2020Proceedings of the 17th International Conference on Principles of Knowledge Representation and ReasoningProceedings of the 17th International Conference on Principles of Knowledge Representation and Reasoning

Rhodes, Greece. September 12-18, 2020.

Edited by
  1. Diego Calvanese
  2. Esra Erdem
  3. Michael Thielscher

ISSN: 2334-1033
ISBN: 978-0-9992411-7-2

Sponsored by
Published by

Copyright © 2020 International Joint Conferences on Artificial Intelligence Organization

Modeling Affordances and Functioning for Personalized Robotic Assistance

  1. Alessandro Umbrico(CNR - National Research Council of Italy)
  2. Gabriella Cortellessa(CNR - National Research Council of Italy)
  3. Andrea Orlandini(CNR - National Research Council of Italy)
  4. Amedeo Cesta(CNR - National Research Council of Italy)
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BibTeX
https://doi.org/10.24963/kr.2020/94

Keywords

  1. Ontologies, and ontological representations for robotics-General
  2. KR for robotic cognition-General
  3. KR for robot plan execution and monitoring-General
  4. KR for human-robot interaction and communication-General
  5. KR for autonomous robot architectures-General
  6. Grounding knowledge in sense-plan-act loop-General

Abstract

A key aspect of robotic assistants is their ability to contextualize their behavior according to different needs of assistive scenarios. This work presents an ontology-based

knowledge representation and reasoning approach supporting the synthesis of personalized behavior of robotic assistants. It introduces an ontological model of health state

and functioning of persons based on the International Classification of Functioning, Disability and Health. Moreover, it borrows the concepts of affordance and function from the literature of robotics and manufacturing and adapts them to robotic (physical and cognitive) assistance domain. Knowledge reasoning mechanisms are developed

on top of the resulting ontological model to reason about stimulation capabilities of a robot and health state of a person in order to identify action opportunities and achieve personalized assistance. Experimental tests assess the performance of the proposed approach and its capability of dealing with different profiles and stimuli.