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Action graphs for proactive robot assistance in smart environments

Helen Harman (UGent) and Pieter Simoens (UGent)
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Abstract
Smart environments can already observe the actions of a human through pervasive sensors. Based on these observations, our work aims to predict the actions a human is likely to perform next. Predictions can enable a robot to proactively assist humans by autonomously executing an action on their behalf. In this paper, Action Graphs are introduced to model the order constraints between actions. Action Graphs are derived from a problem defined in Planning Domain Definition Language (PDDL). When an action is observed, the node values are updated and next actions predicted. Subsequently, a robot executes one of the predicted actions if it does not impact the flow of the human by obstructing or delaying them. Our Action Graph approach is applied to a kitchen domain.
Keywords
ACTIVITY RECOGNITION, PLAN RECOGNITION, Action prediction, proactive assistance, intention recognition, symbolic, AI, smart environment

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Citation

Please use this url to cite or link to this publication:

MLA
Harman, Helen, and Pieter Simoens. “Action Graphs for Proactive Robot Assistance in Smart Environments.” JOURNAL OF AMBIENT INTELLIGENCE AND SMART ENVIRONMENTS, vol. 12, no. 2, 2020, pp. 79–99.
APA
Harman, H., & Simoens, P. (2020). Action graphs for proactive robot assistance in smart environments. JOURNAL OF AMBIENT INTELLIGENCE AND SMART ENVIRONMENTS, 12(2), 79–99.
Chicago author-date
Harman, Helen, and Pieter Simoens. 2020. “Action Graphs for Proactive Robot Assistance in Smart Environments.” JOURNAL OF AMBIENT INTELLIGENCE AND SMART ENVIRONMENTS 12 (2): 79–99.
Chicago author-date (all authors)
Harman, Helen, and Pieter Simoens. 2020. “Action Graphs for Proactive Robot Assistance in Smart Environments.” JOURNAL OF AMBIENT INTELLIGENCE AND SMART ENVIRONMENTS 12 (2): 79–99.
Vancouver
1.
Harman H, Simoens P. Action graphs for proactive robot assistance in smart environments. JOURNAL OF AMBIENT INTELLIGENCE AND SMART ENVIRONMENTS. 2020;12(2):79–99.
IEEE
[1]
H. Harman and P. Simoens, “Action graphs for proactive robot assistance in smart environments,” JOURNAL OF AMBIENT INTELLIGENCE AND SMART ENVIRONMENTS, vol. 12, no. 2, pp. 79–99, 2020.
@article{8657339,
  abstract     = {Smart environments can already observe the actions of a human through pervasive sensors. Based on these observations, our work aims to predict the actions a human is likely to perform next. Predictions can enable a robot to proactively assist humans by autonomously executing an action on their behalf. In this paper, Action Graphs are introduced to model the order constraints between actions. Action Graphs are derived from a problem defined in Planning Domain Definition Language (PDDL). When an action is observed, the node values are updated and next actions predicted. Subsequently, a robot executes one of the predicted actions if it does not impact the flow of the human by obstructing or delaying them. Our Action Graph approach is applied to a kitchen domain.},
  author       = {Harman, Helen and Simoens, Pieter},
  issn         = {1876-1364},
  journal      = {JOURNAL OF AMBIENT INTELLIGENCE AND SMART ENVIRONMENTS},
  keywords     = {ACTIVITY RECOGNITION,PLAN RECOGNITION,Action prediction,proactive assistance,intention recognition,symbolic,AI,smart environment},
  language     = {eng},
  number       = {2},
  pages        = {79--99},
  title        = {Action graphs for proactive robot assistance in smart environments},
  url          = {http://dx.doi.org/10.3233/AIS-200556},
  volume       = {12},
  year         = {2020},
}

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