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Action graphs for performing goal recognition design on human-inhabited environments

Helen Harman (UGent) and Pieter Simoens (UGent)
(2019) SENSORS. 19(12). p.1-38
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Abstract
Goal recognition is an important component of many context-aware and smart environment services; however, a person's goal often cannot be determined until their plan nears completion. Therefore, by modifying the state of the environment, our work aims to reduce the number of observations required to recognise a human's goal. These modifications result in either: Actions in the available plans being replaced with more distinctive actions; or removing the possibility of performing some actions, so humans are forced to take an alternative (more distinctive) plan. In our solution, a symbolic representation of actions and the world state is transformed into an Action Graph, which is then traversed to discover the non-distinctive plan prefixes. These prefixes are processed to determine which actions should be replaced or removed. For action replacement, we developed an exhaustive approach and an approach that shrinks the plans then reduces the non-distinctive plan prefixes, namely Shrink-Reduce. Exhaustive is guaranteed to find the minimal distinctiveness but is more computationally expensive than Shrink-Reduce. These approaches are compared using a test domain with varying amounts of goals, variables and values, and a realistic kitchen domain. Our action removal method is shown to increase the distinctiveness of various grid-based navigation problems, with a width/height ranging from 4 to 16 and between 2 and 14 randomly selected goals, by an average of 3.27 actions in an average time of 4.69 s, whereas a state-of-the-art approach often breaches a 10 min time limit.
Keywords
goal recognition design, symbolic AI, intention recognition, human, aware, graph algorithms, modelling actions, redesigning environments, context-awareness, increasing distinctiveness

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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 Performing Goal Recognition Design on Human-inhabited Environments.” SENSORS 19.12 (2019): 1–38. Print.
APA
Harman, H., & Simoens, P. (2019). Action graphs for performing goal recognition design on human-inhabited environments. SENSORS, 19(12), 1–38.
Chicago author-date
Harman, Helen, and Pieter Simoens. 2019. “Action Graphs for Performing Goal Recognition Design on Human-inhabited Environments.” Sensors 19 (12): 1–38.
Chicago author-date (all authors)
Harman, Helen, and Pieter Simoens. 2019. “Action Graphs for Performing Goal Recognition Design on Human-inhabited Environments.” Sensors 19 (12): 1–38.
Vancouver
1.
Harman H, Simoens P. Action graphs for performing goal recognition design on human-inhabited environments. SENSORS. Basel: Mdpi; 2019;19(12):1–38.
IEEE
[1]
H. Harman and P. Simoens, “Action graphs for performing goal recognition design on human-inhabited environments,” SENSORS, vol. 19, no. 12, pp. 1–38, 2019.
@article{8623412,
  abstract     = {Goal recognition is an important component of many context-aware and smart environment services; however, a person's goal often cannot be determined until their plan nears completion. Therefore, by modifying the state of the environment, our work aims to reduce the number of observations required to recognise a human's goal. These modifications result in either: Actions in the available plans being replaced with more distinctive actions; or removing the possibility of performing some actions, so humans are forced to take an alternative (more distinctive) plan. In our solution, a symbolic representation of actions and the world state is transformed into an Action Graph, which is then traversed to discover the non-distinctive plan prefixes. These prefixes are processed to determine which actions should be replaced or removed. For action replacement, we developed an exhaustive approach and an approach that shrinks the plans then reduces the non-distinctive plan prefixes, namely Shrink-Reduce. Exhaustive is guaranteed to find the minimal distinctiveness but is more computationally expensive than Shrink-Reduce. These approaches are compared using a test domain with varying amounts of goals, variables and values, and a realistic kitchen domain. Our action removal method is shown to increase the distinctiveness of various grid-based navigation problems, with a width/height ranging from 4 to 16 and between 2 and 14 randomly selected goals, by an average of 3.27 actions in an average time of 4.69 s, whereas a state-of-the-art approach often breaches a 10 min time limit.},
  articleno    = {2741},
  author       = {Harman, Helen and Simoens, Pieter},
  issn         = {1424-8220},
  journal      = {SENSORS},
  keywords     = {goal recognition design,symbolic AI,intention recognition,human,aware,graph algorithms,modelling actions,redesigning environments,context-awareness,increasing distinctiveness},
  language     = {eng},
  number       = {12},
  pages        = {2741:1--2741:38},
  publisher    = {Mdpi},
  title        = {Action graphs for performing goal recognition design on human-inhabited environments},
  url          = {http://dx.doi.org/10.3390/s19122741},
  volume       = {19},
  year         = {2019},
}

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