Cyclic Action Graphs for goal recognition problems with inaccurately initialised fluents Article Swipe
Helen Harman
,
Pieter Simoens
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.1007/s10115-023-01976-6
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.1007/s10115-023-01976-6
Goal recognisers attempt to infer an agent’s intentions from a sequence of observed actions. This is an important component of intelligent systems that aim to assist or thwart actors; however, there are many challenges to overcome. For example, the initial state of the environment could be partially unknown, and agents can act suboptimally and observations could be missing. Approaches that adapt classical planning techniques to goal recognition have previously been proposed, but, generally, they assume the initial world state is accurately defined. In this paper, a state is inaccurate if any fluent’s value is unknown or incorrect. Our aim is to develop a goal recognition approach that is as accurate as the current state-of-the-art algorithms and whose accuracy does not deteriorate when the initial state is inaccurately defined. To cope with this complication, we propose solving goal recognition problems by means of an Action Graph. An Action Graph models the dependencies, i.e. order constraints, between all actions rather than just actions within a plan. Leaf nodes correspond to actions and are connected to their dependencies via operator nodes. After generating an Action Graph, the graph’s nodes are labelled with their distance from each hypothesis goal. This distance is based on the number and type of nodes traversed to reach the node in question from an action node that results in the goal state being reached. For each observation, the goal probabilities are then updated based on either the distance the observed action’s node is from each goal or the change in distance. Our experimental results, for 15 different domains, demonstrate that our approach is robust to inaccuracies within the defined initial state.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1007/s10115-023-01976-6
- https://link.springer.com/content/pdf/10.1007/s10115-023-01976-6.pdf
- OA Status
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- References
- 55
- Related Works
- 10
- OpenAlex ID
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All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4387309476Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1007/s10115-023-01976-6Digital Object Identifier
- Title
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Cyclic Action Graphs for goal recognition problems with inaccurately initialised fluentsWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2023Year of publication
- Publication date
-
2023-10-03Full publication date if available
- Authors
-
Helen Harman, Pieter SimoensList of authors in order
- Landing page
-
https://doi.org/10.1007/s10115-023-01976-6Publisher landing page
- PDF URL
-
https://link.springer.com/content/pdf/10.1007/s10115-023-01976-6.pdfDirect link to full text PDF
- Open access
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YesWhether a free full text is available
- OA status
-
hybridOpen access status per OpenAlex
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https://link.springer.com/content/pdf/10.1007/s10115-023-01976-6.pdfDirect OA link when available
- Concepts
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Node (physics), Graph, Computer science, Action (physics), State (computer science), Artificial intelligence, Theoretical computer science, Algorithm, Engineering, Quantum mechanics, Structural engineering, PhysicsTop concepts (fields/topics) attached by OpenAlex
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0Total citation count in OpenAlex
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55Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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| cited_by_percentile_year | |
| countries_distinct_count | 1 |
| institutions_distinct_count | 2 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/11 |
| sustainable_development_goals[0].score | 0.4699999988079071 |
| sustainable_development_goals[0].display_name | Sustainable cities and communities |
| citation_normalized_percentile.value | 0.14748412 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |