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Intention Recognition With ProbLog

In many scenarios where robots or autonomous systems may be deployed, the capacity to infer and reason about the intentions of other agents can improve the performance or utility of the system. For example, a smart home or assisted living facility is better able to select assistive services to deplo...

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Detalles Bibliográficos
Autores principales: Smith, Gary B., Belle, Vaishak, Petrick, Ronald P. A.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9087927/
https://www.ncbi.nlm.nih.gov/pubmed/35558169
http://dx.doi.org/10.3389/frai.2022.806262
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author Smith, Gary B.
Belle, Vaishak
Petrick, Ronald P. A.
author_facet Smith, Gary B.
Belle, Vaishak
Petrick, Ronald P. A.
author_sort Smith, Gary B.
collection PubMed
description In many scenarios where robots or autonomous systems may be deployed, the capacity to infer and reason about the intentions of other agents can improve the performance or utility of the system. For example, a smart home or assisted living facility is better able to select assistive services to deploy if it understands the goals of the occupants in advance. In this article, we present a framework for reasoning about intentions using probabilistic logic programming. We employ ProbLog, a probabilistic extension to Prolog, to infer the most probable intention given observations of the actions of the agent and sensor readings of important aspects of the environment. We evaluated our model on a domain modeling a smart home. The model achieved 0.75 accuracy at full observability. The model was robust to reduced observability.
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spelling pubmed-90879272022-05-11 Intention Recognition With ProbLog Smith, Gary B. Belle, Vaishak Petrick, Ronald P. A. Front Artif Intell Artificial Intelligence In many scenarios where robots or autonomous systems may be deployed, the capacity to infer and reason about the intentions of other agents can improve the performance or utility of the system. For example, a smart home or assisted living facility is better able to select assistive services to deploy if it understands the goals of the occupants in advance. In this article, we present a framework for reasoning about intentions using probabilistic logic programming. We employ ProbLog, a probabilistic extension to Prolog, to infer the most probable intention given observations of the actions of the agent and sensor readings of important aspects of the environment. We evaluated our model on a domain modeling a smart home. The model achieved 0.75 accuracy at full observability. The model was robust to reduced observability. Frontiers Media S.A. 2022-04-26 /pmc/articles/PMC9087927/ /pubmed/35558169 http://dx.doi.org/10.3389/frai.2022.806262 Text en Copyright © 2022 Smith, Belle and Petrick. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Artificial Intelligence
Smith, Gary B.
Belle, Vaishak
Petrick, Ronald P. A.
Intention Recognition With ProbLog
title Intention Recognition With ProbLog
title_full Intention Recognition With ProbLog
title_fullStr Intention Recognition With ProbLog
title_full_unstemmed Intention Recognition With ProbLog
title_short Intention Recognition With ProbLog
title_sort intention recognition with problog
topic Artificial Intelligence
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9087927/
https://www.ncbi.nlm.nih.gov/pubmed/35558169
http://dx.doi.org/10.3389/frai.2022.806262
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