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Article

Deliberative and Conceptual Inference in Service Robots

1
Department of Computer Science, Instituto de Investigaciones en Matemáticas Aplicadas y en Sistemas, Universidad Nacional Autónoma de México, Coyoacán 04510, Mexico
2
Facultad de Estudios Superiores Aragón, Universidad Nacional Autónoma de México, Av Hacienda de Rancho Seco S/N, Impulsora Popular Avícola 57130, Mexico
*
Author to whom correspondence should be addressed.
Academic Editor: Manuel Armada
Appl. Sci. 2021, 11(4), 1523; https://doi.org/10.3390/app11041523
Received: 3 December 2020 / Revised: 25 January 2021 / Accepted: 1 February 2021 / Published: 8 February 2021
(This article belongs to the Collection Advances in Automation and Robotics)
Service robots need to reason to support people in daily life situations. Reasoning is an expensive resource that should be used on demand whenever the expectations of the robot do not match the situation of the world and the execution of the task is broken down; in such scenarios, the robot must perform the common sense daily life inference cycle consisting on diagnosing what happened, deciding what to do about it, and inducing and executing a plan, recurring in such behavior until the service task can be resumed. Here, we examine two strategies to implement this cycle: (1) a pipe-line strategy involving abduction, decision-making, and planning, which we call deliberative inference and (2) the use of the knowledge and preferences stored in the robot’s knowledge-base, which we call conceptual inference. The former involves an explicit definition of a problem-space that is explored through heuristic search, and the latter is based on conceptual knowledge, including the human user preferences, and its representation requires a non-monotonic knowledge-based system. We compare the strengths and limitations of both approaches. We also describe a service robot conceptual model and architecture capable of supporting the daily life inference cycle during the execution of a robotics service task. The model is centered in the declarative specification and interpretation of robot’s communication and task structure. We also show the implementation of this framework in the fully autonomous robot Golem-III. The framework is illustrated with two demonstration scenarios. View Full-Text
Keywords: symbolic inference in service robots; non-monotonic reasoning; diagnosis, decision-making, and planning in service robots; reasoning with preferences in service robots; conceptual model of service robots; declarative specification and interpretation of task and communication structure; SitLog symbolic inference in service robots; non-monotonic reasoning; diagnosis, decision-making, and planning in service robots; reasoning with preferences in service robots; conceptual model of service robots; declarative specification and interpretation of task and communication structure; SitLog
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MDPI and ACS Style

Pineda, L.A.; Hernández, N.; Rodríguez, A.; Cruz, R.; Fuentes, G. Deliberative and Conceptual Inference in Service Robots. Appl. Sci. 2021, 11, 1523. https://doi.org/10.3390/app11041523

AMA Style

Pineda LA, Hernández N, Rodríguez A, Cruz R, Fuentes G. Deliberative and Conceptual Inference in Service Robots. Applied Sciences. 2021; 11(4):1523. https://doi.org/10.3390/app11041523

Chicago/Turabian Style

Pineda, Luis A.; Hernández, Noé; Rodríguez, Arturo; Cruz, Ricardo; Fuentes, Gibrán. 2021. "Deliberative and Conceptual Inference in Service Robots" Appl. Sci. 11, no. 4: 1523. https://doi.org/10.3390/app11041523

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