The obligations and common ground structure of practical dialogues

Pineda, Luis A.; Estrada, Varinia; Coria, Sergio R.; Allen, James F.

doi:10.4114/ia.v11i36.886

“…The dialogue models are designed considering the user's preferences and the whole task oriented conversation is modelled as the interpretation of one main protocol that embeds the goals of the task. The design of the dialogue models is loosely based on the notion of balanced transactions of the DIME-DAMSL annotation scheme [25]. In the first section of the dialogue from ( 1) to (10) the robot asks for the user's preferences, and the KB is updated accordingly.…”

Section: Conceptual Inferencementioning

confidence: 99%

Deliberative and Conceptual Inference in Service Robots

Pineda¹,

Hernández²,

Rodriguez³

et al. 2020

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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

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“…El modelo del diálogo se puede representar como una red de transiciones del robot de un estado del diálogo al otro dependiendo de la información verbal o visual recibida, el historial del diálogo y las intenciones del usuario. Los estados se representan como reglas, otra posibilidad sería construir un autómata finito [11].…”

Section: Modelo Del Diálogounclassified

“…La dificultad de manejo de diálogo se relaciona con la información multimodal que el robot tiene que "entender", tomando en cuenta las diferencias fundamentales entre los lenguajes de representación del espacio en la "mente" del robot y en la mente humana. También a eso se anuda el problema de comprensión del lenguaje natural a un nivel tan profundo que permita interpretar correctamente las intenciones del interlocutor [10,11,13].…”

Section: Introductionunclassified

Modelo computacional del diálogo basado en reglas aplicado a un robot guía móvil

Sidorov¹,

Kobozeva²,

Zimmerling³

et al. 2014

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Resumen-En este artículo se presenta la descripción formal detallada del módulo de control del diálogo para un robot móvil que funciona como guía (en un museo). El módulo incluye el modelo proposicional del diálogo, la especificación de los actos de habla y los bloques del habla, así como el inventario de los patrones de habla correspondientes a todos los actos de habla en el modelo. El modelo del diálogo se implementa como una red de estados y transiciones entre estados condicionados por reglas que estipulan los factores verbales y visuales. La arquitectura del módulo no depende de un idioma particular y puede ser adaptado a cualquier lenguaje natural. Palabras clave-Modelo de diálogo, reglas, robot guía móvil, actos de habla, bloques de habla.

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“…The design of the dialogue models is loosely based on the notion of balanced transactions of the DIME-DAMSL. annotation scheme [66].…”

mentioning

confidence: 99%

Deliberative and Conceptual Inference in Service Robots

Pineda

¹

,

Hernández

²

,

Rodríguez

³

et al. 2021

View full text Add to dashboard Cite

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.

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The obligations and common ground structure of practical dialogues

Cited by 5 publications

References 6 publications

Deliberative and Conceptual Inference in Service Robots

Deliberative and Conceptual Inference in Service Robots

Modelo computacional del diálogo basado en reglas aplicado a un robot guía móvil

Deliberative and Conceptual Inference in Service Robots

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