Knowledge acquisition through introspection in Human-Robot Cooperation

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4 Citazioni (Scopus)

Abstract

When cooperating with a team including humans, robots have to understand and update semantic information concerning the state of the environment. The run-time evaluation and acquisition of new concepts fall in the critical mass learning. It is a cognitive skill that enables the robot to show environmental awareness to complete its tasks successfully. A kind of self-consciousness emerges: the robot activates the introspective mental processes inferring if it owns a domain concept or not, and correctly blends the conceptual meaning of new entities. Many works attempt to simulate human brain functions leading to neural network implementation of consciousness; regrettably, some of these produce accurate model that however do not provide means for creating virtual agents able to interact with a human in a teamwork in a human-like fashion, hence including aspects such as self-conscious abilities, trust, emotions and motivations. We propose a method that, based on a cognitive architecture for human-robot teaming interaction, endows a robot with the ability to model its knowledge about the environment it is interacting with and to acquire new knowledge when it occurs.
Lingua originaleEnglish
pagine (da-a)1-7
Numero di pagine7
RivistaBiologically Inspired Cognitive Architectures
Volume25
Stato di pubblicazionePublished - 2018

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Knowledge acquisition
Robots
Aptitude
Consciousness
Human robot interaction
Mental Processes
Brain
Semantics
Motivation
Neural networks
Emotions
Learning

All Science Journal Classification (ASJC) codes

  • Experimental and Cognitive Psychology
  • Cognitive Neuroscience
  • Artificial Intelligence

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title = "Knowledge acquisition through introspection in Human-Robot Cooperation",
abstract = "When cooperating with a team including humans, robots have to understand and update semantic information concerning the state of the environment. The run-time evaluation and acquisition of new concepts fall in the critical mass learning. It is a cognitive skill that enables the robot to show environmental awareness to complete its tasks successfully. A kind of self-consciousness emerges: the robot activates the introspective mental processes inferring if it owns a domain concept or not, and correctly blends the conceptual meaning of new entities. Many works attempt to simulate human brain functions leading to neural network implementation of consciousness; regrettably, some of these produce accurate model that however do not provide means for creating virtual agents able to interact with a human in a teamwork in a human-like fashion, hence including aspects such as self-conscious abilities, trust, emotions and motivations. We propose a method that, based on a cognitive architecture for human-robot teaming interaction, endows a robot with the ability to model its knowledge about the environment it is interacting with and to acquire new knowledge when it occurs.",
keywords = "Artificial Intelligence, Cognitive Neuroscience, Cognitive agent, Cognitive architecture, Experimental and Cognitive Psychology, Introspection, Knowledge acquisition, Ontology",
author = "Francesco Lanza and Antonio Chella and Valeria Seidita and Arianna Pipitone and Antonio Chella and Valeria Seidita",
year = "2018",
language = "English",
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T1 - Knowledge acquisition through introspection in Human-Robot Cooperation

AU - Lanza, Francesco

AU - Chella, Antonio

AU - Seidita, Valeria

AU - Pipitone, Arianna

AU - Chella, Antonio

AU - Seidita, Valeria

PY - 2018

Y1 - 2018

N2 - When cooperating with a team including humans, robots have to understand and update semantic information concerning the state of the environment. The run-time evaluation and acquisition of new concepts fall in the critical mass learning. It is a cognitive skill that enables the robot to show environmental awareness to complete its tasks successfully. A kind of self-consciousness emerges: the robot activates the introspective mental processes inferring if it owns a domain concept or not, and correctly blends the conceptual meaning of new entities. Many works attempt to simulate human brain functions leading to neural network implementation of consciousness; regrettably, some of these produce accurate model that however do not provide means for creating virtual agents able to interact with a human in a teamwork in a human-like fashion, hence including aspects such as self-conscious abilities, trust, emotions and motivations. We propose a method that, based on a cognitive architecture for human-robot teaming interaction, endows a robot with the ability to model its knowledge about the environment it is interacting with and to acquire new knowledge when it occurs.

AB - When cooperating with a team including humans, robots have to understand and update semantic information concerning the state of the environment. The run-time evaluation and acquisition of new concepts fall in the critical mass learning. It is a cognitive skill that enables the robot to show environmental awareness to complete its tasks successfully. A kind of self-consciousness emerges: the robot activates the introspective mental processes inferring if it owns a domain concept or not, and correctly blends the conceptual meaning of new entities. Many works attempt to simulate human brain functions leading to neural network implementation of consciousness; regrettably, some of these produce accurate model that however do not provide means for creating virtual agents able to interact with a human in a teamwork in a human-like fashion, hence including aspects such as self-conscious abilities, trust, emotions and motivations. We propose a method that, based on a cognitive architecture for human-robot teaming interaction, endows a robot with the ability to model its knowledge about the environment it is interacting with and to acquire new knowledge when it occurs.

KW - Artificial Intelligence

KW - Cognitive Neuroscience

KW - Cognitive agent

KW - Cognitive architecture

KW - Experimental and Cognitive Psychology

KW - Introspection

KW - Knowledge acquisition

KW - Ontology

UR - http://hdl.handle.net/10447/339802

UR - http://www.journals.elsevier.com/biologically-inspired-cognitive-architectures/

M3 - Article

VL - 25

SP - 1

EP - 7

JO - Biologically Inspired Cognitive Architectures

JF - Biologically Inspired Cognitive Architectures

SN - 2212-683X

ER -