Knowledge acquisition through introspection in Human-Robot Cooperation

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.

Here is the link to “Knowledge acquisition through introspection in Human-Robot Cooperation”: https://www.sciencedirect.com/science/article/pii/S2212683X18300847

  • [DOI] A. Chella, F. Lanza, A. Pipitone, and V. Seidita, “Knowledge acquisition through introspection in Human-Robot Cooperation,” Biologically inspired cognitive architectures, vol. 25, p. 1–7, 2018.
    [Bibtex]
    @article{chella2018knowledge,
    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.},
    author = {Chella, Antonio and Lanza, Francesco and Pipitone, Arianna and Seidita, Valeria},
    doi = {10.1016/j.bica.2018.07.016},
    issn = {2212683X},
    journal = {Biologically Inspired Cognitive Architectures},
    keywords = {Cognitive agent,Cognitive architecture,Introspection,Knowledge acquisition,Ontology},
    pages = {1--7},
    publisher = {Elsevier},
    title = {{Knowledge acquisition through introspection in Human-Robot Cooperation}},
    volume = {25},
    year = {2018}
    }