48,478 research outputs found
Symbol Emergence in Robotics: A Survey
Humans can learn the use of language through physical interaction with their
environment and semiotic communication with other people. It is very important
to obtain a computational understanding of how humans can form a symbol system
and obtain semiotic skills through their autonomous mental development.
Recently, many studies have been conducted on the construction of robotic
systems and machine-learning methods that can learn the use of language through
embodied multimodal interaction with their environment and other systems.
Understanding human social interactions and developing a robot that can
smoothly communicate with human users in the long term, requires an
understanding of the dynamics of symbol systems and is crucially important. The
embodied cognition and social interaction of participants gradually change a
symbol system in a constructive manner. In this paper, we introduce a field of
research called symbol emergence in robotics (SER). SER is a constructive
approach towards an emergent symbol system. The emergent symbol system is
socially self-organized through both semiotic communications and physical
interactions with autonomous cognitive developmental agents, i.e., humans and
developmental robots. Specifically, we describe some state-of-art research
topics concerning SER, e.g., multimodal categorization, word discovery, and a
double articulation analysis, that enable a robot to obtain words and their
embodied meanings from raw sensory--motor information, including visual
information, haptic information, auditory information, and acoustic speech
signals, in a totally unsupervised manner. Finally, we suggest future
directions of research in SER.Comment: submitted to Advanced Robotic
Artificial Intelligence and Statistics
Artificial intelligence (AI) is intrinsically data-driven. It calls for the
application of statistical concepts through human-machine collaboration during
generation of data, development of algorithms, and evaluation of results. This
paper discusses how such human-machine collaboration can be approached through
the statistical concepts of population, question of interest,
representativeness of training data, and scrutiny of results (PQRS). The PQRS
workflow provides a conceptual framework for integrating statistical ideas with
human input into AI products and research. These ideas include experimental
design principles of randomization and local control as well as the principle
of stability to gain reproducibility and interpretability of algorithms and
data results. We discuss the use of these principles in the contexts of
self-driving cars, automated medical diagnoses, and examples from the authors'
collaborative research
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