Artificial neural networks have gone through a recent rise in popularity, achieving state-of-
the-art results in various fields, including image classification, speech recognition, and …

This paper describes a framework for the development of an integrative cognitive system
based on probabilistic generative models (PGMs) called Neuro-SERKET. Neuro-SERKET is …

In this paper, we propose an online learning algorithm based on a Rao-Blackwellized
particle filter for spatial concept acquisition and mapping. We have proposed a …

Digital images have revolutionized work in numerous scientific fields such as healthcare,
astronomy, biology, agriculture as well as in every day life. One of the frequent tasks in …

In building artificial intelligence (AI) agents, referring to how brains function in real
environments can accelerate development by reducing the design space. In this study, we …

We present a computational model for a symbol emergence system that enables the
emergence of lexical knowledge with combinatoriality among agents through a Metropolis …

This paper describes how to achieve highly accurate unsupervised spatial lexical
acquisition from speech-recognition results including phoneme recognition errors. In most …

Interactive and tangible computing platforms have garnered increased interest in the pursuit
of embedding active learning pedagogies within curricula through educational technologies …

To enhance the safety and stability of autonomous vehicles, we present a deep learning
platooning-based video information-sharing Internet of Things framework in this study. The …

In this paper, we propose a hierarchical spatial concept formation method based on the
Bayesian generative model with multimodal information eg, vision, position and word …