155 citations · 765 across the 63 of their papers we have counts for
13 papers · 1 filter
Control as Probabilistic Inference as an Emergent Communication Mechanism in Multi-Agent Reinforcement Learning
Tomoaki Nakamura, Akira Taniguchi, Tadahiro Taniguchi
This paper proposes a generative probabilistic model integrating emergent communication and multi-agent reinforcement learning. The agents plan their actions by probabilistic infer…
Emergent Communication through Metropolis-Hastings Naming Game with Deep Generative Models
Tadahiro Taniguchi, Yuto Yoshida, Akira Taniguchi +1
Constructive studies on symbol emergence systems seek to investigate computational models that can better explain human language evolution, the creation of symbol systems, and the…
Multi-View Dreaming: Multi-View World Model with Contrastive Learning
Akira Kinose, Masashi Okada, Ryo Okumura +1
In this paper, we propose Multi-View Dreaming, a novel reinforcement learning agent for integrated recognition and control from multi-view observations by extending Dreaming. Most…
Unsupervised Multimodal Word Discovery based on Double Articulation Analysis with Co-occurrence cues
Akira Taniguchi, Hiroaki Murakami, Ryo Ozaki +1
Human infants acquire their verbal lexicon with minimal prior knowledge of language based on the statistical properties of phonological distributions and the co-occurrence of other…
Multiagent Multimodal Categorization for Symbol Emergence: Emergent Communication via Interpersonal Cross-modal Inference
Yoshinobu Hagiwara, Kazuma Furukawa, Akira Taniguchi +1
This paper describes a computational model of multiagent multimodal categorization that realizes emergent communication. We clarify whether the computational model can reproduce th…
Unsupervised Lexical Acquisition of Relative Spatial Concepts Using Spoken User Utterances
Rikunari Sagara, Ryo Taguchi, Akira Taniguchi +4
This paper proposes methods for unsupervised lexical acquisition for relative spatial concepts using spoken user utterances. A robot with a flexible spoken dialog system must be ab…
A Whole Brain Probabilistic Generative Model: Toward Realizing Cognitive Architectures for Developmental Robots
Tadahiro Taniguchi, Hiroshi Yamakawa, Takayuki Nagai +5
Building a humanlike integrative artificial cognitive system, that is, an artificial general intelligence (AGI), is the holy grail of the artificial intelligence (AI) field. Furthe…
Symbol Emergence in Cognitive Developmental Systems: a Survey
Tadahiro Taniguchi, Emre Ugur, Matej Hoffmann +8
Humans use signs, e.g., sentences in a spoken language, for communication and thought. Hence, symbol systems like language are crucial for our communication with other agents and a…
SERKET: An Architecture for Connecting Stochastic Models to Realize a Large-Scale Cognitive Model
Tomoaki Nakamura, Takayuki Nagai, Tadahiro Taniguchi
To realize human-like robot intelligence, a large-scale cognitive architecture is required for robots to understand the environment through a variety of sensors with which they are…
Online Spatial Concept and Lexical Acquisition with Simultaneous Localization and Mapping
Akira Taniguchi, Yoshinobu Hagiwara, Tadahiro Taniguchi +1
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 nonparametric Ba…
Spatial Concept Acquisition for a Mobile Robot that Integrates Self-Localization and Unsupervised Word Discovery from Spoken Sentences
Akira Taniguchi, Tadahiro Taniguchi, Tetsunari Inamura
In this paper, we propose a novel unsupervised learning method for the lexical acquisition of words related to places visited by robots, from human continuous speech signals. We ad…
Symbol Emergence in Robotics: A Survey
Tadahiro Taniguchi, Takayuki Nagai, Tomoaki Nakamura +3
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 computationa…
Nonparametric Bayesian Double Articulation Analyzer for Direct Language Acquisition from Continuous Speech Signals
Tadahiro Taniguchi, Ryo Nakashima, Shogo Nagasaka
Human infants can discover words directly from unsegmented speech signals without any explicitly labeled data. In this paper, we develop a novel machine learning method called nonp…