Formation of hierarchical object concept using hierarchical latent Dirichlet allocation

Yoshiki, Atsushi; Nagaoka, Tomoaki; Araki, Takaya; Nagai, Takayuki

doi:10.1109/iros.2013.6696674

Cited by 22 publications

(19 citation statements)

References 19 publications

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“…First, (a) shows a personal multimodal categorizer, which is a generative model with an integrated category c as a latent variable and sensor information from haptics and vision as observations o h and o v . The model is a simple version of multimodal latent Dirichlet allocation used as an object categorizer in the previous studies (Nakamura et al, 2009;Ando et al, 2013).…”

Section: Expansion Of a Multimodal Categorizer From Personal To Intermentioning

confidence: 99%

Symbol Emergence as an Interpersonal Multimodal Categorization

et al. 2019

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This study focuses on category formation for individual agents and the dynamics of symbol emergence in a multi-agent system through semiotic communication. Semiotic communication is defined, in this study, as the generation and interpretation of signs associated with the categories formed through the agent's own sensory experience or by exchange of signs with other agents. From the viewpoint of language evolution and symbol emergence, organization of a symbol system in a multi-agent system (i.e., agent society) is considered as a bottom-up and dynamic process, where individual agents share the meaning of signs and categorize sensory experience. A constructive computational model can explain the mutual dependency of the two processes and has mathematical support that guarantees a symbol system's emergence and sharing within the multi-agent system. In this paper, we describe a new computational model that represents symbol emergence in a two-agent system based on a probabilistic generative model for multimodal categorization. It models semiotic communication via a probabilistic rejection based on the receiver's own belief. We have found that the dynamics by which cognitively independent agents create a symbol system through their semiotic communication can be regarded as the inference process of a hidden variable in an interpersonal multimodal categorizer, i.e., the complete system can be regarded as a single agent performing multimodal categorization using the sensors of all agents, if we define the rejection probability based on the Metropolis-Hastings algorithm. The validity of the proposed model and algorithm for symbol emergence, i.e., forming and sharing signs and categories, is also verified in an experiment with two agents observing daily objects in the real-world environment. In the experiment, we compared three communication algorithms: no communication, no rejection, and the proposed algorithm. The experimental results demonstrate that our model reproduces the phenomena of symbol emergence, which does not require a teacher who would know a pre-existing symbol system. Instead, the multi-agent system can form and use a symbol system without having pre-existing categories.

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Section: Expansion Of a Multimodal Categorizer From Personal To Intermentioning

confidence: 99%

Symbol Emergence as an Interpersonal Multimodal Categorization

et al. 2019

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“…A further advancement of such cognitive systems allows the robots to find meanings of words by treating a linguistic input as another modality [13][14][15]. Cognitive models have recently become more complex in realizing various cognitive capabilities: grammar acquisition [16], language model learning [17], hierarchical concept acquisition [18,19], spatial concept acquisition [20], motion skill acquisition [21], and task planning [7] (see Fig. 1).…”

Section: Introductionmentioning

confidence: 99%

Neuro-SERKET: Development of Integrative Cognitive System Through the Composition of Deep Probabilistic Generative Models

et al. 2020

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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 an extension of SERKET, which can compose elemental PGMs developed in a distributed manner and provide a scheme that allows the composed PGMs to learn throughout the system in an unsupervised way. In addition to the head-to-tail connection supported by SERKET, Neuro-SERKET supports tail-totail and head-to-head connections, as well as neural network-based modules, i.e., deep generative models. As an example of a Neuro-SERKET application, an integrative model was developed by composing a variational autoencoder (VAE), a Gaussian mixture model (GMM), latent Dirichlet allocation (LDA), and automatic speech recognition (ASR). The model is called VAE + GMM + LDA + ASR. The performance of VAE + GMM + LDA + ASR and the validity of Neuro-SERKET were demonstrated through a multimodal categorization task using image data and a speech signal of numerical digits.

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“…However, there are hierarchies of categories, e.g., ball and doll belong to the toy category. Griffiths et al ( 2003 ) proposed a hierarchical LDA (hLDA), which is a hierarchical clustering method based on a Bayesian generative model, and it was applied to objects (Ando et al, 2013 ) and places (Hagiwara et al, 2016 ). We consider the possibility of applying hLDA to the proposed method for hierarchical categorization of sensory-channels.…”

Section: Discussionmentioning

confidence: 99%

Cross-Situational Learning with Bayesian Generative Models for Multimodal Category and Word Learning in Robots

Taniguchi

Cangelosi

2017

Front. Neurorobot.

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In this paper, we propose a Bayesian generative model that can form multiple categories based on each sensory-channel and can associate words with any of the four sensory-channels (action, position, object, and color). This paper focuses on cross-situational learning using the co-occurrence between words and information of sensory-channels in complex situations rather than conventional situations of cross-situational learning. We conducted a learning scenario using a simulator and a real humanoid iCub robot. In the scenario, a human tutor provided a sentence that describes an object of visual attention and an accompanying action to the robot. The scenario was set as follows: the number of words per sensory-channel was three or four, and the number of trials for learning was 20 and 40 for the simulator and 25 and 40 for the real robot. The experimental results showed that the proposed method was able to estimate the multiple categorizations and to learn the relationships between multiple sensory-channels and words accurately. In addition, we conducted an action generation task and an action description task based on word meanings learned in the cross-situational learning scenario. The experimental results showed that the robot could successfully use the word meanings learned by using the proposed method.

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Formation of hierarchical object concept using hierarchical latent Dirichlet allocation

Cited by 22 publications

References 19 publications

Symbol Emergence as an Interpersonal Multimodal Categorization

Symbol Emergence as an Interpersonal Multimodal Categorization

Neuro-SERKET: Development of Integrative Cognitive System Through the Composition of Deep Probabilistic Generative Models

Cross-Situational Learning with Bayesian Generative Models for Multimodal Category and Word Learning in Robots

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