Tag Archives: Symbol Grounding

Weighting relations between concepts to form (hierarchically) further concepts

T. Nakamura and T. Nagai, Ensemble-of-Concept Models for Unsupervised Formation of Multiple Categories, IEEE Transactions on Cognitive and Developmental Systems, vol. 10, no. 4, pp. 1043-1057, DOI: 10.1109/TCDS.2017.2745502.

Recent studies have shown that robots can form concepts and understand the meanings of words through inference. The key idea underlying these studies is the “multimodal categorization” of a robot’s experiences. Despite the success in the formation of concepts by robots, a major drawback of previous studies stems from the fact that they have been mainly focused on object concepts. Obviously, human concepts are limited not only to object concepts but also to other kinds such as those connected to the tactile sense and color. In this paper, we propose a novel model called the ensemble-of-concept models (EoCMs) to form various kinds of concepts. In EoCMs, we introduce weights that represent the strength connecting modalities and concepts. By changing these weights, many concepts that are connected to particular modalities can be formed; however, meaningless concepts for humans are included in these concepts. To communicate with humans, robots are required to form meaningful concepts for us. Therefore, we utilize utterances taught by human users as the robot observes objects. The robot connects words included in the teaching utterances with formed concepts and selects meaningful concepts to communicate with users. The experimental results show that the robot can form not only object concepts but also others such as color-related concepts and haptic concepts. Furthermore, using word2vec, we compare the meanings of the words acquired by the robot in connecting them to the concepts formed.

A cognitive architecture for self-development in robots that interact with humans, with a nice state-of-the-art of robot cognitive architectures

C. Moulin-Frier et al., DAC-h3: A Proactive Robot Cognitive Architecture to Acquire and Express Knowledge About the World and the Self, IEEE Transactions on Cognitive and Developmental Systems, vol. 10, no. 4, pp. 1005-1022, DOI: 10.1109/TCDS.2017.2754143.

This paper introduces a cognitive architecture for a humanoid robot to engage in a proactive, mixed-initiative exploration and manipulation of its environment, where the initiative can originate from both human and robot. The framework, based on a biologically grounded theory of the brain and mind, integrates a reactive interaction engine, a number of state-of-the-art perceptual and motor learning algorithms, as well as planning abilities and an autobiographical memory. The architecture as a whole drives the robot behavior to solve the symbol grounding problem, acquire language capabilities, execute goal-oriented behavior, and express a verbal narrative of its own experience in the world. We validate our approach in human-robot interaction experiments with the iCub humanoid robot, showing that the proposed cognitive architecture can be applied in real time within a realistic scenario and that it can be used with naive users.

Emergence of symbols in robotics as a “new” area of research in developmental robotics: a survey

Tadahiro Taniguchi, Takayuki Nagai, Tomoaki Nakamura, Naoto Iwahashi, Tetsuya Ogata, Hideki Asoh, Symbol Emergence in Robotics: A Survey, arXiv:1509.08973.

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.

How to make that a symbol becomes related to things on which it is not grounded, and a nice introduction to the symbolist/subsymbolist dilemma

Veale, Tony and Al-Najjar, Khalid (2016). Grounded for life: creative symbol-grounding for lexical invention. Connection Science 28(2). DOI: 10.1080/09540091.2015.1130025

One of the challenges of linguistic creativity is to use words in a way that is novel and striking and even whimsical, to convey meanings that remain stubbornly grounded in the very same world of familiar experiences as serves to anchor the most literal and unimaginative language. The challenge remains unmet by systems that merely shuttle or arrange words to achieve novel arrangements without concern as to how those arrangements are to spur the processes of meaning construction in a listener. In this paper we explore a problem of lexical invention that cannot be solved without a model ? explicit or implicit ? of the perceptual grounding of language: the invention of apt new names for colours. To solve this problem here we shall call upon the notion of a linguistic readymade, a phrase that is wrenched from its original context of use to be given new meaning and new resonance in new settings. To ensure that our linguistic readymades ? which owe a great deal to Marcel Duchamp’s notion of found art ? are anchored in a consensus model of perception, we introduce the notion of a lexicalised colour stereotype.