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

Analyzing collaborative learning processes automatically

In this article we describe the emerging area of text classification research focused on the problem of collaborative learning process analysis both from a broad perspective and more specifically in terms of a publicly available tool set called TagHelper tools. Analyzing the variety of pedagogically valuable facets of learners’ interactions is a time consuming and effortful process. Improving automated analyses of such highly valued processes of collaborative learning by adapting and applying recent text classification technologies would make it a less arduous task to obtain insights from corpus data. This endeavor also holds the potential for enabling substantially improved on-line instruction both by providing teachers and facilitators with reports about the groups they are moderating and by triggering context sensitive collaborative learning support on an as-needed basis. In this article, we report on an interdisciplinary research project, which has been investigating the effectiveness of applying text classification technology to a large CSCL corpus that has been analyzed by human coders using a theory-based multidimensional coding scheme. We report promising results and include an in-depth discussion of important issues such as reliability, validity, and efficiency that should be considered when deciding on the appropriateness of adopting a new technology such as TagHelper tools. One major technical contribution of this work is a demonstration that an important piece of the work towards making text classification technology effective for this purpose is designing and building linguistic pattern detectors, otherwise known as features, that can be extracted reliably from texts and that have high predictive power for the categories of discourse actions that the CSCL community is interested in

The impact of ICT sophistication on geographically distant networks: the case of space physics as seen from France

This paper examines scientific collaboration between French public research teams and distant partners. We first analyse the role and the development of trust and then, the relation between the degree of sophistication of Information and Communication Technologies (ICT) and the constraint of geographical proximity. In that purpose, we present a typology of the different kinds of knowledge and a classification of technologies. A case study in the field of space physics allows us to confront our theoretical elements to real life. We study the evolution of ICT sophistication parallel to collaboration patterns. Finally, we give some recommendations for public funding of virtual networks.collaboratory, knowledge transfer, trust, ICT classification, space physics

Exploratory Content Analysis Using Text Data Mining: Corporate Citizenship Reports of Seven US Companes from 2004 to 2012

This study demonstrates the use of Text Data Mining (TDM) for exploring the content of a collection of Corporate Citizenship(CC) reports. The collection analyzed comprises CC reports produced by seven Dow Jones companies (Citi, Coca-Cola, ExxonMobil, General Motors, Intel, McDonalds and Microsoft) in2004, 2008 and 2012.Exploratory con-tent analysis using TDM enables insights for CC professionals and analysts, in less time using fewer resources, which in turn could help them explore collaboration opportunities around supply chains, re-training programs, and alternative risk mitigation strategies in terms of governance and compliance. In addition, TDM, using supervised machine learning on the whole collection (or corpus) as well as unsupervised machine learning on document collections by year, suggests the integration of CC considerations related to environmental sustain-ability in CC report components discussing the core business of some firms. This method has been used in many contexts in which a collection of documents needs to be categorized and/or analyzed to uncover new patterns and relationships

Validating the Automated Assessment of Participation and of Collaboration in Chat Conversations

International audienceAs Computer Supported Collaborative Learning (CSCL) gains a broader usage as a viable alternative to classic educational scenarios, the need for automated tools capable of supporting tutors in the time consuming process of analyzing conversations becomes more stringent. Moreover, in order to fully explore the benefits of such scenarios, a clear demarcation must be made between participation or active involvement, and collaboration that presumes the intertwining of ideas or points of view with other participants. Therefore, starting from a cohesion-based model of the discourse, we propose two computational models for assessing collaboration and participation. The first model is based on the cohesion graph and can be perceived as a longitudinal analysis of the ongoing conversation, thus accounting for participation from a social knowledge-building perspective. In the second approach, collaboration is regarded from a dialogical perspective as the intertwining or overlap of voices pertaining to different speakers, therefore enabling a transversal analysis of subsequent discussion slices