148,073 research outputs found
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
Integration of Action and Language Knowledge: A Roadmap for Developmental Robotics
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Team Learning: A Theoretical Integration and Review
With the increasing emphasis on work teams as the primary architecture of organizational structure, scholars have begun to focus attention on team learning, the processes that support it, and the important outcomes that depend on it. Although the literature addressing learning in teams is broad, it is also messy and fraught with conceptual confusion. This chapter presents a theoretical integration and review. The goal is to organize theory and research on team learning, identify actionable frameworks and findings, and emphasize promising targets for future research. We emphasize three theoretical foci in our examination of team learning, treating it as multilevel (individual and team, not individual or team), dynamic (iterative and progressive; a process not an outcome), and emergent (outcomes of team learning can manifest in different ways over time). The integrative theoretical heuristic distinguishes team learning process theories, supporting emergent states, team knowledge representations, and respective influences on team performance and effectiveness. Promising directions for theory development and research are discussed
The Hierarchic treatment of marine ecological information from spatial networks of benthic platforms
Measuring biodiversity simultaneously in different locations, at different temporal scales, and over wide spatial scales is of strategic importance for the improvement of our understanding of the functioning of marine ecosystems and for the conservation of their biodiversity. Monitoring networks of cabled observatories, along with other docked autonomous systems (e.g., Remotely Operated Vehicles [ROVs], Autonomous Underwater Vehicles [AUVs], and crawlers), are being conceived and established at a spatial scale capable of tracking energy fluxes across benthic and pelagic compartments, as well as across geographic ecotones. At the same time, optoacoustic imaging is sustaining an unprecedented expansion in marine ecological monitoring, enabling the acquisition of new biological and environmental data at an appropriate spatiotemporal scale. At this stage, one of the main problems for an effective application of these technologies is the processing, storage, and treatment of the acquired complex ecological information. Here, we provide a conceptual overview on the technological developments in the multiparametric generation, storage, and automated hierarchic treatment of biological and environmental information required to capture the spatiotemporal complexity of a marine ecosystem. In doing so, we present a pipeline of ecological data acquisition and processing in different steps and prone to automation. We also give an example of population biomass, community richness and biodiversity data computation (as indicators for ecosystem functionality) with an Internet Operated Vehicle (a mobile crawler). Finally, we discuss the software requirements for that automated data processing at the level of cyber-infrastructures with sensor calibration and control, data banking, and ingestion into large data portals.Peer ReviewedPostprint (published version
Learning about knowledge: A complex network approach
This article describes an approach to modeling knowledge acquisition in terms
of walks along complex networks. Each subset of knowledge is represented as a
node, and relations between such knowledge are expressed as edges. Two types of
edges are considered, corresponding to free and conditional transitions. The
latter case implies that a node can only be reached after visiting previously a
set of nodes (the required conditions). The process of knowledge acquisition
can then be simulated by considering the number of nodes visited as a single
agent moves along the network, starting from its lowest layer. It is shown that
hierarchical networks, i.e. networks composed of successive interconnected
layers, arise naturally as a consequence of compositions of the prerequisite
relationships between the nodes. In order to avoid deadlocks, i.e. unreachable
nodes, the subnetwork in each layer is assumed to be a connected component.
Several configurations of such hierarchical knowledge networks are simulated
and the performance of the moving agent quantified in terms of the percentage
of visited nodes after each movement. The Barab\'asi-Albert and random models
are considered for the layer and interconnecting subnetworks. Although all
subnetworks in each realization have the same number of nodes, several
interconnectivities, defined by the average node degree of the interconnection
networks, have been considered. Two visiting strategies are investigated:
random choice among the existing edges and preferential choice to so far
untracked edges. A series of interesting results are obtained, including the
identification of a series of plateaux of knowledge stagnation in the case of
the preferential movements strategy in presence of conditional edges.Comment: 18 pages, 19 figure
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