16,163 research outputs found
A Review of Verbal and Non-Verbal Human-Robot Interactive Communication
In this paper, an overview of human-robot interactive communication is
presented, covering verbal as well as non-verbal aspects of human-robot
interaction. Following a historical introduction, and motivation towards fluid
human-robot communication, ten desiderata are proposed, which provide an
organizational axis both of recent as well as of future research on human-robot
communication. Then, the ten desiderata are examined in detail, culminating to
a unifying discussion, and a forward-looking conclusion
Agents for educational games and simulations
This book consists mainly of revised papers that were presented at the Agents for Educational Games and Simulation (AEGS) workshop held on May 2, 2011, as part of the Autonomous Agents and MultiAgent Systems (AAMAS) conference in Taipei, Taiwan. The 12 full papers presented were carefully reviewed and selected from various submissions. The papers are organized topical sections on middleware applications, dialogues and learning, adaption and convergence, and agent applications
On the nature and role of intersubjectivity in communication
We outline a theory of human agency and communication and discuss the role that the capability to share (that is, intersubjectivity) plays in it. All the notions discussed are cast in a mentalistic and radically constructivist framework. We also introduce and discuss the relevant literature
An analysis of the application of AI to the development of intelligent aids for flight crew tasks
This report presents the results of a study aimed at developing a basis for applying artificial intelligence to the flight deck environment of commercial transport aircraft. In particular, the study was comprised of four tasks: (1) analysis of flight crew tasks, (2) survey of the state-of-the-art of relevant artificial intelligence areas, (3) identification of human factors issues relevant to intelligent cockpit aids, and (4) identification of artificial intelligence areas requiring further research
Natural Notation for the Domestic Internet of Things
This study explores the use of natural language to give instructions that
might be interpreted by Internet of Things (IoT) devices in a domestic `smart
home' environment. We start from the proposition that reminders can be
considered as a type of end-user programming, in which the executed actions
might be performed either by an automated agent or by the author of the
reminder. We conducted an experiment in which people wrote sticky notes
specifying future actions in their home. In different conditions, these notes
were addressed to themselves, to others, or to a computer agent.We analyse the
linguistic features and strategies that are used to achieve these tasks,
including the use of graphical resources as an informal visual language. The
findings provide a basis for design guidance related to end-user development
for the Internet of Things.Comment: Proceedings of the 5th International symposium on End-User
Development (IS-EUD), Madrid, Spain, May, 201
SCREEN: Learning a Flat Syntactic and Semantic Spoken Language Analysis Using Artificial Neural Networks
In this paper, we describe a so-called screening approach for learning robust
processing of spontaneously spoken language. A screening approach is a flat
analysis which uses shallow sequences of category representations for analyzing
an utterance at various syntactic, semantic and dialog levels. Rather than
using a deeply structured symbolic analysis, we use a flat connectionist
analysis. This screening approach aims at supporting speech and language
processing by using (1) data-driven learning and (2) robustness of
connectionist networks. In order to test this approach, we have developed the
SCREEN system which is based on this new robust, learned and flat analysis.
In this paper, we focus on a detailed description of SCREEN's architecture,
the flat syntactic and semantic analysis, the interaction with a speech
recognizer, and a detailed evaluation analysis of the robustness under the
influence of noisy or incomplete input. The main result of this paper is that
flat representations allow more robust processing of spontaneous spoken
language than deeply structured representations. In particular, we show how the
fault-tolerance and learning capability of connectionist networks can support a
flat analysis for providing more robust spoken-language processing within an
overall hybrid symbolic/connectionist framework.Comment: 51 pages, Postscript. To be published in Journal of Artificial
Intelligence Research 6(1), 199
Towards a satisfactory conversion of messages among agent-based information systems
Over the last years, there has been a change of perspective concerning the management of information systems, since they are no longer isolated and need to communicate with others. However, from a semantic point of view, real communication is difficult to achieve due to the heterogeneity of the systems. We present a proposal which, considering information systems are represented by software agents, provides a framework that favors a semantic communication among them, overcoming the heterogeneity of their agent communication languages. The main components of the framework are a suite of ontologies – conceptualizing communication acts – that will be used for generating the communication conversion, and an Event Calculus interpretation of the communications, which will be used for formalizing the notion of a satisfactory conversion. Moreover, we present a motivating example in order to complete the explanation of the whole picture.The work of Idoia Berges was supported by a grant of the Basque Government (Programa de Formación de Investigadores del Departamento de Educación, Universidades e Investigación). This work is also supported the Spanish Ministry of Education and Science TIN2010–21387-C02–01
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