[robotics-worldwide] 2nd CFP: ACM TiiS special issue on Machine Learning for Multiple Modalities in Interactive Systems and Robots

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[robotics-worldwide] 2nd CFP: ACM TiiS special issue on Machine Learning for Multiple Modalities in Interactive Systems and Robots

Heriberto Cuayahuitl-2
Second Call for Papers

Special Issue of the
ACM Transactions on Interactive Intelligent Systems
on MACHINE LEARNING FOR MULTIPLE MODALITIES
IN INTERACTIVE SYSTEMS AND ROBOTS

Main submission deadline: February 28th, 2013
http://tiis.acm.org/special-issues.html#si-call-ml4mm

AIMS AND SCOPE

This special issue will highlight research that applies machine
learning to robots and other systems that interact with users
through more than one modality, such as speech, touch, gestures,
and vision.

Interactive systems such as multimodal interfaces, robots, and
virtual agents often use some combination of these modalities to
communicate meaningfully. For example, a robot may coordinate its
speech with its actions, taking into account visual feedback
during their execution. Alternatively, a multimodal system can
adapt its input and output modalities to the user's goals,
workload, and surroundings. Machine learning provides interactive
systems with opportunities to improve performance not only of
individual components but also of the system as a whole. However,
machine learning methods that encompass multiple modalities of an
interactive system are still relatively hard to find. This
special issue aims to help fill this gap.

The dimensions listed below indicate the range of work that is
relevant to the special issue. Each article will normally
represent one or more points on each of these dimensions. In case
of doubt about the relevance of your topic, please contact the
special issue associate editors.

TOPIC DIMENSIONS

System Types
- Interactive robots
- Embodied virtual characters
- Avatars
- Multimodal systems

Machine Learning Paradigms
- Reinforcement learning
- Active learning
- Supervised learning
- Unsupervised learning
- Any other learning paradigm

Functions to Which Machine Learning Is Applied
- Multimodal recognition and understanding in dialog with users
- Multimodal generation to present information through several channels
- Alignment of gestures with verbal output during interaction
- Adaptation of system skills through interaction with human users
- Any other functions, especially combining two or all of speech,
touch, gestures, and vision

SPECIAL ISSUE ASSOCIATE EDITORS

- Heriberto Cuayahuitl, Heriot-Watt University, UK
  (contact: h.cuayahuitl[at]gmail[dot]com)
- Lutz Frommberger, University of Bremen, Germany
- Nina Dethlefs, Heriot-Watt University, UK
- Antoine Raux, Honda Research Institute, USA
- Matthew Marge, Carnegie Mellon University, USA
- Hendrik Zender, Nuance Communications, Germany

IMPORTANT DATES

- By February 28th, 2013: Submission of manuscripts
- By June 12th, 2013: Notification about decisions on initial
  submissions
- By September 10th, 2013: Submission of revised manuscripts
- By November 9th, 2013: Notification about decisions on revised
  manuscripts
- By December 9th, 2013: Submission of manuscripts with final
  minor changes
- Starting January, 2014: Publication of the special issue on the TiiS
  website, in the ACM Digital Library, and subsequently as a
  printed issue

HOW TO SUBMIT

Please see the instructions for authors on the TiiS website
(tiis.acm.org).

ABOUT ACM TiiS

TiiS (pronounced "T double-eye S"), launched in 2010, is an ACM
journal for research about intelligent systems that people
interact with.

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