Any system that communicates must be able to cope with the possibility
of miscommunication---including misunderstanding, non-understanding, and
misinterpretation:
o In misunderstanding, one participant obtains an interpretation
that she believes is complete and correct, but which is, however,
not the one that the other speaker intended her to obtain.
o In non-understanding, a participant either fails to obtain any
interpretation at all, or obtains more than one interpretation,
with no way to choose among them.
o In misinterpretation, the most likely interpretation of a
participant's utterance suggests that their beliefs about the world
are unexpectedly out of alignment with the other's.
All three forms of miscommunication can eventually lead to repair in
a dialogue; however, misinterpretations and non-understandings are
typically recognized immediately, whereas a participant is not aware,
at least initially, when a misunderstanding occurs. Additionally,
misinterpretation can be a source of misunderstanding.
Successful communication requires that participants share considerable
knowledge. For example, they must share some knowledge about the state
of their interaction and about the physical and social situation in
which they are communicating. Knowledge of their interaction includes
the current topic under discussion (often a shared task), the focus of
attention, and the relevance of each utterance to the previous
interaction. In practice, no two participants start with an identical
understanding of their task or of the situation---nor can they take the
time to identify and resolve discrepancies beforehand. As a result,
participants must be prepared to handle miscommunication during dialogue.
Research related to achieving robust interaction is an important subarea
in Artificial Intelligence (AI). Early work concerned the correction of
spelling or grammatical errors in a user's utterance so that the system
could more easily match them against a fixed linguistic model; work has
also been done in the area of speech recognition, attempting to find the
best fit of a sound signal to legal sequences of linguistic objects.
Other systems have attempted to detect misconceptions in the user's model
of the domain of discourse. All of these approaches have assumed that the
system's model is always correct. More recently, researchers have been
looking at detecting and correcting errors in the system's model of an
interaction. This work includes research on speech repairs,
miscommunication, misunderstanding, non-understanding, and related work
in planning, such as plan misrecognition and plan repair.
The focus of this workshop is to bring together researchers interested in
developing theoretical models of robust interaction or in designing robust
systems. Topics of interest include, but are not limited to, the following:
o Theories that delineate what knowledge must be represented, how
it will be obtained and updated, and how responsibility for
achieving robustness might be distributed among the interactants.
o Strategies for identifying POTENTIAL causes of breakdowns, such as
ambiguities, misconceptions, and plan misrecognition, in order to
avert miscommunication.
o Strategies for identifying symptoms of ACTUAL breakdowns, such as
deviations from expected behavior, unresolvable ambiguities, and
speech errors.
o Techniques for correcting errors in interpretation that have
been used in other areas of AI, such as plan recognition and
computer vision, and in related areas, such as human-computer
interaction and multimedia.
o Approaches to minimizing and correcting miscommunication in
tutoring systems and education.
o Empirical data regarding the occurrence of miscommunication and
approaches to robust communication that derive from empirical
methods.
o Research in knowledge representation that would be useful
in detecting, repairing, and preventing miscommunication.
We solicit papers that explore these issues, and papers that discuss
implementations of solutions to the problems of detecting, repairing,
and preventing human--machine miscommunication. Papers submitted to
the workshop should address these topics explicitly. As AAAI procedures
require, participation will be limited to 65.
COMMITTEE:
Susan McRoy, chair
University of Wisconsin--Milwaukee
mcroy@cs.uwm.edu
(414) 229--6695 (phone)
(414) 229--6958 (fax)
Brad Goodman Kathleen McCoy
Mitre Corporation University of Delaware
bgoodman@linus.mitre.org mccoy@louie.udel.edu
Susan Haller Ronnie Smith
University of Wisconsin--Parkside East Carolina University
haller@cs.uwp.edu rws@math1.math.ecu.edu
Graeme Hirst David Traum
University of Toronto TECFA, Universite de Geneve
gh@cs.toronto.edu David.Traum@tecfa.unige.ch
SCHEDULE:
Submission deadline: March 18, 1996
Author notification: April 15, 1996
Camera-ready copy due: May 13, 1996
Conference dates: August 4--8, 1996
SUBMISSIONS:
Submit an extended abstract. Abstracts should not exceed 10 pages,
exclusive of references, in 12 point, double-spaced text, with one-inch
margins.
We strongly encourage electronic submissions, either plain text or
postscript. Emailed submissions should be emailed to
mcroy@cs.uwm.edu with a subject heading ``ATTN: AAAI MNM''.
In the event that electronic submission is not possible, send 6
copies to:
Susan McRoy
ATTN: AAAI MNM Workshop
Computer Science, University of Wisconsin--Milwaukee
3200 North Cramer Street, EMS Room 503
Milwaukee, WI 53211
This cfp is on the WWW at http://www.cs.uwm.edu/faculty/mcroy/mnm.html