[PlanetKR] Robustness 2011 @ Stanford University

[Mary-Anne Williams]

Inconsistency Robustness 2011

Stanford University
August 16-18, 2011

Inconsistency robustness is information system performance in the face of
continually pervasive inconsistencies---a shift from the previously dominant
paradigms of inconsistency denial and inconsistency elimination attempting
to sweep them under the rug.

Illustrative Issues

In fact, inconsistencies are pervasive throughout our information
infrastructure and they affect one another. Consequently, an
interdisciplinary approach is needed. For example, (in no particular order
):

 Computational linguistics relies on human-annotated data to train machine
learners. Inconsistency among the human
annotators must be carefully managed (otherwise, the annotations are useless
in computation). How can this
annotation process be made scalable?
 What are the limitations in the ability of a many-core computer software
system to measure and diagnose its own
performance?
 How to deal with the strategic inconsistency between classical
microeconomics (i.e. individual economic transactions
lead to generally desirable outcomes) and Keynesian macroeconomics (i.e.
fraud, externalities, and monetary
instabilities require government regulation)?
 What should be the federal investment in science and engineering research
next year?
 In teaching situations (e.g. with infants, avatars, or robots), how does a
teacher realize that they need to help correct a
learner and how does a learner realize what correction is needed?
 Is privacy protection inconsistent with preventing terrorism?
 How do appellate courts reconcile inconsistent decisions of lower courts?
 If interlocutors in the same organization hold inconsistent positions, how
do they negotiate? If the interlocutors are in
 separate organizations with overlapping concerns, how are the negotiations
different?
 Is the existence of an observer-independent objective view of reality
inconsistent with the laws of physics?
 What kind of regulation is consistent with innovation?
 How are inconsistencies in law related to inconsistencies in science?
 Is there a mathematical logic for robust reasoning in pervasively
inconsistent theories?
 Does the human brain mediate inconsistencies among its constituent parts?

In each case, inconsistencies need to be precisely identified and their
consequences explored.

Innovation

Inconsistency robustness differs from previous paradigms based on belief
revision, probability, and uncertainty as follows:
 Belief revision: Large information systems are continually, pervasively
inconsistent and there is no way to revise them to attain consistency.
 Probability and fuzzy logic: In large information systems, there are
typically several ways to calculate probability. Often the result is that
the probability is both close to 0% and close to 100%!
 Uncertainty: Resolving uncertainty to determine truth is not a realistic
goal in large information systems.

Particulars
This interdisciplinary 3-day symposium, broadly based on theory and
practice, addresses fundamental issues in inconsistency robustness. (On the
day after the symposium there will be a workshop of
researchers, practitioners, and sponsors to take stock and investigate
possibilities for further research and development in this area.)

This symposium is the first one on the subject building on previous related
work in inconsistency tolerance. Consequently, position statements and
overviews of previous work are explicitly encouraged. Also, submission of
preliminary provisional results is encouraged. Submissions will be refereed
by the program committee and those accepted will be published in the
proceedings. The symposium program will consist of presentations of accepted
refereed submissions, panel discussions, and invited lectures that will be
video-recorded, edited, and
posted on the Internet after the end of the symposium.

Topics of Interest include: affect and sentiment, argumentation, authority
and accountability, classifications and ontologies, collaboration, design,
discretion, education, efficiency, finance, history, innovation,
judgment, language, organizational management, prediction, provenance, risk
management, repair, social structure, scalability, and timeliness.

Important dates

February 15, 2011: Due date of extended abstracts for position statements,
overviews, panel
proposals, and technical papers (consisting of approximately 2000 words in
PDF format ). Rather than have authors try to pigeon hole their work by key
word ahead of time, we plan to distribute submissions to referees on the
basis of the extended abstract.
March 31, 2011: Due date of full technical papers, revised panel proposals
and position statements. A hard limit on size will not be imposed because
the proceedings will be produced electronically. So anything up to about 25K
words would be possible. Of course, the length must be suitable to
the subject matter.

May 30, 2011: Notification of acceptance, conditional acceptance, or
non-acceptance

August 16-18, 2011: Symposium at Stanford

August 19, 2011: Invited workshop for active researchers, practitioners, and
sponsors

Submissions should be made via the EasyChair website at:
http://submit.robust11.org

Program Committee (additions pending in biology, economics, physics,
sociology, and statistics). The current program committee is as follows
(affiliations are listed only for the purpose of identification):

Fei Xia, Washington Linguistics
Mary-Anne Williams, Sydney Innovation and Enterprise Research Lab
Mario Tokoro, Sony CSL
Jamie Taylor, Google
Markus Strohmaier, Graz CS
Tom Stace, Queensland Physics
Yuval Shachar, Ben-Gurion Information Systems Engineering
Erik Sandewall, Linköping Computer and Information Science
Neil Rubens, Electro-Communications Information Systems
Carlo Rovelli, Marseille Centre de Physique Theorique de Luminy
Greg Restall, Melbourne Philosophy
Kay Prüfer, Max-Planck Institute for Evolutionary Anthropology
Stanley Peters, Stanford CSLI
Peter Neumann, SRI
Fanya S. Montalvo, independent consultant
Subhasish Mitra, Stanford CS and EE
Joao Marcos, Rio Grande de Norte Informatics and Applied Mathematics
Ben Kuipers, Michigan CS
Andrei Khrennikov, Linnaeus Applied Mathematics
Mike Huhns, South Carolina Electrical & Computer Engineering
Chuck House, Stanford Media X
Robert Hoehndorf, Cambridge Genetics
Carl Hewitt (chair), emeritus MIT EECS, visiting Stanford CS
Ted Goldstein, UCSC Bioinformatics and Biomolecular Engineering
Elihu M. Gerson, Tremont Research Institute
Mike Genesereth, Stanford CS
Giacomo Mauro D'Ariano, Pavia Quantum Information, Mechanics, & Optics
Rainer Brendle, SAP
Francesco Berto, Aberdeen Philosophy
Gil Alterovitz, MIT EECS and Harvard Medical School

Background
There are many examples of practical inconsistency robustness including the
following:

 Our economy relies on large software systems that have tens of thousands
of known inconsistencies (often called “bugs”) along with tens of thousands
more that have yet to be pinned down even though their symptoms are
sometimes obvious.
 Physics has progressed for centuries in the face of numerous
inconsistencies including the ongoing decades-long inconsistency between its
two most fundamental theories (general relativity and quantum mechanics).
 Decision makers commonly ask for the case against as well as the case for
proposed findings and action plans in corporations, governments, and
judicial systems.
Inconsistency robustness stands to become a more central theme for
computation. The basic argument is that because inconsistency is continually
pervasive in large information systems, the issue of
inconsistency robustness must be addressed! And the best way to address the
issue is computationally. Inconsistency robustness is both an observed
phenomenon and a desired feature:
 It is an observed phenomenon because large information systems are
required to operate in an environment of pervasive inconsistency. How are
they doing?
 It is a desired feature because we need to improve the performance of
large information systems.

Electronic version of Call: http://call.robust11.org




------
Professor Mary-Anne Williams
Associate Dean (Research and Development)
Faculty of Engineering and Information Technology
University of Technology, Sydney
*
Research and Development Office
Building 2 Level 7 Room 7092
*P.O. 123 Broadway NSW 2007 Australia
Phone: + 61 2 9514 2663 (Gunasmin)
Facsimile: + 61 2 9514 2868
http://innovation.it.uts.edu.au/Mary-Anne
 <http://innovation.it.uts.edu.au/Mary-Anne>
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