=Paper=
{{Paper
|id=Vol-2028/CAW2017_preface
|storemode=property
|title=None
|pdfUrl=https://ceur-ws.org/Vol-2028/CAW2017_preface.pdf
|volume=Vol-2028
}}
==None==
https://ceur-ws.org/Vol-2028/CAW2017_preface.pdf
9
Preface
Computational Analogy and Case-Based Reasoning (CBR) are closely related
research areas. Both employ prior cases to reason in complex situations with
incomplete information. Analogy research often focuses on modeling human cog-
nitive processes, the structural alignment between a case/source and target, and
adaptation/abstraction of the analogical source content. While CBR research
also deals with alignment and adaptation, the field tends to focus more on re-
trieval, case-base maintenance, and pragmatic solutions to real-world problems.
However, despite their obvious overlap in research goals and approaches, cross
communication and collaboration between these areas has been progressively
diminishing. CBR and Analogy researchers stand to benefit greatly from in-
creased exposure to each others work and greater cross-pollination of ideas. The
objective of this workshop is to promote such communication by bringing to-
gether researchers from the two areas, to foster new collaborative endeavors, to
stimulate new ideas and avoid reinventing old ones.
In this second edition of the ICCBR Workshop on Computational Analogy,
8 papers have been selected for presentation (on 9 submissions) from researchers
coming from USA, France, and Japan).
A first set of contributions concern formal aspects of the analogical inference,
its complexity and evaluation. Henri Prade and Gilles Richard provide a state
of the art on analogical-proportion based inference. As recent results show that
affine Boolean functions can be predicted without error by means of analogical
proportions, the authors discuss how one might take advantage of this result to
refine the scope of application of the analogical-proportion based inference to
subparts of a Boolean function that may be assumed to be “locally” linear.
Also contributing to formal analogy research, Yves Lepage addresses the
problem of answering analogy questions of the type A : B :: C : D between word
forms where the unknown is D. The author proposes to add a new criterion
for partial determination of the solution to an analogy question: the pairwise
indices of the positions of the characters. A character-position matrix is built
which assigns a probability to each character and position in the answer D of
an analogy question A : B :: C : D.
Two papers adress the issue of the evaluation of an analogical inference. The
work of Pierre-Alexandre Murena and his colleagues aim at testing the hypothe-
sis that the relevance of an analogical solution can be measured by the complexity
of the analogy. In order to compute the complexity, they propose some specifica-
tions for a prototype language used to describe analogies in a basic alphanumeric
micro-world domain. Joseph Blass and his colleagues take another approach to
evaluation. Starting from the observation that inferred facts are, even if the rea-
soning technique is sound, only as accurate as the assumptions upon which they
are based, they propose a domain-independent method to evaluate inferences for
analogical reasoning, via a prototype system.
10
II
Another set of contributions concern analogy for reuse and adaptation. Scott
Friedman and his colleagues explore how analogical mappings can be used to
help humans and computers negotiate to define shared goals and collaborate
over the fulfillment of those goals. Their application domain is plan localization,
the problem of establishing the set of steps within the plan that are candidates
(potentially after some adaptive repair actions) for next actions given the worlds
unforeseeen changes. Fadi Badra develops a qualitative modeling approach of
the case-based analogical inference, and proposes a language to represent and
symbolically reason upon differences between cases. This language can be used
to represent both similarity paths and adaptation rules.
A last contribution uses machine learning techniques to learn how to solve
analogical equations in the domain of Natural Language Processing. Rashel Fam
and Yves Lepage analyse the characteristics of a data structure called an ”ana-
logical grid”, which can be used to predict new word forms (morphological forms)
from purely surface level observations of the words found in text. In particular,
the saturation of analogical grids is measured against their size. Reported results
show that the logarithm of the saturation of an analogical grid is linear in the
logarithm of its size, and the relation between the saturation and the size of an
analogical grid is almost independent of the size, the genre and the language of
a text.
June 2017 Fadi Badra and Tarek Besold
Program Chairs
Computational Analogy Workshop 2017
11
Organization
Organizing Commitee
Fadi Badra (Université Paris 13, France)
Tarek Besold (University of Bremen, Germany)
Joe Blass (Northwestern University, USA)
Tesca Fitzgerald (Georgia Institute of Technology, USA)
Vincent Letard (LIMSI CNRS, France)
Program Commitee
Myriam Bounhas (LARODEC, Tunisia)
Mark Burstein (SIFT, USA)
Hernan Casakin (Ariel University, Israel)
Amaresh Chakrabarti (Indian Institute of Science, India)
Antoine Cornuéjols (LRI, Orsay, France)
Mark Finlayson (FIU, USA)
Kenneth Forbus (Northwestern University, Chicago, USA)
Scott Friedman (SIFT, USA)
Katherine Fu (Georgia Institute of Technology, Atlanta, USA)
Bipin Indurkhya (Jagiellonian University, Poland)
Mark Keane (UC Dublin, Ireland)
Philippe Langlais (Université de Montreal, Canada)
Yves Lepage (Waseda University, Japan)
Abhijit Mahabal (Google, USA)
Santiago Ontanon (Drexel University, USA)
Henri Prade (IRIT, Toulouse, France)
Gilles Richard (Universit Paul Sabatier, Toulouse, France)
Emmanuel Sander (Universit Paris 8, Paris, France)
Ute Schmid (Universität Bamberg, Germany)
Steven Schockaert (Cardiff University, UK)
Christian Schunn (University of Pittsburg, USA)
Bryan Wiltgen (Georgia Institute of Technology, USA)