Artificial Intelligence V. Methodology, Systems, Applications

260 Pages·1992·18.383 MB·English

by  B. du Boulay, V. Sgurev

ARTIFICIAL INTELLIGENCE V
Methodology, Systems, Applications
Proceedings of the Fifth International Conference on
Artificial Intelligence: Methodology, Systems, Applications
(AIMSA'92)
Sofia, Bulgaria, 21-24 September, 1992
editedby
B. du BOULAY
School of Cognitive and Computing Sciences
University of Sussex
Brighton, UK
and
V.SGUREV
Bulgarian Academy of Sciences
Institute of Industrial Cybernetics and Robotics
Sofia, Bulgaria
1992
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ν
FOREWORD
This book is the Proceedings of AIMSA'92, the Fifth International Confer­
ence on "Artificial Intelligence: Methodology, Systems, Applications", held
in Sofia, Bulgaria, September 21-24, 1992.
It presents recent results and describes ongoing research in Artificial In­
telligence, with emphasis on fundamental questions in several key areas: ma­
chine learning, neural networks, automated reasoning, natural language pro­
cessing, and logic methods in AI. There are also more applied papers in the
fields of vision, architectures for KBS, expert systems and intelligent tutoring
systems. One of the changes since AIMS A'90 has been the increased num­
bers of papers submitted in the fields of machine learning, neural networks
and hybrid systems.
One of the special features of the AIMSA series of conferences is their
coverage of work across both Eastern and Western Europe and the former
Soviet Union as well as including papers from North America. AIMSA'92 is
no exception and this volume provides a unique multi-cultural view of AI.
Enormous changes have occurred in many parts of the world since the
AIMSA conference in 1990. We take this opportunity of congratulating
the conference organisers and those authors from countries undergoing rapid
change for continuing to carry out normal academic duties despite manifold
difficulties. Finally, many thanks are due to Alison Mudd, Jackie Dugard,
Lydia Sinapova and Danail Dochev for assistance in preparing this volume.
Benedict du Boulay Vassil Sgurev
Sussex, U.K. Sofia, Bulgaria
May, 1992
vii
ACKNOWLEDGEMENTS
EUROPEAN COORDINATING COMMITTEE FOR ARTIFICIAL
INTELLIGENCE
CYRIL AND METHODIUS INTERNATIONAL FOUNDATION
BULGARIAN ACADEMY OF SCIENCES:
INSTITUTE OF INFORMATICS
INSTITUTE OF MATHEMATICS
BULGARIAN ARTIFICIAL INTELLIGENCE ASSOCIATION
UNION OF BULGARIAN MATHEMATICIANS
ix
CHAIRMAN OF THE CONFERENCE
Blagovest Sendov - President of IFIP
PROGRAM COMMITTEE
Benedict du Boulay (UK) - Chairman
P. Agre (USA) G. Mcalla (Canada)
M. Barbiceanu (Romania) I. Popchev (Bulgaria)
W. Bibel (Germany) D. Pospelov (Russia)
L. Bole (Poland) A. Ramsay (Ireland)
M. Burstein (USA) J. Self (UK)
I. Futo (Hungary) V. Sgurev (Bulgaria)
E. Haichova (Czecho-Slovakia) L. Steels (Belgium)
P. Jorrand (France) M. Stickel (USA)
V. Khoroshevsky (Russia) C. Thornton (UK)
E. Knuth (Hungary) D. Tufis (Romania.)
J.P. Laurent (France) E. Tyugu (Estonia)
V. Marik (Czecho-Slovakia) D. Young (UK)
BULGARIAN ORGANIZING COMMITTEE
Vassil Sgurev - Chairman
Danail Dochev - Secretary
L. Dakovsky L. Hiev R. Pavlov M. Tachev V. Tomov
ARTIFICIAL INTELLIGENCE V: Methodology, Systems, Applications
B. du Boulay and V. Sgurev (Editors)
© 1992 Elsevier Science Publishers B.V. All rights reserved. 3
SIMILARITY IN ANALOGICAL REASONING
Boicho Kokinov
Institute of Mathematics
Bulgarian Academy of Sciences
B1.8, Acad. G. Bonchev Street
Sofia 1113, BULGARIA
FAX: [phone]
E-mail: [email protected]
Abstract
A computational model of similarity assessment in the
context of analogical reasoning is proposed. Three types of
similarity are defined: associative, semantic and structural
and their specific role in the process of analogical reasoning
is discussed.
Mechanisms for similarity computation are proposed on the
basis of a hybrid cognitive architecture (DUAL). The
interaction between the three types of similarity is
discussed.
Finally, a number of experimental facts is explained in
terms of the model. In particular, the dynamic and context-
dependent aspects of similarity as well as why it is not a
transitive and symmetric relation are discussed.
1. INTRODUCTION
Analogy is based on similarity judgements, thus defining
similarity is crucial for the success of analogy modeling.
Various researchers define various measures of similarity and
therefore concentrate on various types of analogy. People
demonstrate, however, an extraordinary flexibility in
discovering analogies between situations. Moreover, they find
different analogies between the same situations under
different circumstances, thus demonstrating a dynamic context-
dependent measure of similarity. That is why models of human
analogical reasoning should reflect all the diverse kinds of
similarity as well as their dynamic nature.
In the present paper an attempt is made at modeling all
these aspects of similarity. It combines three kinds of
similarity, namely, structural similarity, semantic
4
similarity, and associative similarity, which are considered
as dynamic and context-dependent. A parallel hybrid
(symbolic/connectionist) architecture, DUAL, is used to
support this considerable flexibility without increasing the
complexity of computation.
2. BACKGROUND
The problem of similarity is under intensive study both in
artificial intelligence and psychology. There are various
theories of similarity and various distinctions between types
of similarity.
Thus, for example. Smith (1989) argues for distinguishing
between global and dimensional similarity. Global similarity
is defined in terms of the holistic perception of objects or
situations (resemblance, overall similarity, identity),
whereas dimensional similarity is defined in respect to
certain discriminable dimensions (color, size, form,
structure).
Further, many researchers studying dimensional similarity
differentiate between surface similarity and deep similarity
depending on the choice of the dimension made. Rips (1989)
distinguishes between perceptual and conceptual similarity
depending on whether the features in common are perceptual or
not (moreover, he argues that there is a developmental shift
from perceptual to conceptual similarity). Gentner (1983,
1989) discriminates between attributes (one-place predicates)
and relations (many-place predicates) and defines deep (or
structural) similarity in terms of relations and surface (or
superficial) similarity in terms of attributes (she argues
that there is a developmental shift from attributive to
relational similarity).
Vosniadou (1989) proposes the use of the term salient
similarity instead of surface similarity because some
attributional or perceptual features may be difficult to
access (e.g. the spherical shape of the Earth, the solidity of
the Moon) whereas some relational or conceptual features may
be easy to access in the entity's representation. Thus she
proposes salient similarity to be defined as referring to
similarity grounded in easily retrievable aspects of
representations. Salience can change with the elaboration of
representations and in this way the surface/deep distinction
changes with learning.
There is a number of computational approaches to
similarity. The most common one is rooted in the work of
Tversky (1977) where he proposes the contrast model based on
computation of the degree of feature overlap. This approach
has been further developed and used by many researchers. Thus
Stanfill & Waltz (1986) propose a value differences metric
which extends the overlap metric by goal-dependent feature
weighting and replacing the feature identity constraint by the
feature similarity constraint.
5
The proponents of deep similarity, however, rely on the
common structure in the representation rather than on isolated
features. Thus Gentner (1983) proposes computing the
structural similarity on the basis of the common relational
structures used in the representations, stressing the priority
of higher-order relations. Holyoak & Thagard (1989) define the
degree of isomorphism between two descriptions as a measure
for structural similarity. All the approaches mentioned so far
are based on the representations of both entities whose
similarity is being judged.
Other computational approaches to similarity are based on
memory organization, i.e. two entities are considered to be
similar to the extent their representations are closely
located in the memory. Thus Schank (1982) proposes episodes in
memory to be organized in a way that allows episodes
represented by very different features to be within the same
neighborhood (called TOP) if they share some more abstract
relationships between goals and plans. Thagard et al. (1990)
define two relations to be semantically similar if they are
identical, synonyms, hyponyms (are of the same kind), or
meronyms (are parts of the same whole), i.e. if they are
immediate associates in their memory organization.
There are empirical facts obtained in psychology which have
to be taken into account when modeling similarity; the
produced models have to be able to explain these facts. It is
well known, for example, that superficial similarity plays a
dominant role in retrieval, whereas structural similarity in
mapping and transfer (Ross 1987, 1989, Holyoak & Koh, 1987).
However, there are results which demonstrate that superficial
similarity between elements of the descriptions significantly
influences the mapping process (facilitating or destroying
it) , e.g. constructing a mapping between situations where
similar objects play different roles (cross-mapping) is
difficult for people while more similar objects and relations
are put in correspondence more easily (Gentner & Toupin, 1986,
Holyoak & Koh, 1987, Ross, 1987, 1989, Keane, 1991). Goldstone
et al. (1991) demonstrate that separate features do not
contribute to the similarity measure independently (the
feature independence assumption is not true). Smith (1989)
points out that similarity is neither a transitive nor a
symmetric relation as it is considered in mathematics and in
most AI models (Stanfill & Waltz, 1986). Kokinov (1990)
demonstrates priming effects on analogical reasoning thus
revealing the dynamic nature of similarity. All these facts
should be explained by a model of similarity computation.
3. SIMILARITY IN CONTEXT
Similarity is always estimated in and with respect to a
particular context, i.e. in separate contexts different
aspects of the situations being judged for similarity will be
considered as relevant.
6
So what is context and how is it modeled? The minimal
description of a context is reduced to a description of the
goal of the cognitive system. However, this is often not
enough. A complete description of a context includes
specifications for both the internal and the external
contexts. The former encompasses the reasoner's current state
of mind, including the currently active concepts, facts,
general knowledge, goals, etc. The external context consist of
the reasoner's representations of the currently perceived part
of the environment - not necessarily related to the reasoner's
goals and to the problem situation. The complete description
of the context does not need to be explicit, i.e. to be
represented by a separate structure. It is rather represented
by the whole set of currently active descriptions in the
reasoner's working memory.
With respect to the minimal and complete context
descriptions two different criteria for relevance have been
defined (Kokinov, in press): causal and associative relevance,
respectively.
Causal relevance is defined with respect to the goal of the
cognitive system. It is a binary-valued function: an element
is considered to be relevant if a causal chain connecting that
element with the goal can be found. This definition of
relevance, however, is not quite useful for similarity
judgement because of the high complexity of computations
needed to check the relevance of each element of each
description.
Associative relevance is defined with respect to the whole
context. The degree of connectivity of an element with all
other elements of that context is used as a measure for
relevance of this element. In other words, if the cognitive
system knows that a particular element is somehow connected to
other pieces of knowledge, presently considered as relevant,
even without being able to report the exact nature of these
connections, it will consider it as associatively relevant.
It is the associative relevance which is extensively used
in similarity judgements. There are at least two reasons for
that:
1. its computational tractability - associative relevance is
computed for all memory elements at once;
2. its sensitivity to the whole context, i.e. to every change
in the context - this makes it highly dynamic and context-
dependent .
4. TYPES OF SIMILARITY IN ANALOGICAL REASONING
The current computational model of similarity is being
developed to support a more general model of human reasoning,
called AMBR (Kokinov, 1988, in press), but it can be used also
for modeling human explicit similarity judgements. Within this
model three different types of similarity are distinguished.
7
Semantic Similarity
Two entities are considered as semantically similar if a
common point of view on them can be found, i.e. if a common
superclass at any level can be found including the case where
one of them is the common superclass itself. Two entities are
considered to be semantically similar also when they
correspond to two points of view on the same thing (i.e. both
of them represent one and the same object or concept in the
world).
Semantic similarity is used by the mapping and transfer
processes in analogical reasoning.
Structural Similarity
Two entities are considered as structurally similar when a
mapping between the elements of their descriptions can be
established. Two constraints are imposed on the mapping: 1) to
be an isomorphism and 2) to put only semantically similar
entities into correspondence. Of course, both constraints are
rather weak and can be better described as pressures on the
mapping.
Structural similarity is used by the mapping and transfer
processes in analogical reasoning.
Associative Similarity
The associative similarity between two situations is
measured by the degree to which they tend to produce the same
memory state. This means that the corresponding descriptions
are formed in similar contexts. This is a global (holistic)
measure of similarity. In fact only the associative similarity
between the current situation and all other memorized
descriptions is computed. It is the associative relevance of a
description that is used as a measure for its associative
similarity to the current situation.
In contrast with other types of similarity which compare
two explicitly mentioned entities, the associative similarity
of all memory elements to the current situation is computed at
once.
The associative similarity is used by the retrieval
process.
5. COMPUTATION OF SIMILARITY IN THE DUAL ARCHITECTURE
DUAL is a hybrid (symbolic/connectionist) cognitive
architecture (Kokinov, to appear), where both symbolic and
connectionist processes work on the same structures which are
considered as frames by the symbolic processes while the
connectionist mechanisms consider them simply as nodes and
links.
Symbolism and connectionism are considered as dual aspects
of human cognition, the former representing the world