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260 Pages·1992·18.383 MB·English
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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 NORTH-HOLLAND AMSTERDAM · LONDON · NEW YORK · TOKYO ELSEVIER SCIENCE PUBLISHERS B.V. Sara Burgerhartstraat25 P.O. Box 211,1000 AE Amsterdam, The Netherlands ISBN: 0-444-89752-6 © 1992 Elsevier Science Publishers B.V. All rights reserved. No part of this publication may be reproduced, stored in a retrieval system or transmitted in any form or by any means, electronic, mechanical, photocopying, recording or other­ wise, without the prior written permission of the publisher, Elsevier Science Publishers B.V, Copyright & Permissions Department, P.O. Box 521, 1000 AM Amsterdam, The Netherlands. Special regulations for readers in the U.S.A. - This publication has been registered with the Copyright Clearance Center, Inc. (CCC), Salem, Massachusetts. Information can be obtained from the CCC about conditions under which photocopies of parts of this publication may be made in the U.S.A. All other copyright questions, including photo­ copying outside of the U.S.A., should be referred to the copyright owner, Elsevier Science Publishers B.V, unless otherwise specified. No responsibility is assumed by the publisher for any injury an/or damage to persons or property as a matter of products liability, negligence or otherwise, or from any use or operation of any methods, products, instructions or ideas contained in the material herein. pp. 47-56, 57-66, 125-130: Copyright not transferred. This book is printed on acid-free paper. Printed in The Netherlands 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: (+359) 2-752078 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

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