Analogical reasoning for logic programming

Tausend, Birgit; Bell, Siegfried

doi:10.1007/bfb0017032

Cited by 12 publications

(5 citation statements)

References 4 publications

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“…The remarkable ability of humans to understand novel situations by analogy to familiar ones and to solve new problems by remembering solutions to old analogous ones, has motivated the study of analogy as a non deductive paradigm of reasoning and its application in a variety of domains including law [4], medicine [16], economics [3], mechanical [59]) and software engineering [44,53,54,64,75,76,77].…”

Section: Introductionmentioning

confidence: 99%

Elaborating analogies from conceptual models

Spanoudakis

Constantopoulos

1998

Int. J. Intell. Syst.

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This article defines and analyzes a computational model of similarity which detects analogies between objects based on conceptual descriptions of them, constructed from classification, generalization relations, and attributes. Analogies are detected (elaborated) by functions which measure conceptual distances between objects with respect to these semantic modeling abstractions. The model is domain independent and operational upon objects described in nonuniform ways. It does not require any special forms of knowledge for identifying analogies and distinguishes the importance of distinct object elements. Also, it has a polynomial complexity. Due to these characteristics, it may be used in complex tasks involving intra‐ or interdomain analogical reasoning. So far the similarity model has been applied in the domain of software engineering. First, to support the specification of software requirements by analogical reuse and second, to enable the integration of requirements specifications, generated by the multiple agents involved in information system development. Details of these applications can be found in cited references. Also, we have conducted an empirical evaluation of: (i) the consistency of the estimates generated by the model against human intuition about similarity and (ii) its recall performance in tasks of analogical retrieval, the results of which are presented in this article. © 1996 John Wiley & Sons, Inc.

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Section: Introductionmentioning

confidence: 99%

Elaborating analogies from conceptual models

Spanoudakis

Constantopoulos

1998

Int. J. Intell. Syst.

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“…Any LPS system receives an example set from a target program, and tries to find the target program in a huge search space. Some researchers [13,10] have pointed out that the use of analogy might be helpful in LPS. Such Analo~cal Logic Program Synthesis (ALPS) systems try to find a correct prograrn 1 which is similar to a source program.…”

Section: Introductionmentioning

confidence: 99%

“…In the literatures [7,10], the authors have precisely defined that notion from which only small classes of similarities are derived. On the other hand, in the literature [13], the authors have considered a wider class of similarities that has no firm theoretical basis. Because of the lack of an appropriate theoretical basis on similarities between programs, we have not been able to evaluate ALPS's usefulness.…”

Section: Introductionmentioning

confidence: 99%

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Analogical logic program synthesis from examples

Sadohara

Haraguchi

1995

Lecture Notes in Computer Science

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The purpose of this paper is to present a theory and an algorithm for analogical logic program synthesis from examples. Given a source program and examples, the task of our algorithm is to find a program which explains the examples correctly and is similar to the source program. Although we can define a notion of shnila~:ity in various ways, we consider a class of similarities from the viewpoint of how examples are explained by a program. In a word, two programs are said to be similar if they share a common explanation structure at an abstract level. Using this notion of similarity, we formalize an analogical logic program synthesis and show that our algorithm based on a framework of model inference can identify a desired program.

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“…• software engineering(e.g. reuse of requirements specifications[MH91,MS92] or other software artifacts [SC94a,SC94b], logic programming [TB91]).…”

Section: Overview Of Analogical Similaritymentioning

confidence: 99%

Analogical Similarity of Objects: A Conceptual Modeling Approach

Σπανουδακησ¹

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This dissertation describes a computational model for detecting analogies between objects and estimating their similarity, based on an analysis of conceptual descriptions of these objects.The proposed model adopts a representation framework with three general-purpose semantic modeling abstractions (i.e. classification, generalization and attribution[BMW86,PM88,Myl90]), which are adequate for building conceptual models for a wide spectrum of application domains and tasks. Furthermore, their semantics inherently reveal analogies between such conceptual models.The detection(elaboration) of analogies is achieved by using metrics measuring the distance between conceptual descriptions of objects with respect to each of the prescribed abstractions. The similarity model we propose, consists of three such metrics, namely the classification, generalization and attribution distances.These metrics distinguish the importance of the specific relations they take into account.In particular, the attribution distance is informed about the importance of the attributes of the involved objects by the so called measures of salience. These measures are estimated from beliefs[Sha75] about three properties of attributes (i.e. the charactericity, abstractness and determinance), which are introduced by our model as predicting the importance of attributes to the elaboration of analogies. The beliefs about the truthness of these properties are measured by functions taking into account specific patterns of representing attributes in conceptual models. The exact criteria for hypothesizing analogies in the first place and the functions measuring distances and salience ensure the domain independency of the proposed model. Also, they make it operational under non uniform representations of objects(i.e. representations which are not based on specific sets of relations) and tolerant to the absence of explicitly asserted knowledge determining important factors for the detection of analogies. Hence, the proposed similarity model overcomes identified problems of other models for elaborating analogies[Ked88,Hall89] and offers a generally applicable computational mechanism for this elaboration. The model has been implemented and integrated with the Semantic Index System[CDV93](i.e. a system for representing scientific knowledge and engineering artifacts). The resulted prototype (SIS/SA) was used in a couple of tasks in the domain of software engineering, namely the analogical reuse of requirements specifications[Maid92,SC94a] and the integration of requirements viewpoints[LF91,NKF93]. These applications demonstrated the applicability of the proposed model in complex tasks involving interdomain analogical reasoning.SIS/SA was also used in experiments evaluating the consistency of similarity estimates generated by our model with human assessments, as well as, the recall and the time performance of it. Albeit preliminary, these experiments indicated a promising behavior of the similarity model, with respect to the mentioned aspects of evaluation.

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Analogical reasoning for logic programming

Cited by 12 publications

References 4 publications

Elaborating analogies from conceptual models

Elaborating analogies from conceptual models

Analogical logic program synthesis from examples

Analogical Similarity of Objects: A Conceptual Modeling Approach

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