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Article

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Title

Abductive and Deductive Approach in Learning from Examples Method for Technological Decisions Making

Authors

[ 1 ] Instytut Konstrukcji Budowlanych, Wydział Budownictwa i Inżynierii Środowiska, Politechnika Poznańska | [ P ] employee

Year of publication

2013

Published in

Procedia Engineering

Journal year: 2013 | Journal volume: vol. 57

Article type

scientific article / paper

Publication language

english

Keywords
EN
  • hybrid advisory system
  • case-based reasoning
  • simulation
  • abductive approach
Abstract

EN One of the fundamental problems in building engineering is to ensure conformance between the planned and the actual course of construction works. What may help solve this problem is a database with examples of performance of similar processes in analogous environmental conditions that enables estimating the basic process parameters (time, cost, quality, etc.). Case-based reasoning in a hybrid advisory system may play an important role between the rule-based reasoning and the machine learning when the data that has been gathered enables inference based on analogies with the completed processes, but their number is still inadequate for application of machine learning. Introduction of the abductive approach is intended to enable a detailed analysis of causes of nonconformances – a limited number of examples facilitate this. This is an example of cooperation between a human (search for causes based on intuition) and a computer (systematic gathering of examples). The authors also plan to use simulations to verify the abductive hypotheses. In the final part of the article, an example of application of case-based reasoning in the process of delivery of ready-mix concrete is presented.

Pages (from - to)

583 - 588

DOI

10.1016/j.proeng.2013.04.074

URL

https://www.sciencedirect.com/science/article/pii/S1877705813008072?via%3Dihub

Presented on

11th International Conference on Modern Building Materials, Structures and Techniques, MBMST 2013, 16-17.05.2013, Vilnius, Lithuania

License type

CC BY-NC-ND (attribution - noncommercial - no derivatives)

Open Access Mode

open journal

Open Access Text Version

final published version

Full text of article

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Access level to full text

public

Publication indexed in

WoS (15)

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