Case-based reasoning and the deep structure approach to knowledge representation

1991 ◽  
Author(s):  
Andrzej Kowalski
Keyword(s):  
Knowledge Representation ◽  
Deep Structure ◽  
Case Based Reasoning ◽  
Structure Approach ◽  
Case Based

scholarly journals Research on new creative conceptual design system using adapted case-based reasoning technique

2016 ◽  
Vol 31 (1) ◽  
pp. 16-29 ◽  
Author(s):  
Jie Hu ◽  
Jin Ma ◽  
Jin-Feng Feng ◽  
Ying-Hong Peng
Keyword(s):  
Knowledge Representation ◽  
Conceptual Design ◽  
Design System ◽  
Design Knowledge ◽  
Case Based Reasoning ◽  
Pump Design ◽  
Design Models ◽  
Representation Model ◽  
Previous Design ◽  
Case Based

AbstractCreative conceptual design requires significant previous design knowledge. Case-based reasoning enables learning from previous design experience and has a great potential in supporting creative conceptual design by means of seeking to retrieve, reuse, and revise most appropriate cases to generate inspired solutions. However, traditional case-based reasoning based creative conceptual design models focus on design strategies research, pay little attention to defining a consistent knowledge representation model, and neglect the research to make various types of knowledge retrieval tractable. Faced with such drawbacks, the expected design knowledge cannot be retrieved properly, especially in cases where multidisciplinary knowledge is concerned or exact query terms are absent. In order to solve these issues, this paper presents a combined approach to support creative conceptual design process. First, function–behavior–structure knowledge cell is introduced as a unified consistent design knowledge representation model. Second, a hybrid similarity measure is proposed to increase the overall possibility of obtaining useful design knowledge by considering semantic understanding ability. Third, an intelligent creative conceptual design system has been developed with a case study of a novel insulin pump design to demonstrate its usage, and two experiments are conducted to evaluate the performance of the proposed approach. The results show that the proposed approach outperforms other case-based reasoning based creative conceptual design models.

scholarly journals Implementasi Case Base Reasoning Untuk Mendiagnosa Penyakit Gigi dan Mulut

Techno Com ◽  
2017 ◽  
Vol 16 (1) ◽  
pp. 70-79
Author(s):  
Fryda Fatmayati ◽  
Kusrini ◽  
Emha Taufiq Lutfi
Keyword(s):  
Knowledge Representation ◽  
Euclidean Distance ◽  
Case Based Reasoning ◽  
Jaccard Coefficient ◽  
Tanimoto Coefficient ◽  
Case Base ◽  
Case Base Reasoning ◽  
Case Based

Penyakit gigi dan mulut dapat dialami oleh semua orang mulai dari anak-anak hingga dewasa.Namun karena biaya berobat ke dokter gigi yang mahal maka masyarakat enggan memeriksanakan keluhannya terutama pada masyarakat kalangan menengah ke bawah. Padahal jika penyakit gigi dan mulut tidak segera dirawat akan bertambah parah. Case-Based Reasoning meniru kemampuan manusia, yaitu menyelesaikan masalah baru menggunakan jawaban atau pengalaman dari masalah lama.Penyajian pengetahuan (knowledge representation) dibuat dalam bentuk kasus-kasus (case).Setiap kasus berisi masalah dan jawaban, sehingga kasus lebih mirip dengan suatu pola tertentu.Cara kerja Case-Based Reasoning adalah dengan membandingkan kasus baru dengan kasus lama. Jika tidak ada yang cocok maka Case-Based Reasoning akan melakukan adaptasi, dengan cara memasukkan kasus baru tersebut ke dalam database penyimpanan kasus (case base), sehingga secara tidak langsung pengetahuan CBR akan bertambah. Tujuan dari penelitian ini, yaitu mengetahui kemiripan kasus baru dan kasus lama dengan penerapan Case-Based Reasoning (CBR) dan membandingkan dua metode yang digunakan, yaitu Extended Jaccard Coefficient (Tanimoto Coefficient) dan Euclidean Distance similarity dengan memilih hasil akurasi terbaik dari kedua metode tersebut. Hasil pengujian terhadap data uji penyakit gigi dan mulut menunjukkan sistem memiliki unjuk kerja dengan tingkat akurasi menggunakan metode Extended Jaccard Coefficient sebesar 95.24% dan Euclidean Distance Similarity sebesar 100%.   Kata kunci—Case Base Reasoning, Extended Jaccard Coefficient, Euclidean Distance Similarity, penyakit gigi dan mulut 

Case-based reasoning foundations

2005 ◽  
Vol 20 (3) ◽  
pp. 203-207 ◽  
Author(s):  
MICHAEL M. RICHTER ◽  
AGNAR AAMODT
Keyword(s):  
Machine Learning ◽  
Knowledge Representation ◽  
Cognitive Science ◽  
Case Based Reasoning ◽  
Science Knowledge ◽  
Processing Machine ◽  
And Mathematics ◽  
Case Based

A basic observation is that case-based reasoning has roots in different disciplines: cognitive science, knowledge representation and processing, machine learning and mathematics. As a consequence, there are foundational aspects from each of these areas. We briefly discuss them and comment on the relations between these types of foundations.

scholarly journals Implementation of Fuzzy Legal Expert System FLES

2000 ◽  
Vol 4 (6) ◽  
pp. 421-427
Author(s):  
Kaoru Hirota ◽  
◽  
MingQiang Xu ◽  
Yasufumi Takama ◽  
Hajime Yoshino ◽  
...  
Keyword(s):  
Expert System ◽  
Knowledge Representation ◽  
Similarity Measure ◽  
Case Based Reasoning ◽  
United Nation Convention ◽  
Legal Expert ◽  
International Sale Of Goods ◽  
Case Based ◽  
United Nation

A fuzzy legal expert system FLES based on a fuzzy Housdorff similarity measure is implemented. The reasoning approach in this system includes the fuzzy case-based reasoning that is composed of knowledge representation, retrieval, and inference. The proposed approaches are illustrated by the experiments, where the target law is CISG (United Nation Convention on Contract for the International Sale of Goods).

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