Successful Case-based Reasoning Applications (Studies in Computational Intelligence, 305)

✍ Scribed by Stefania Montani (editor)

• Publisher: Springer
• Year: 2010
• Tongue: English
• Leaves: 227
• Category: Library

✦ Synopsis

Case-based reasoning (CBR) is an Artificial Intelligence (AI) technique to support the capability of reasoning and learning in advanced decision support systems. CBR exploits the specific knowledge collected on previously encountered and solved situations, which are known as cases. In this book, we have collected a selection of papers on very recent CBR applications. These, after an in-depth analysis of their specific application domain needs, propose proper methodological solutions and give encouraging evaluation results, which have in some cases led to the commercialization step. The collected contributions demonstrate the capability of CBR to solve or handle issues which would be too difficult to manage with other classical AI methods and techniques, such as rules or models. The heterogeneity of the involved application domains indicates the flexibility of CBR, and its applicability in all those fields where experiential knowledge is (readily) available.

✦ Table of Contents

Title
Preface
Editors
Table of Contents
Innovations in Case-Based Reasoning Applications
Introduction
Chapters Included in the Book
Conclusion
References
Case-Based Reasoning for Medical and Industrial Decision Support Systems
Introduction
Case-Based Reasoning (CBR)
The CBR Cycle
Medical Decision Support System: An Application in Stress Management
Stress
CBR System for Stress Diagnosis and Treatment
Textual Information Retrieval in Stress Management
Fuzzy Rule-Based Classification into CBR
Experimental Work
Decision Support in Industrial Applications
Intelligent Agents
Factors Affecting Decisions by Agents
Designing and Building Agent-Based Systems Using Artificial Intelligence
Conclusion
References
Development of Industrial Knowledge Management Applications with Case-Based Reasoning
Introduction
Defining and Designing a CBR System
User Types
Application Domain
Organizational Aspects
CBR System Development Steps
Initial Application Area and Project Team Selection, Project Kick-Off
Project Team Training
Requirements Specification
Data Analysis and Vocabulary Definition
Selection and Impact of Representation
Initial Knowledge Acquisition
System Design
Conclusion
References
Case Based Reasoning for SupportingStrategy Decision Making in Small and Medium Enterprises
Introduction
Research and Implementation Problems
Case-Based Reasoning as the Implementation Framework
The Main Idea
Knowledge Representation
The CBR Cycle
Empirical Evaluation
Final Remarks
References
Heterogeneity in Ontological CBR Systems
Introduction
Related Work
COBRA Architecture
Domain Model
Case Model
System Processes
Case Authoring
Case Retrieval and Heterogeneity Problems
Case Alignment
Similarity Measures
Similarity-Based Alignment
Role-Based Alignement
Case Alignment Approach: An Overview
Illustrative Example
COBRA: Ontology-Based CBR Platform
Conclusion
References
The Adaptation Problem in Medical Case–Based Reasoning Systems
Introduction
Medical Case–Based Reasoning Systems
Avoiding the Adaptation Task
Solving the Adaptation Problem
CBR Systems and Adaptation
Retrieval-Only for the Kidney Function
Adaptation Rules for Dysmorphic Diagnosis
Constraints for Antibiotic Therapy Advice
Compositional Adaptation in TeCoMed
Adaptation Problems in Endocrine Therapy Support
Computing Initial Doses
Updating the Dose in a Patient's Lifetime
Additional Diseases or Complications
Adaptation Techniques for Therapy Support
Summary
References
Prototype–Based Classification in Unbalanced Biomedical Problems
Introduction
Classification in Unbalanced Problems
Case-Based Classifiers
The Classification Rule
Prototype Selection by Chang’s Algorithm
Feature-Subset Selection and Feature Weighting
Classifier Construction and Evaluation
Datasets and Methods for Comparison
Results
Discussion
Future Work and Conclusions
References
Case–Based Ranking for Environmental Risk Assessment
Introduction
Environmental Risk Assessment
Case-Based Ranking
Learning algorithm
A First Case Study
Distributed Case–Based Ranking
A Second Case Study
Related Works
Conclusions
References
CookIIS – A Successful Recipe Advisor and Menu Creator
Introduction
Computer Cooking Contest
First Computer Cooking Contest in 2008
Second Computer Cooking Contest in 2009
Information Access Suite
System Architecture
Model Manager
Workflow Organisation
Similarity Assessment
Retrieval
Rule Engine
The CookIIS Project
Requirements of the Computer Cooking Contest
Case Representation
Type of Meal and Type of Cuisine
Similarity Assessment
Modelling Dietary Practices
Three-Course Menu Creator
Adaptation in CookIIS
Three Kinds of Adaptation
Model-based Adaptation
Community-Based Adaptation
Evaluation of the Results of the Community-Based Suggestions
In-Place Adaptation on Recipes (for Adaptation Challenge)
User Interface and Feedback
Conclusion, Related Work and Outlook
References
Author Index