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Learning to improve case adaptation by introspective reasoning and CBR [chapter]

David B. Leake, Andrew Kinley, David Wilson
1995 Lecture Notes in Computer Science  
The method uses reasoning from scratch, based on introspective reasoning about the requirements for successful adaptation, to build up a library of adaptation cases that are stored for future reuse.  ...  In current CBR systems, case adaptation is usually performed by rule-based methods that use task-speci c rules hand-coded by the system developer.  ...  From Rule-Based Adaptation to CBR After an adaptation problem has been solved by reasoning from scratch, a natural question is how to learn from that reasoning.  ... 
doi:10.1007/3-540-60598-3_21 fatcat:ysrolg6fc5eptpo2sfdjyz2yte

Using Introspective Reasoning to Refine Indexing

Susan Fox, David B. Leake
1995 International Joint Conference on Artificial Intelligence  
Introspective reasoning about a system's own reasoning processes can form the basis for learning to refine those reasoning processes.  ...  The ROBBIE1 system uses introspective reasoning to monitor the retrieval process of a case-based planner to detect retrieval of inappropriate cases.  ...  Applying introspective reasoning to CBR is novel in that learning in CBR systems is generally focused on acquiring domain knowledge, by acquiring new cases.  ... 
dblp:conf/ijcai/FoxL95 fatcat:pb2s7hqzajcqlhi2ritjevomta

Case Based Reasoning as a Model for Cognitive Artificial Intelligence [chapter]

Susan Craw, Agnar Aamodt
2018 Lecture Notes in Computer Science  
Case Based Reasoning (CBR) systems naturally capture knowledge as experiences in memory and they are able to learn new experiences to retain in their memory.  ...  Here we explore cognition and meta-cognition for CBR through self-reflection and introspection of both memory and retrieve and reuse reasoning.  ...  Agnar Aamodt wishes to thank Enric Plaza for discussions of CBR, analogy, and cognition relevant to this paper, in preparing an invited talk at ICCBR 2017.  ... 
doi:10.1007/978-3-030-01081-2_5 fatcat:spwkpikuhbh7pnihfuswp5v77a

Dynamic Refinement of Feature Weights Using Quantitative Introspective Learning

Zhong Zhang, Qiang Yang
1999 International Joint Conference on Artificial Intelligence  
In this paper, we introduce a quantitative introspective learning paradigm into case-based reasoning (CBR).  ...  In such a system, while the reasoning part is still case-based, the learning part is shouldered by a quantitative introspective learning model.  ...  Its goal is to improve reasoning process when encountering failures in its reasoning.  ... 
dblp:conf/ijcai/ZhangY99 fatcat:xap7bbqrcfceha5nkenllergzq

Improving Transfer Learning by Introspective Reasoner [chapter]

Zhongzhi Shi, Bo Zhang, Fuzhen Zhuang
2012 IFIP Advances in Information and Communication Technology  
Introspective learning exploits explicit representations of its own organization and desired behavior to determine when, what, and how to learn in order to improve its own reasoning.  ...  In this paper we have proposed an introspective reasoner to overcome the negative transfer learning.  ...  This work is supported by Key projects of National Natural Science Foundation of China (No. 61035003, 60933004) , National Natural Science Foundation of China (No. 61072085, 60970088, 60903141) , National  ... 
doi:10.1007/978-3-642-32891-6_7 fatcat:nvwwpedutrevlda7l5zbzzon6a

Engineering and Learning of Adaptation Knowledge in Case-Based Reasoning [chapter]

Amélie Cordier, Béatrice Fuchs, Alain Mille
2006 Lecture Notes in Computer Science  
Case-based reasoning (CBR) uses various knowledge containers for problem solving: cases, domain, similarity, and adaptation knowledge.  ...  We focus on adaptation and similarity knowledge containers that are of first importance, difficult to acquire and to model at the design stage.  ...  Acknowledgements The authors would like to thank the referees whose remarks and comments were very helpful to improve this paper.  ... 
doi:10.1007/11891451_27 fatcat:pfnawgctkbhvjgxhtmhjtdk7gm

Experience, introspection and expertise: Learning to refine the case-based reasoning process

David B. Leake
1996 Journal of experimental and theoretical artificial intelligence (Print)  
To support that view and to illustrate the practicality of learning to re ne case-based reasoning, this article presents ongoing research into using introspective reasoning about the case-based reasoning  ...  process to increase expertise at retrieving and adapting stored cases. i  ...  Without introspective learning of new retrieval criteria, 25% of the plans retrieved could not be adapted by its limited adaptation component with learning of retrieval criteria to improve the appropriateness  ... 
doi:10.1080/095281396147357 fatcat:3bjdryyxpbantdp3wlcx4nm5fu

Learning adaptation knowledge to improve case-based reasoning

Susan Craw, Nirmalie Wiratunga, Ray C. Rowe
2006 Artificial Intelligence  
Case-Based Reasoning systems retrieve and reuse solutions for previously solved problems that have been encountered and remembered as cases.  ...  This paper describes an introspective learning approach where the case knowledge itself provides a source from which training data for the adaptation task can be assembled.  ...  Jacek Jarmulak also contributed to the earlier retrieval knowledge phase of this research, and its underpinning of the project's subsequent approach to learning adaptation knowledge.  ... 
doi:10.1016/j.artint.2006.09.001 fatcat:wbrc6eomhnckne763lduoeks24

Improving a Distributed Case-Based Reasoning System Using Introspective Learning [chapter]

Ian Watson, Dan Gardingen
2000 Research and Development in Intelligent Systems XVI  
This paper describes the improvements to a fielded case-based reasoning (CBR) system. The system, called "Really Cool Air" is a distributed application that supports engineering sales staff.  ...  most crucially the automatic adjustments of query relaxation parameters by an incremental learning algorithm.  ...  For example, CBR systems may learn to modify feature weights, adaptation rules [Leake, et al., 1995; Hanney & Keane, 1996] , or even learn to forget redundant cases .  ... 
doi:10.1007/978-1-4471-0745-3_12 fatcat:pvbzthlrdzdrjhbgmpdwhqcy6a

Research of Hospital Knowledge Management Based on Case-Based Reasoning Technology

Hui LI, Wei-Shu MA, Yi XIN, Yan-Li LU
2017 DEStech Transactions on Social Science Education and Human Science  
CBR by some examples.  ...  This article gives detailed descriptions on Case-Based Reasoning (CBR) technology, analyze on its realization of knowledge management in health care and clarify the knowledge management process based on  ...  CBR adopts incremental learning method-new solution and new case are stored for future use-learning ability has been continuously improved and then knowledge and experience has been increased.  KM in  ... 
doi:10.12783/dtssehs/icss2016/9163 fatcat:qjn4vqev7naqvhg25tpvkpmd54

A Distributed Case-Based Reasoning Application for Engineering Sales Support

Ian D. Watson, Dan Gardingen
1999 International Joint Conference on Artificial Intelligence  
techniques using introspective reasoning to improve retrieval efficiency.  ...  The paper describes the distributed architecture of the application, the two case retrieval techniques used, its implementation, trial, roll-out and subsequent improvements to its architecture and retrieval  ...  This is an approach in CBR where the reasoning system itself learns over time to modify its internal representation to improve its performance [Markovitch & Scott, 1993] .  ... 
dblp:conf/ijcai/WatsonG99 fatcat:c2i2sa25eneyddclihnu65rv2q

The Utility Problem for Lazy Learners - Towards a Non-eager Approach [chapter]

Tor Gunnar Houeland, Agnar Aamodt
2010 Lecture Notes in Computer Science  
The two primary approaches to handling the utility problem are through efficient indexing and by reducing the number of cases during case base maintenance.  ...  The utility problem occurs when the performance of learning systems degrade instead of improve when additional knowledge is added.  ...  This work is situated within our research on a new architecture for meta-level reasoning and introspective learning [10] .  ... 
doi:10.1007/978-3-642-14274-1_12 fatcat:efrgxyniofcf3pmnpp4m45u6ke

A comparative utility analysis of case-based reasoning and control-rule learning systems [chapter]

Anthony G. Francis, Ashwin Ram
1995 Lecture Notes in Computer Science  
We present models of case-based reasoning and control-rule learning systems and compare their performance with respect to the swamping utility problem.  ...  Our analysis suggests that case-based reasoning systems are more resistant to the utility problem than control-rule learning systems. 1  ...  Retrieval in Case-Based Reasoners To analyze utility effects in case-based reasoning systems, we need to measure the performance of a CBR system as it learns.  ... 
doi:10.1007/3-540-59286-5_54 fatcat:fzs4fkcm55ei7cp6zf6u5jmwdu

Distributed case-based reasoning

ENRIC PLAZA, LORRAINE MCGINTY
2005 Knowledge engineering review (Print)  
Distribution of resources within case-based reasoning (CBR) architectures is beneficial in a variety of application contexts.  ...  This article briefly discusses some of the approaches that fall under the heading of distributed CBR, and their general impact.  ...  The authors also describe how system efficiency can be further improved by making some adjustments to its architecture and retrieval techniques using introspective reasoning (Watson & Gardingen, 1999b  ... 
doi:10.1017/s0269888906000683 fatcat:iileuztvujbkllzqobrmqhwb4y

Using Meta-reasoning to Improve the Performance of Case-Based Planning [chapter]

Manish Mehta, Santiago Ontañón, Ashwin Ram
2009 Lecture Notes in Computer Science  
The evaluation of Meta-Darmok shows that the system successfully adapts itself and its performance improves through appropriate revision of the case base.  ...  Our approach uses failure patterns to detect anomalous behaviors, and it can learn from experience which of the failures detected are important enough to be fixed.  ...  Previous research on introspective CBR (See Section 2) has shown that meta-reasoning can enable a CBR system to learn by refining its own reasoning process.  ... 
doi:10.1007/978-3-642-02998-1_16 fatcat:ituh2obmfrabvnnswbgrjqssre
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