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Conventional approaches to similarity search and case-based retrieval, such as nearest neighbor search, require the specification of a global similarity measure which is typically expressed as an aggregation of local measures pertaining to different aspects of a case. Since the proper aggregation of local measures is often quite difficult, we propose a novel concept called similarity skyline. Roughly...
Case provenance concerns how cases came into being in a case-based reasoning system. Case provenance information has been proposed as a resource to exploit for tasks such as guiding case-based maintenance and estimating case confidence [1]. The paper presents a new bidirectional provenance-based method for propagating case confidence, examines when provenance-based maintenance is likely to be useful,...
e-Science brings large-scale computation to bear on scientific problems, often by performing sequences of computational tasks organized into workflows and executed on distributed Web resources. Sophisticated AI tools have been developed to apply knowledge-rich methods to compose scientific workflows by generative planning, but the required knowledge can be difficult to acquire. Current work by the...
How to endow case-based reasoning systems with effective case adaptation capabilities is a classic problem. A significant impediment to developing automated adaptation procedures is the difficulty of acquiring the required knowledge. Initial work on WebAdapt [1] proposed addressing this problem with “just-in-time” knowledge mining from Web sources. This paper addresses two key questions building on...
Estimation by analogy EBA (effort estimation by analogy) is one of the proven methods for effort prediction in software engineering; in AI this would be called Case-Based Reasoning. In this paper we consider effort predictions using the EBA () method AQUA and pay attention to two aspects: (i) The influence of the set of analogs on the quality of prediction. The set of analogs is determined by a learning...
Many medical diagnosis applications are characterized by datasets that contain under-represented classes due to the fact that the disease appears more rarely than the normal case. In such a situation classifiers that generalize over the data such as decision trees and Naïve Bayesian are not the proper choice as classification methods. Case-based classifiers that can work on the samples seen so far...
This paper presents a case-based approach to decision support for diabetes management in patients with Type 1 diabetes on insulin pump therapy. To avoid serious disease complications, including heart attack, blindness and stroke, these patients must continuously monitor their blood glucose levels and keep them as close to normal as possible. Achieving and maintaining good blood glucose control is...
Self-healing and recovery informed by environment knowledge (SHRIEK) is an autonomic computing approach to improving the robustness of computing systems. Case-based reasoning (CBR) is used to guide fault diagnosis and enable learning from experience, and rule-based reasoning to enable fault remediation and recovery informed by environment knowledge. Focusing on the role of conversational CBR (CCBR)...
Case-Based Planning (CBP) is an effective technique for solving planning problems that has the potential to reduce the computational complexity of the generative planning approaches [8,3]. However, the success of plan execution using CBP depends highly on the selection of a correct plan; especially when the case-base of plans is extensive. In this paper we introduce the concept of a situation and...
Finding most similar deductive consequences, MSDC, is a new approach which builds a unified framework to integrate similarity-based and deductive reasoning. In this paper we introduce a new formulation -MSDC(q) of MSDC which is a mixed integer optimization problem. Although mixed integer optimization problems are exponentially solvable in general, our experimental results show that...
Being able to predict the performance of a Case-Based Reasoning system against a set of future problems would provide invaluable information for design and maintenance of the system. Thus, we could carry out the needed design changes and maintenance tasks to improve future performance in a proactive fashion. This paper proposes a novel method for identifying regions in a case base where the system...
There are case-based recommender systems that generate personalized recommendations for users exploiting the knowledge contained in past recommendation cases. These systems assume that the quality of a new recommendation depends on the quality of the recorded recommendation cases. In this paper, we present a case model exploited in a mobile critique-based recommender system that generates recommendations...
The variety in email related tasks, as well as the increase in daily email load, has created a need for automated email management tools. In this paper, we provide an empirical evaluation of representational schemes and retrieval strategies for email. In particular, we study the impact of both textual and non-textual email content for case representation applied to Email task management. Our first...
Case-based reasoning (CBR) solves problems using the already stored knowledge, and captures new knowledge, making it immediately available for solving the next problem. Therefore, CBR can be seen as a method for problem solving, and also as a method to capture new experience and make it immediately available for problem solving. The CBR paradigm has been originally introduced by the cognitive science...
Textual-case based reasoning (TCBR) systems where the problem and solution are in free text form are hard to evaluate. In the absence of class information, domain experts are needed to evaluate solution quality, and provide relevance information. This approach is costly and time consuming. We propose three measures that can be used to compare alternate TCBR system configurations, in the absence of...
Our goal is to support system developers in rapid prototyping of Case-Based Reasoning (CBR) systems through component reuse. In this paper, we propose the idea of templates that can be readily adapted when building a CBR system. We define a case base of templates for case-based recommender systems. We devise a novel case-based template recommender, based on recommender systems research, but using...
To meet time constraints, a CBR system must control the time spent searching in the case base for a solution. In this paper, we presents the results of a case study comparing the proficiency of some criteria for forgetting cases, hence bounding the number of cases to be explored during retrieval. The criteria being considered are case usage, case value and case density. As we make use of a sequential...
Effective encoding of information is one of the keys to qualitative problem solving. Our aim is to explore Knowledge representation techniques that capture meaningful word associations occurring in documents. We have developed iReMedI, a TCBR based problem solving system as a prototype to demonstrate our idea. For representation we have used a combination of NLP and graph based techniques which we...
Adaptation is probably the most difficult task in Case-Based Reasoning (CBR) systems. Most techniques for adaptation propose ad-hoc solutions that require an effort on knowledge acquisition beyond typical CBR standards. In this paper we demonstrate the applicability of domain-independent planning techniques that exploit the knowledge already acquired in many knowledge-rich approaches to CBR...
Experts who narrate their knowledge in case-like form often express significant parts of it in folk arguments ( considerations for and against alternative recommendations where informal judgment is involved. Such arguments do not fit naturally into common frameworks of case-based reasoning. The knowledge they contain may therefore be overlooked despite its value. The paper indicates a mean of helping...
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