Case-Based Student Modeling Using Concept Maps

Sørmo, Frode

doi:10.1007/11536406_38

Cited by 5 publications

(3 citation statements)

References 5 publications

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“…Kolodner's (1997) more recent work takes this approach, as does our own (Sørmo and Aamodt, 2002;Sørmo, 2005). As mentioned in Section 5, there are knowledge-light techniques that do produce generalizations that may be useful for learning in humans e.g.…”

Section: Connecting Cases To General Knowledge In Tutoringmentioning

confidence: 94%

Explanation in Case-Based Reasoning–Perspectives and Goals

2005

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We present an overview of different theories of explanation from the philosophy and cognitive science communities. Based on these theories, as well as models of explanation from the knowledge-based systems area, we present a framework for explanation in case-based reasoning (CBR) based on explanation goals. We propose ways that the goals of the user and system designer should be taken into account when deciding what is a good explanation for a given CBR system. Some general types of goals relevant to many CBR systems are identified, and used to survey existing methods of explanation in CBR. Finally, we identify some future challenges.

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Section: Connecting Cases To General Knowledge In Tutoringmentioning

confidence: 94%

Explanation in Case-Based Reasoning–Perspectives and Goals

2005

Self Cite

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“…The semantic network knowledge representation is partly developed on the basis of the CreekL representation language with some modifications and extension. CreekL is a frame-based language with some implemented basic inference methods, such as frame-matching, default inheritance, spreading activation, and constraint satisfaction [24,25].…”

Section: Representing Knowledge With the Semantic Networkmentioning

confidence: 99%

Fault diagnosis under uncertain situations within a Bayesian knowledge-intensive CBR system

Nikpour

Aamodt

2021

Prog Artif Intell

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This paper presents fault diagnosis and problem solving under uncertainty by a Bayesian supported knowledge-intensive case-based reasoning (CBR) system called BNCreek. In this system, the main goal is to diagnose the causal failures behind the symptoms in complex and uncertain domains. The system’s architecture is described in three aspects: the general, structural, and functional architectures. The domain knowledge is represented by formally defined methods. An integration of semantic networks, Bayesian networks, and CBR is employed to deal with the domain uncertainty. An experiment is conducted from the oil well drilling domain, which is a complex and uncertain area as an application domain. The system is evaluated against the expert estimations to find the most efficient solutions for the problems. The obtained results reveal the capability of the system in diagnosing causal failures.

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“…Tutoring Systems: This cluster is evident in the combined view but not in the cocitation view (see figure 6) because the three most prominent papers in the cluster do not show up in the cocitation structure (Sørmo 2005, Seitz 1999, Gómez‐Martín et al 2005). It is not surprising that the papers by Gómez‐Martín et al and Sørmo do not show up in the cocitation structure, as they are recent papers.…”

Section: Research Themesmentioning

confidence: 99%

An Analysis of Current Trends in CBR Research Using Multiview Clustering

et al. 2010

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The European Conference on Case-Based Reasoning (CBR) in 2008 marked 15 years of international and European CBR conferences where almost seven hundred research papers were published. In this report we review the research themes covered in these papers and identify the topics that are active at the moment. The main mechanism for this analysis is a clustering of the research papers based on both co-citation links and text similarity. It is interesting to note that the core set of papers has attracted citations from almost three thousand papers outside the conference collection so it is clear that the CBR conferences are a sub-part of a much larger whole. It is remarkable that the research themes revealed by this analysis do not map directly to the sub-topics of CBR that might appear in a textbook. Instead they reflect the applications-oriented focus of CBR research, and cover the promising application areas and research challenges that are faced.

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Case-Based Student Modeling Using Concept Maps

Cited by 5 publications

References 5 publications

Explanation in Case-Based Reasoning–Perspectives and Goals

Explanation in Case-Based Reasoning–Perspectives and Goals

Fault diagnosis under uncertain situations within a Bayesian knowledge-intensive CBR system

An Analysis of Current Trends in CBR Research Using Multiview Clustering

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