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Case-based reasoning foundations
The Knowledge Engineering Review, 2005A 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.
Michael M. Richter, Agnar Aamodt
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Holographic Case-Based Reasoning
2020In this paper, we present a novel extension of CBR that allows cases to be more proactive at problem solving, by enriching case representations and facilitating richer interconnectedness between cases. We empirically study the improvements resulting from a holographic realization on experimental datasets.
Devi Ganesan, Sutanu Chakraborti
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Cooperative Case-based Reasoning
1997We are investigating possible modes of cooperation among homogeneous agents with learning capabilities. In this paper we will be focused on agents that learn and solve problems using Case-based Reasoning (CBR), and we will present two modes of cooperation among them: Distributed Case-based Reasoning (DistCBR) and Collective Case-based Reasoning (ColCBR)
Enric Plaza +2 more
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Integrations with case-based reasoning
The Knowledge Engineering Review, 2005This commentary succinctly summarizes work in integrating case-based reasoning (CBR) with other reasoning modalities. Including CBR in mixed mode approaches promotes synergies and benefits beyond those achievable using CBR or other individual reasoning approaches alone.
Marling, C, Rissland, E, Aamodt, A
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The Knowledge Engineering Review, 2005
This commentary provides a definition of textual case-based reasoning (TCBR) and surveys research contributions according to four research questions. We also describe how TCBR can be distinguished from text mining and information retrieval. We conclude with potential directions for TCBR research.
Rosina O. Weber +2 more
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This commentary provides a definition of textual case-based reasoning (TCBR) and surveys research contributions according to four research questions. We also describe how TCBR can be distinguished from text mining and information retrieval. We conclude with potential directions for TCBR research.
Rosina O. Weber +2 more
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2001
This chapter contains an overview of Case-Based Reasoning (CBR). The main goal is to have a balance between brevity and expressiveness and to provide helpful pointers to literature in the field. To do so, we first describe the CBR types and the CBR cycle, then we briefly review a representative set of systems, next we discuss the connections between ...
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This chapter contains an overview of Case-Based Reasoning (CBR). The main goal is to have a balance between brevity and expressiveness and to provide helpful pointers to literature in the field. To do so, we first describe the CBR types and the CBR cycle, then we briefly review a representative set of systems, next we discuss the connections between ...
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The Knowledge Engineering Review, 1994
Abstract This paper reviews a number of hybrid Case-Based Reasoning (CBR) systems. These systems are hybrid because the CBR components cooperate with one or more “co-reasoners” which employ a different type of reasoning strategy (e.g. qualitative simulation, constraint satisfaction, etc.). In this paper, we propose that CBR is in fact
John Hunt, Roger Miles
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Abstract This paper reviews a number of hybrid Case-Based Reasoning (CBR) systems. These systems are hybrid because the CBR components cooperate with one or more “co-reasoners” which employ a different type of reasoning strategy (e.g. qualitative simulation, constraint satisfaction, etc.). In this paper, we propose that CBR is in fact
John Hunt, Roger Miles
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Case-Based Reasoning with Confidence
2000A case-based reasoning system can produce both a solution and an estimate of the confidence in that solution. The confidence value can be used to determine whether the solution does or does not have the needed accuracy. A statistical method can be used to compute a confidence value from information generated during the case-based reasoning process ...
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Reformulation in case-based reasoning
1998By generalising our common experience, this paper addresses case-based reasoning that employs reformulations. Reformulation is useful when standard mapping is insufficient to retrieve a case. The paper provides a definition of reformulation and shows how reformulation is linked to retrieval and adaptation in the case-based reasoning cycle.
Erica Melis, Jean Lieber, Amedeo Napoli
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CASE-BASED-REASONING FOR IMAGE SEGMENTATION
International Journal of Pattern Recognition and Artificial Intelligence, 2008This paper proposes to use case-based-reasoning for grey-level image segmentation. Different approaches to image segmentation have been proposed in the literature. The selection of the segmentation approach and the assignment of the values to the parameters involved in the selected algorithm depend on image domain and on the specific application.
Maria Frucci +2 more
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