Case-Based Reasoning

Mántaras, Ramon López de

doi:10.1007/3-540-44673-7_6

Cited by 31 publications

(15 citation statements)

References 66 publications

(72 reference statements)

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“…CBR is a field of AI that uses past experiences to solve new problems, assuming that similar problems have similar solutions [Aamodt and Plaza 1994;Leake 1996;De Mántaras 2001]. CBR systems have at their disposal a set L of previously solved problems constituting the experience of the system, called case base.…”

Section: Case-based Methodologymentioning

confidence: 99%

Progress in Case-Based Planning

2015

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Case-based planning (CBP) is an approach to automated planning that tries to save computational effort by reusing previously found solutions. In 2001, Spalazzi published a survey of work in CBP; here, we present an updated overview of systems that have contributed to the evolution of the field or addressed some issues related to planning by reuse in a novel way. The article presents relevant planners so that readers gain insight into the operation of these systems. This analysis will allow readers to understand the approaches both in the quality of the solutions and in the complexity of finding them.

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Section: Case-based Methodologymentioning

confidence: 99%

Progress in Case-Based Planning

2015

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“…It is based on the following sentence, also known as the CBR assumption, "Similar problems have similar solutions". A Case-based reasoner solves new problems by adapting solutions that have been successfully used in the past to the new situation [36].…”

Section: Case-based Reasoningmentioning

confidence: 99%

A Hybrid Recommender System to Improve Circular Economy in Industrial Symbiotic Networks

2019

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Recently, the need of improved resource trading has arisen due to resource limitations and energy optimization problems. Various platforms supporting resource exchange and waste reuse in industrial symbiotic networks are being developed. However, the actors participating in these networks still mainly act based on predefined patterns, without taking the possible alternatives into account, usually due to the difficulty of properly evaluating them. Therefore, incorporating intelligence into the platforms that these networks use, supporting the involved actors to automatically find resources able to cover their needs, is still of high importance both for the companies and the whole ecosystem. In this work, we present a hybrid recommender system to support users in properly identifying the symbiotic relationships that might provide them an improved performance. This recommender combines a graph-based model for resource similarities, while it follows the basic case-based reasoning processes to generate resource recommendations. Several criteria, apart from resource similarity, are taken into account to generate, each time, the list of the most suitable solutions. As highlighted through a use case scenario, the proposed system could play a key role in the emerging industrial symbiotic platforms, as the majority of them still do not incorporate automatic decision support mechanisms.

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“…CBR-BN algorithm consists in an hybrid approach based on the combination of Bayesian Networks and Case-Based Reasoning techniques [3]. In order to optimize the diagnosis process, the algorithm considers only a subset of the Bayesian Network structure (called problem case), which can potentially be the root cause, and ignores the rest of BN nodes.…”

Section: A Intra-cluster Inference Using Cbr-bnmentioning

confidence: 99%

Scalable and fast root cause analysis using inter cluster inference

Bennacer¹,

Ciavaglia²,

Ghamri-Doudane³

et al. 2013

2013 IEEE International Conference on Communications (ICC)

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The capability to diagnose the root cause of an observed problem precisely and quickly is a desirable feature for large communication networks. However, the design of a technique that is at the same time fast, scalable and accurate is a challenging task. In this paper, we propose a novel method based on inter-cluster inference to overcome the usual limits of fault diagnosis techniques. The approach is based on two important concepts: a cluster decomposition of the dependency graph in order to ensure scalability, and the introduction of duplicated nodes aiming at preserving the end-to-end network view. The evaluation of the proposed approach has demonstrated a significant reduction in the complexity and the computation time of the root cause analysis, since it is based on a set of small-scale dependency graphs.

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Case-Based Reasoning

Cited by 31 publications

References 66 publications

Progress in Case-Based Planning

Progress in Case-Based Planning

A Hybrid Recommender System to Improve Circular Economy in Industrial Symbiotic Networks

Scalable and fast root cause analysis using inter cluster inference

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