Case-Based Reasoning System and Artificial Neural Networks: A Review

Chen, Daqing; Burrell, Phillip

doi:10.1007/pl00009897

Cited by 42 publications

(16 citation statements)

References 28 publications

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“…Casebased reasoning is an alternative to expert systems that are based on rule-based reasoning. Case-based reasoning is based on experience or memory (Chen and Burrell, 2001), whereby a case-based reasoner solves new problems by adopting solutions that were used to solve old problems (Riesbeck and Schank, 1989). Problem solving case-based reasoning is useful for a wide variety of problem solving tasks, including planning, diagnosis, and design (Kolodner, 1992).…”

Section: Preliminary Studymentioning

confidence: 99%

Automatic Rebar Estimation Algorithms for Integrated Project Delivery

Lim

Hong

Lee

et al. 2016

Journal of Asian Architecture and Building Engineering

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Integrated project delivery is advantageous in that it can reflect the constructor's expertise at the design phase. Furthermore, integrated project delivery allows project stockholders to promptly evaluate the financial performance of design decisions. However, there are many problems among existing quantity estimation processes, including human error, loss of information during data exchange and import-export, and time delays. These problems are major obstructions to the application of integrated project delivery. In particular, when it comes to rebar in structural works, errors generated during the drafting process of structural design information have a direct impact on estimation and construction. Such errors can be resolved by employing automatic quantity estimation software that uses the structural design information. In this regard, the present study proposes an automatic rebar estimation algorithm for use in integrated project delivery, the purpose of which is to further develop the software necessary for integrated project delivery. Continued development of additional algorithms for other types of resources as well as software capable of integrating these tools will lead to excellent decision-making support tools for project stockholders, including architectural designers.

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Section: Preliminary Studymentioning

confidence: 99%

Automatic Rebar Estimation Algorithms for Integrated Project Delivery

Lim

Hong

Lee

et al. 2016

Journal of Asian Architecture and Building Engineering

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“…In this way, appealing characteristics of neural networks such as parallelism, robustness, adaptability, generalization and ability to cope with incomplete input data are exploited [10], [35]. Due to the fact that different types of neural networks have been developed (e.g.…”

Section: Combinations Of Cbr With Neural Networkmentioning

confidence: 99%

“…[12], [34]). Knowledge extracted from neural networks could also be exploited by CBR [10], [35]. An interesting direction could involve non-embedded coupling approaches combining CBR with neural networks.…”

Section: Combinations Of Cbr With Neural Networkmentioning

confidence: 99%

Combinations of case-based reasoning with other intelligent methods

Prentzas

Hatzilygeroudis

2009

HIS

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Abstract. Case-based reasoning is a popular approach used in intelligent systems. Whenever a new case has to be dealt with, the most similar cases are retrieved from the case base and their encompassed knowledge is exploited in the current situation. Combinations of case-based reasoning with other intelligent methods have been explored deriving effective knowledge representation schemes. Although some types of combinations have been mostly explored, other types have not been thoroughly investigated. In this paper, we briefly outline popular case-based reasoning combinations. More specifically, we focus on combinations of case-based reasoning with rulebased reasoning, soft computing and ontologies. We illustrate basic types of such combinations and discuss future directions.

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“…Case-based reasoning has been combined with value function approximation algorithms such as reinforcement learning [16,17,18,19] and neural networks [20,21]. The particular value function approximation algorithm developed in PITS++ has been shown to be particularly useful for spatial event prediction and, therefore, a suitable base line to measure performance gains by using case-based reasoning techniques.…”

Section: Related Workmentioning

confidence: 99%

Spatial Event Prediction by Combining Value Function Approximation and Case-Based Reasoning

Li¹,

Muñoz-Ávila

Bramsen³

et al. 2009

Case-Based Reasoning Research and Development

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Abstract. This paper presents a new approach for spatial event prediction that combines a value function approximation algorithm and case-based reasoning predictors. Each of these predictors makes unique contributions to the overall spatial event prediction. The function value approximation prediction is particularly suitable to reasoning with geographical features such as the (x,y) coordinates of an event. The case-based prediction is particularly well suited to deal with non-geographical features such as the time of the event or income level of the population. We claim that the combination of these two predictors results in a significant improvement of the accuracy in the spatial event prediction compared to pure geographically-based predictions. We support our claim by reporting on an ablation study for the prediction of improvised explosive device (IED) attacks.

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Case-Based Reasoning System and Artificial Neural Networks: A Review

Cited by 42 publications

References 28 publications

Automatic Rebar Estimation Algorithms for Integrated Project Delivery

Automatic Rebar Estimation Algorithms for Integrated Project Delivery

Combinations of case-based reasoning with other intelligent methods

Spatial Event Prediction by Combining Value Function Approximation and Case-Based Reasoning

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