Case-Based Reasoning: Concepts, Features and Soft Computing

Shiu, Simon; Pal, Sankar K.

doi:10.1023/b:apin.0000043556.29968.81

Cited by 62 publications

(33 citation statements)

References 5 publications

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“…A usual approach is the incorporation of fuzzy logic into a CBR system in order to improve CBR aspects [4], [29], [35], [9]. Such combinations have been vastly explored as imprecision and uncertainty are inherent in various CBR tasks.…”

Section: Combinations Of Cbr With Fuzzy Logicmentioning

confidence: 99%

“…Fuzzy logic may be also proved very useful in indexing and retrieval. Fuzzy indexing enables multiple indexing of a case on a single feature with different degrees of membership [35]. Fuzzy similarity assessment and matching methods can produce more accurate results.…”

Section: Combinations Of Cbr With Fuzzy Logicmentioning

confidence: 99%

“…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%

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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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Section: Combinations Of Cbr With Fuzzy Logicmentioning

confidence: 99%

Section: Combinations Of Cbr With Fuzzy Logicmentioning

confidence: 99%

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Combinations of case-based reasoning with other intelligent methods

Prentzas

Hatzilygeroudis

2009

HIS

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“…The underlying mechanism of our model is a CaseBased-Reasoning (CBR) [2,34,23] model, in which by means of observing past sporting events, it can predict future odd movements for an unknown sporting event and therefore, drawing a odds evolution over time. To apply this model, we provide an agent with trading capabilities called the CBR-Trader agent (CBR-Tagent).…”

Section: Introductionmentioning

confidence: 99%

Using a case-based reasoning approach for trading in sports betting markets

Alberola

García-Fornes

2012

Appl Intell

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The sports betting market has emerged as one of the most lucrative markets in recent years. Trading in sports betting markets entails predicting odd movements in order to bet on an outcome, whilst also betting on the opposite outcome, at different odds in order to make a profit, regardless of the final result. These markets are mainly composed by humans, which take decisions according to their past experience in these markets. However, human rational reasoning is limited when taking quick decisions, being influenced by emotional factors and offering limited calibration capabilities for estimating probabilities. In this paper, we show how artificial techniques could be applied to this field and demnostrate that they can outperform even the bevahior of high-experienced humans. To achieve this goal, we propose a case-based reasoning model for trading in sports betting markets, which is integrated in an agent to provide it with the capabilities to take trading decisions based on future odd predictions. In order to test the performance of the system, we compare trading decisions taken by the agent with trading decisions taken by human traders when they compete in real sports betting markets.

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“…To overcome the shortcomings of rule-based reasoning systems, we have started to look into applying CaseBased Reasoning (CBR) [28] [29] and agent technologies to ship collision avoidance [15,17,18]. CBR is one of the reasoning paradigms and is a feasible and effective solution to problems which are difficult to be solved with traditional methods such as model-based reasoning.…”

Section: Introductionmentioning

confidence: 99%

Case learning for CBR-based collision avoidance systems

Liu

Yang

et al. 2010

Appl Intell

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Abstract.With the rapid development of case-based reasoning (CBR) techniques, CBR has been widely applied to real-world applications such as collision avoidance systems. A successful CBR-based system relies on a high-quality case base, and a case creation technique for generating such a case base is highly required. In this paper, we propose an automated case learning method for CBR-based collision avoidance systems. Building on techniques from CBR and natural language processing, we developed a methodology for learning cases from maritime affair records. After giving an overview on the developed systems, we present the methodology and the experiments conducted in case creation and case evaluation. The experimental results demonstrated the usefulness and applicability of the case learning approach for generating cases from the historic maritime affair records.

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Case-Based Reasoning: Concepts, Features and Soft Computing

Cited by 62 publications

References 5 publications

Combinations of case-based reasoning with other intelligent methods

Combinations of case-based reasoning with other intelligent methods

Using a case-based reasoning approach for trading in sports betting markets

Case learning for CBR-based collision avoidance systems

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