INRECA: A seamlessly integrated system based on inductive inference and case-based reasoning

Auriol, Eric; Wess, Stefan; Manago, Michel; Althoff, Klaus-Dieter; Traphöner, Ralph

doi:10.1007/3-540-60598-3_33

Cited by 22 publications

(12 citation statements)

References 6 publications

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“…As mentioned, probabilistic tree-type constructs have been purported to provide inductive inference capabilities [11,12]. What is most often the case is that the system performs statistical inference operations (just a computational form of induction).…”

Section: Directive Decision Devices Limitationsmentioning

confidence: 98%

Prospects for the Employment of Directive Decision Devices in Financial Applications

Blue

Andoh‐Baidoo

Sutherland

2006

Proceedings of the 39th Annual Hawaii International Conference on System Sciences (HICSS'06)

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Expert systems, decision support systems, and knowledge management systems, are computer-based constructs intended to somehow assist human users in making decisions. Directive decision devices, on the other hand, are a prospective class designed to function as entirely autonomous decision agents, and so are capable of displacing human decision-makers. The primary purpose of this paper is to explore the prospects transferring the responsibility for handling certain sorts of financial management applications from human functionaries to directive decision devices.

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Section: Directive Decision Devices Limitationsmentioning

confidence: 98%

Prospects for the Employment of Directive Decision Devices in Financial Applications

Blue

Andoh‐Baidoo

Sutherland

2006

Proceedings of the 39th Annual Hawaii International Conference on System Sciences (HICSS'06)

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“…In contrast our approach is grouping states as they are visited, which is the classical lazy learning approach in CBR. However, similar to work integrating lazy and non lazy learning approaches [14], one could use our CBR approach until enough states have been visited and at that point run a clustering algorithm. Other works combine gradient-descent methods with RL [9].…”

Section: Related Workmentioning

confidence: 99%

Reducing the Memory Footprint of Temporal Difference Learning over Finitely Many States by Using Case-Based Generalization

Dilts

Muñoz-Ávila

2010

Case-Based Reasoning. Research and Development

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Abstract. In this paper we present an approach for reducing the memory footprint requirement of temporal difference methods in which the set of states is finite. We use case-based generalization to group the states visited during the reinforcement learning process. We follow a lazy learning approach; cases are grouped in the order in which they are visited. Any new state visited is assigned to an existing entry in the Q-table provided that a similar state has been visited before. Otherwise a new entry is added to the Q-table. We performed experiments on a turn-based game where actions have non-deterministic effects and might have long term repercussions on the outcome of the game. The main conclusion from our experiments is that by using case-based generalization, the size of the Q-table can be substantially reduced while maintaining the quality of the RL estimates.

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“…In the past, some attempts in inductive learning and neural network have been made in applying clustering techniques to case-based reasoning [12]. The basic idea of the inductive methods is to build a classification tree based on analysis of information gain that are associated with each attribute or question used to represent a case [3,4,12]. A drawback of inductive approaches are that they are expensive to run for large case base [4,14].…”

Section: Clustering Techniques In Cbrmentioning

confidence: 99%

Keep It Simple: A Case-Base Maintenance Policy Based on Clustering and Information Theory

Yang

2000

Advances in Artificial Intelligence

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Abstract. Today's case based reasoning applications face several challenges. In a typical application, the case bases grow at a very fast rate and their contents become increasingly diverse, making it necessary to partition a large case base into several smaller ones. Their users are overloaded with vast amounts of information during the retrieval process. These problems call for the development of effective case-base maintenance methods. As a result, many researchers have been driven to design sophisticated case-base structures or maintenance methods. In contrast, we hold a different point of view: we maintain that the structure of a case base should be kept as simple as possible, and that the maintenance method should be as transparent as possible. In this paper we propose a case-base maintenance method that avoids building sophisticated structures around a case base or perform complex operations on a case base. Our method partitions cases into clusters where the cases in the same cluster are more similar than cases in other clusters. In addition to the content of textual cases, the clustering method we propose can also be based on values of attributes that may be attached to the cases. Clusters can be converted to new case bases, which are smaller in size and when stored distributedly, can entail simpler maintenance operations. The contents of the new case bases are more focused and easier to retrieve and update. To support retrieval in this distributed case-base network, we present a method that is based on a decision forest built with the attributes that are obtained through an innovative modification of the ID3 algorithm.

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INRECA: A seamlessly integrated system based on inductive inference and case-based reasoning

Cited by 22 publications

References 6 publications

Prospects for the Employment of Directive Decision Devices in Financial Applications

Prospects for the Employment of Directive Decision Devices in Financial Applications

Reducing the Memory Footprint of Temporal Difference Learning over Finitely Many States by Using Case-Based Generalization

Keep It Simple: A Case-Base Maintenance Policy Based on Clustering and Information Theory

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