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Fraud in mobile telecommunications is a complex and dynamic problem for Telecom operators. These companies have developed and are exploring new ways of making the fraud detection process more efficient. Most of these attempts are based in fraud management systems, capable of detecting fraudulent communications. In this paper, we present a case-based reasoning system that aids fraud analysts in the...
A new predicting system is presented in which the aim is to forecast the presence or not of oil slicks in a certain area of the open sea after an oil spill. In this case, the CBR methodology has been chosen to solve the problem. The system designed to predict the presence of oil slicks wraps other artificial intelligence techniques such as a Growing Radial Basis Function Networks, Growing Cell Structures...
This paper focuses on the application of CBR to soil analysis from chromatograms. The shape, size and colour of the chromatogram image are hypothesized to contain important information of the mineral content in the soil. Since chromotogram preparation is cheaper than chemical analysis the goal is to predict the nutrients from the chromatogram image features in the future rather than by direct chemical...
In this paper we present a case–based troubleshooting tool developed in the context of the SMMART project. The application aims at the identification and solution of failures in trucks, exploiting a hybrid approach based on the integration of CBR, model based reasoning and fault trees. The case–based module of the final system allows to identify the most probable part of the truck that is responsible...
Although Case-Based Reasoning (CBR) claims to reduce the effort required for developing knowledge-based systems substantially compared with more traditional Artificial Intelligence approaches, the implementation of a CBR application from scratch is still a time consuming task. In this paper we present a novel, freely available tool for rapid prototyping of CBR applications that focuses on the similarity-based...
Biomedical domains have been an application domain of choice for artificial intelligence (AI) since its pioneering years in expert systems. Some simple explanations to this phenomenon are the intellectual complexity presented by this domain, as well as the dominant industry market share of healthcare. Following in AI’s tracks, case-based reasoning (CBR) has been abundantly applied to the health sciences...
After fifteen years of CBR conferences, this paper sets out to examine the themes that have evolved in CBR research as revealed by the implicit and explicit relationships between the conference papers. We have examined a number of metrics for demonstrating connections between papers and between authors and have found that a clustering based on co-citation of papers appears to produce the most meaningful...
The Web is a vibrant environment for innovation in computer science, AI, and social interaction; these innovations come in such great number and speed that it is unlikely to follow them. This paper will focus on some emerging aspects on the web that are an opportunity and challenge for Case-based Reasoning, specifically the large amount of experiences that individual people share in the Web. The talk...
This paper presents CBRetaliate, an agent that combines Case-Based Reasoning (CBR) and Reinforcement Learning (RL) algorithms. Unlike most previous work where RL is used to improve accuracy in the action selection process, CBRetaliate uses CBR to allow RL to respond more quickly to changing conditions. CBRetaliate combines two key features: it uses a time window to compute similarity and stores and...
In previous work, Bogaerts and Leake [1,2] introduced the rank quality measure for the evaluation of conversational case-based reasoning (CCBR) systems. Rank quality assesses how well a system copes with the limited problem information available in an ongoing dialog, giving useful evaluation information not readily available from standard precision and efficiency measures. However, that work also...
Despite the success of modern Web search engines, challenges remain when it comes to providing people with access to the right information at the right time. In this paper, we describe how a novel combination of case-based reasoning, Web search, and peer-to-peer networking can be used to develop a platform for personalized Web search. This novel approach benefits from better result quality and improved...
This paper deals with two relatively less well studied problems in Textual CBR, namely visualizing and evaluating complexity of textual case bases. The first is useful in case base maintenance, the second in making informed choices regarding case base representation and tuning of parameters for the TCBR system, and also for explaining the behaviour of different retrieval/classification techniques...
The performance of a case-based reasoning system often depends on the suitability of an underlying similarity (distance) measure, and specifying such a measure by hand can be very difficult. In this paper, we therefore develop a machine learning approach to similarity assessment. More precisely, we propose a method that learns how to combine given local similarity measures into a global one. As training...
Conservative adaptation consists in a minimal change on a source case to be consistent with the target case, given the domain knowledge. It has been formalised in a previous work thanks to the AGMtheory of belief revision applied to propositional logic. However, this formalism is rarely used in case-based reasoning systems. In this paper, conservative adaptation is extended to a more general representation...
A case-based reasoning system relies on different knowledge containers, including cases and adaptation knowledge. The knowledge acquisition that aims at enriching these containers for the purpose of improving the accuracy of the CBR inference may take place during design, maintenance, and also on-line, during the use of the system. This paper describes IakA, an approach to on-line acquisition of cases...
“Similar problems have similar solutions” is a basic tenet of case-based inference. However this is not satisfied for CBR systems where the task is to achieve original solutions — i.e. solutions that, even for “old problems,” are required to be noticeably different from previously known solutions. This paper analyzes the role of reuse in CBR systems in originality driven tasks (ODT), where a new solution...
Artificial intelligence in games is usually used for creating player’s opponents. Manual edition of intelligent behaviors for Non-Player Characters (NPC) of games is a cumbersome task that needs experienced designers. Amongst other activities, they design new behaviors in terms of perception and actuation over the environment. Behaviors typically use recurring patterns, so that experience and reuse...
Case-base reasoning in a real-time context requires the system to output the solution to a given problem in a predictable and usually very fast time frame. As the number of cases that can be processed is limited by the real-time constraint, we explore ways of selecting the most important cases and ways of speeding up case comparisons by optimizing the representation of each case. We focus on spatially-aware...
One of the key issues in Case-Based Reasoning (CBR) systems is the efficient retrieval of cases when the case base is huge and/or it contains uncertainty and partial knowledge. We tackle these issues by organizing the case memory using an unsupervised clustering technique to identify data patterns for promoting all CBR steps. Moreover, another useful property of these patterns is that they provide...
Credible case-based inference (CCBI) is a new and theoretically sound inferencing mechanism for case-based systems. In this paper, we formally investigate the level of precision that CCBI-based retrieval results may yield. Building upon our theoretical findings, we derive a number of optimization criteria that can be utilized for learning such similarity measures that bring about more precise predictions...
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