172,171 research outputs found

    Cooperation between expert knowledge and data mining discovered knowledge: Lessons learned

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    Expert systems are built from knowledge traditionally elicited from the human expert. It is precisely knowledge elicitation from the expert that is the bottleneck in expert system construction. On the other hand, a data mining system, which automatically extracts knowledge, needs expert guidance on the successive decisions to be made in each of the system phases. In this context, expert knowledge and data mining discovered knowledge can cooperate, maximizing their individual capabilities: data mining discovered knowledge can be used as a complementary source of knowledge for the expert system, whereas expert knowledge can be used to guide the data mining process. This article summarizes different examples of systems where there is cooperation between expert knowledge and data mining discovered knowledge and reports our experience of such cooperation gathered from a medical diagnosis project called Intelligent Interpretation of Isokinetics Data, which we developed. From that experience, a series of lessons were learned throughout project development. Some of these lessons are generally applicable and others pertain exclusively to certain project types

    Using evidence combination for transformer defect diagnosis

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    This paper describes a number of methods of evidence combination, and their applicability to the domain of transformer defect diagnosis. It explains how evidence combination fits into an on-line and implemented agent-based condition monitoring system, and the benefits of giving selected agents reflective abilities. Reflection has not previously been deployed in an industrial setting, and theoretical work has been in domains other than power engineering. This paper presents the results of implementing five different methods of evidence combination, showing that reflective techniques give greater accuracy than non-reflective

    Automated post-fault diagnosis of power system disturbances

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    In order to automate the analysis of SCADA and digital fault recorder (DFR) data for a transmission network operator in the UK, the authors have developed an industrial strength multi-agent system entitled protection engineering diagnostic agents (PEDA). The PEDA system integrates a number of legacy intelligent systems for analyzing power system data as autonomous intelligent agents. The integration achieved through multi-agent systems technology enhances the diagnostic support offered to engineers by focusing the analysis on the most pertinent DFR data based on the results of the analysis of SCADA. Since November 2004 the PEDA system has been operating online at a UK utility. In this paper the authors focus on the underlying intelligent system techniques, i.e. rule-based expert systems, model-based reasoning and state-of-the-art multi-agent system technology, that PEDA employs and the lessons learnt through its deployment and online use

    Synthetic Semiotics: on modelling and simulating the \ud emergence of sign processes

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    Based on formal-theoretical principles about the \ud sign processes involved, we have built synthetic experiments \ud to investigate the emergence of communication based on \ud symbols and indexes in a distributed system of sign users, \ud following theoretical constraints from C.S.Peirce theory of \ud signs, following a Synthetic Semiotics approach. In this paper, we summarize these computational experiments and results regarding associative learning processes of symbolic sign modality and cognitive conditions in an evolutionary process for the emergence of either symbol-based or index-based communication

    Knowledge Search within a Company-WIKI

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    The usage of Wikis for the purpose of knowledge management within a business company is only of value if the stored information can be found easily. The fundamental characteristic of a Wiki, its easy and informal usage, results in large amounts of steadily changing, unstructured documents. The widely used full-text search often provides search results of insufficient accuracy. In this paper, we will present an approach likely to improve search quality, through the use of Semantic Web, Text Mining, and Case Based Reasoning (CBR) technologies. Search results are more precise and complete because, in contrast to full-text search, the proposed knowledge-based search operates on the semantic layer

    Irreducible incoherence and intelligent design : a look into the conceptual toolbox of a pseudoscience

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    The concept of Irreducible Complexity (IC) has played a pivotal role in the resurgence of the creationist movement over the past two decades. Evolutionary biologists and philosophers have unambiguously rejected the purported demonstration of “intelligent design” in nature, but there have been several, apparently contradictory, lines of criticism. We argue that this is in fact due to Michael Behe's own incoherent definition and use of IC. This paper offers an analysis of several equivocations inherent in the concept of Irreducible Complexity and discusses the way in which advocates of the Intelligent Design Creationism (IDC) have conveniently turned IC into a moving target. An analysis of these rhetorical strategies helps us to understand why IC has gained such prominence in the IDC movement, and why, despite its complete lack of scientific merits, it has even convinced some knowledgeable persons of the impending demise of evolutionary theory
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