CBR and MBR techniques: review for an application in the emergencies domain

The purpose of this document is to provide an in-depth analysis of current reasoning engine practice and the integration strategies of Case Based Reasoning and Model Based Reasoning that will be used in the design and development of the RIMSAT system. RIMSAT (Remote Intelligent Management Support and Training) is a European Commission funded project designed to: a.. Provide an innovative, 'intelligent', knowledge based solution aimed at improving the quality of critical decisions b.. Enhance the competencies and responsiveness of individuals and organisations involved in highly complex, safety critical incidents - irrespective of their location. In other words, RIMSAT aims to design and implement a decision support system that using Case Base Reasoning as well as Model Base Reasoning technology is applied in the management of emergency situations. This document is part of a deliverable for RIMSAT project, and although it has been done in close contact with the requirements of the project, it provides an overview wide enough for providing a state of the art in integration strategies between CBR and MBR technologies.Postprint (published version

Case-Based Decision Support for Disaster Management

Disasters are characterized by severe disruptions of the society’s functionality and adverse impacts on humans, the environment, and economy that cannot be coped with by society using its own resources. This work presents a decision support method that identifies appropriate measures for protecting the public in the course of a nuclear accident. The method particularly considers the issue of uncertainty in decision-making as well as the structured integration of experience and expert knowledge

The development of an expert system for the diagnosis of diseases in fibre and dairy goats

This thesis details the development of an expert system for the diagnosis of diseases in fibre and dairy goats. Divided into five sections, five appendices, and a bibliography, this thesis centres on the methods used to build the expert system: the decisions taken at the outset of, and during the course of, development; some of the problems encountered, and the solutions to those problems. A detailed appraisal is made of the development process and suggestions are made for future developments over similar domains (for example, the diagnosis of diseases in animals other than goats). Much emphasis is placed on three topics in particular: the selection of the expert system tool(s) to be used (and the rejection of numerous others); the methodology employed for this selection process; and the methodology used for the process of development. Other topics which are routinely found in texts on expert systems (for example, knowledge elicitation techniques, explanatory facilities, expert system evaluation etc) are dealt with only briefly. However, for the reader interested in further information on these topics, references are made in the text to appropriate sources

Expert knowledge in geostatistical inference and prediction

Geostatistics provides an efficient tool for mapping environmental variables from observations and layers of explanatory variables. The number and configuration of the observations importantly determine the accuracy of geostatistical inference and prediction. Data collection is costly, and coarse sampling may lead to large uncertainties in interpolated maps. In such case, additional information may be gathered from experts who are knowledgeable about the spatial variability of environmental variables. Statistical expert elicitation has gradually become a mature research field and has proved to be able to extract from experts reliable information to form a sound scientific database. In this thesis, expert knowledge has been elicited and incorporated in geostatistical models for inference and prediction. Various extensions to the expert elicitation literature were required to make it suitable for elicitation of spatial data. The use of expert knowledge in geostatistical research is promising, yet challenging.</p

An object-oriented approach to structuring multicriteria decision support in natural resource management problems

Includes bibliographical references.The undertaking of MCDM (Multicriteria Decision Making) and the development of DSSs (Decision Support Systems) tend to be complex and inefficient, leading to low productivity in decision analysis and DSSs. Towards this end, this study has developed an approach based on object orientation for MCDM and DSS modelling, with the emphasis on natural resource management. The object-oriented approach provides a philosophy to model decision analysis and DSSs in a uniform way, as shown by the diagrams presented in this study. The solving of natural resource management decision problems, the MCDM decision making procedure and decision making activities are modelled in an object-oriented way. The macro decision analysis system, its DSS, the decision problem, the decision context, and the entities in the decision making procedure are represented as "objects". The object-oriented representation of decision analysis also constitutes the basis for the analysis ofDSSs

Intelligent Systems

This book is dedicated to intelligent systems of broad-spectrum application, such as personal and social biosafety or use of intelligent sensory micro-nanosystems such as "e-nose", "e-tongue" and "e-eye". In addition to that, effective acquiring information, knowledge management and improved knowledge transfer in any media, as well as modeling its information content using meta-and hyper heuristics and semantic reasoning all benefit from the systems covered in this book. Intelligent systems can also be applied in education and generating the intelligent distributed eLearning architecture, as well as in a large number of technical fields, such as industrial design, manufacturing and utilization, e.g., in precision agriculture, cartography, electric power distribution systems, intelligent building management systems, drilling operations etc. Furthermore, decision making using fuzzy logic models, computational recognition of comprehension uncertainty and the joint synthesis of goals and means of intelligent behavior biosystems, as well as diagnostic and human support in the healthcare environment have also been made easier

The Cultural Landscape & Heritage Paradox; Protection and Development of the Dutch Archeological-Historical Landscape and its European Dimension

To what extent can we know past and mainly invisible landscapes, and how we can use this still hidden knowledge for actual sustainable management of landscape’s cultural and historical values. It has also been acknowledged that heritage management is increasingly about ‘the management of future change rather than simply protection’. This presents us with a paradox: to preserve our historic environment, we have to collaborate with those who wish to transform it and, in order to apply our expert knowledge, we have to make it suitable for policy and society. The answer presented by the Protection and Development of the Dutch Archaeological-Historical Landscape programme (pdl/bbo) is an integrative landscape approach which applies inter- and transdisciplinarity, establishing links between archaeological-historical heritage and planning, and between research and policy. This is supported by two unifying concepts: ‘biography of landscape’ and ‘action research’. This approach focuses upon the interaction between knowledge, policy and an imagination centered on the public. The European perspective makes us aware of the resourcefulness of the diversity of landscapes, of social and institutional structures, of various sorts of problems, approaches and ways forward. In addition, two related issues stand out: the management of knowledge creation for landscape research and management, and the prospects for the near future. Underlying them is the imperative that we learn from the past ‘through landscape’

Advanced Data Mining and Machine Learning Algorithms for Integrated Computer-Based Analyses of Big Environmental Databases

Einsicht in die räumliche Verteilung geotechnischer und hydrologischer Untergrundeigenschaften sowie von Reservoir- und Umweltparametern sind grundlegend für geowissenschaftliche Forschungen. Entwicklungen in den Bereichen geophysikalische Erkundung sowie Fernerkundung resultieren in der Verfügbarkeit verschiedenster Verfahren für die nichtinvasive, räumlich kontinuierliche Datenerfassung im Rahmen hochauflösender Messverfahren. In dieser Arbeit habe ich verschiedene Verfahren für die Analyse erdwissenschaftlicher Datenbasen entwickelt auf der Basis von Wissenserschließungsverfahren. Eine wichtige Datenbasis stellt geophysikalische Tomographie dar, die als einziges geowissenschaftliches Erkundungsverfahren 2D und 3D Abbilder des Untergrunds liefern kann. Mittels unterschiedlicher Verfahren aus den Bereichen intelligente Datenanalyse und maschinelles Lernen (z.B. Merkmalsextraktion, künstliche neuronale Netzwerke, etc.) habe ich ein Verfahren zur Datenanalyse mittels künstlicher neuronaler Netzwerke entwickelt, das die räumlich kontinuierliche 2D oder 3D Vorhersage von lediglich an wenigen Punkten gemessenen Untergrundeigenschaften im Rahmen von Wahrscheinlichkeitsaussagen ermöglicht. Das Vorhersageverfahren basiert auf geophysikalischer Tomographie und berücksichtigt die Mehrdeutigkeit der tomographischen Bildgebung. Außerdem wird auch die Messunsicherheit bei der Erfassung der Untergrundeigenschaften an wenigen Punkten in der Vorhersage berücksichtigt. Des Weiteren habe ich untersucht, ob aus den Trainingsergebnissen künstlicher neuronaler Netzwerke bei der Vorhersage auch Aussagen über die Realitätsnähe mathematisch gleichwertiger Lösungen der geophysikalischen tomographischen Bildgebung abgeleitet werden können. Vorhersageverfahren wie das von mir vorgeschlagene, können maßgeblich zur verbesserten Lösung hydrologischer und geotechnischer Fragestellungen beitragen. Ein weiteres wichtiges Problem ist die Kartierung der Erdoberfläche, die von grundlegender Bedeutung für die Bearbeitung verschiedener ökonomischer und ökologischer Fragestellungen ist, wie z.B., die Identifizierung von Lagerstätten, den Schutz von Böden, oder Ökosystemmanagement. Kartierungsdaten resultieren entweder aus technischen (objektiven) Messungen oder visuellen (subjektiven) Untersuchungen durch erfahrene Experten. Im Rahmen dieser Arbeit zeige ich erste Entwicklungen hin zu einer automatisierten und schnellen Integration technischer und visueller (subjektiver) Daten auf der Basis unterschiedlicher intelligenter Datenanalyseverfahren (z.B., Graphenanalyse, automatische Konturerfassung, Clusteranalyse, etc.). Mit solchem Verfahren sollen hart oder weich klassifizierte Karten erstellt werden, die das Untersuchungsgebiet optimal segmentieren um höchstmögliche Konformität mit allen verfügbaren Daten zu erzielen

An intelligent Geographic Information System for design

Recent advances in geographic information systems (GIS) and artificial intelligence (AI) techniques have been summarised, concentrating on the theoretical aspects of their construction and use. Existing projects combining AI and GIS have also been discussed, with attention paid to the interfacing methods used and problems uncovered by the approaches. AI and GIS have been combined in this research to create an intelligent GIS for design. This has been applied to off-shore pipeline route design. The system was tested using data from a real pipeline design project. [Continues.