This book contains a cohesive, self-contained collection of theoretical and applied research results that have been achieved in this project which pertain to nonmonotonic and approximate easoning systems developed for an experimental unmanned aerial vehicle system used in the project. This book should be of interest to the theoretician and applied researcher alike and to autonomous system developers and software agent and intelligent system developers.

Handbook of Knowledge Representation describes the essential foundations of Knowledge Representation, which lies at the core of Artificial Intelligence (AI). The book provides an up-to-date review of twenty-five key topics in knowledge representation, written by the leaders of each field. It includes a tutorial background and cutting-edge developments, as well as applications of Knowledge Representation in a variety of AI systems. This handbook is organized into three parts. Part I deals with general methods in Knowledge Representation and reasoning and covers such topics as classical logic in Knowledge Representation; satisfiability solvers; description logics; constraint programming; conceptual graphs; nonmonotonic reasoning; model-based problem solving; and Bayesian networks. Part II focuses on classes of knowledge and specialized representations, with chapters on temporal representation and reasoning; spatial and physical reasoning; reasoning about knowledge and belief; temporal action logics; and nonmonotonic causal logic. Part III discusses Knowledge Representation in applications such as question answering; the semantic web; automated planning; cognitive robotics; multi-agent systems; and knowledge engineering. This book is an essential resource for graduate students, researchers, and practitioners in knowledge representation and AI. * Make your computer smarter* Handle qualitative and uncertain information* Improve computational tractability to solve your problems easily

Medicine and health care are currently faced with a significant rise in their complexity. This is partly due to the progress made during the past three decades in the fundamental biological understanding of the causes of health and disease at the molecular, (sub)cellular, and organ level. Since the end of the 1970s, when knowledge representation and reasoning in the biomedical field became a separate area of research, huge progress has been made in the development of methods and tools that are finally able to impact on the way medicine is being practiced. Even though there are huge differences in the techniques and methods used by biomedical researchers, there is now an increasing tendency to share research results in terms of formal knowledge representation methods, such as ontologies, statistical models, network models, and mathematical models. As there is an urgent need for health-care professionals to make better decisions, computer-based support using this knowledge is now becoming increasingly important. It may also be the only way to integrate research results from the different parts of the spectrum of biomedical and clinical research. The aim of this book is to shed light on developments in knowledge representation at different levels of biomedical application, ranging from human biology to clinical guidelines, and using different techniques, from probability theory and differential equations to logic. The book starts with two introductory chapters followed by 18 contributions organized in the following topical sections: diagnosis of disease; monitoring of health and disease and conformance; assessment of health and personalization; prediction and prognosis of health and disease; treatment of disease; and recommendations.

Advances in Knowledge Representation offers a compilation of state of the art research works on topics such as concept theory, positive relational algebra and k-relations, structured, visual and ontological models of knowledge representation, as well as detailed descriptions of applications to various domains, such as semantic representation and extraction, intelligent information retrieval, program proof checking, complex planning, and data preparation for knowledge modelling, and a extensive bibliography. It is a valuable contribution to the advancement of the field. The expected readers are advanced students and researchers on the knowledge representation field and related areas; it may also help to computer oriented practitioners of diverse fields looking for ideas on how to develop a knowledge-based application.

The proceedings of the Second International Conference on [title] held in Cambridge, Massachusetts, April 1991, comprise 55 papers on topics including the logical specifications of reasoning behaviors and representation formalisms, comparative analysis of competing algorithms and formalisms, and ana

Recording knowledge in a common framework that would make it possible to seamlessly share global knowledge remains an important challenge for researchers. This brief examines several ideas about the representation of knowledge addressing this challenge. A widespread general agreement is followed that states uniform knowledge representation should be achievable by using ontologies populated with concepts. A separate chapter is dedicated to each of the three introduced topics, following a uniform outline: definition, organization, and use. This brief is intended for those who want to get to know the field of knowledge representation quickly, or would like to be up to date with current developments in the field. It is also useful for those dealing with implementation as examples of numerous operational systems are also given.

This series is directed to diverse managerial professionals who are leading the transformation of individual domains by using expert information and domain knowledge to drive decision support systems (DSSs). The series offers a broad range of subjects addressed in specific areas such as health care, business management, banking, agriculture, environmental improvement, natural resource and spatial management, aviation administration, and hybrid applications of information technology aimed to interdisciplinary issues. This book series is composed of three volumes: Volume 1 consists of general concepts and methodology of DSSs; Volume 2 consists of applications of DSSs in the biomedical domain; Volume 3 consists of hybrid applications of DSSs in multidisciplinary domains. The book is shaped upon decision support strategies in the new infrastructure that assists the readers in full use of the creative technology to manipulate input data and to transform information into useful decisions for decision makers.

"This book focuses on the latest innovations in the process of manufacturing in engineering"--Provided by publisher.

The main goal of this book is to help organizations improve their effort estimates and effort estimation processes by providing a step-by-step methodology that takes them through the creation and validation of models that are based on their own knowledge and experience. Such models, once validated, can then be used to obtain predictions, carry out risk analyses, enhance their estimation processes for new projects and generally advance them as learning organizations. Emilia Mendes presents the Expert-Based Knowledge Engineering of Bayesian Networks (EKEBNs) methodology, which she has used and adapted during the course of several industry collaborations with different companies world-wide over more than 6 years. The book itself consists of two major parts: first, the methodology’s foundations in knowledge management, effort estimation (with special emphasis on the intricacies of software and Web development) and Bayesian networks are detailed; then six industry case studies are presented which illustrate the practical use of EKEBNs. Domain experts from each company participated in the elicitation of the bespoke models for effort estimation and all models were built employing the widely-used Netica TM tool. This part is rounded off with a chapter summarizing the experiences with the methodology and the derived models. Practitioners working on software project management, software process quality or effort estimation and risk analysis in general will find a thorough introduction into an industry-proven methodology as well as numerous experiences, tips and possible pitfalls invaluable for their daily work.