This book describes a method for building real-world problem solving systems such as medical diagnostic procedures and intelligent controllers for autonomous underwater vehicles (AUVs) and other robots. The approach taken is different from other work reported in the artificial intelligence literature in several respects: * It defines schema-based reasoning, in which schemas -- explicitly declared packets of related knowledge -- are used to control not only the reasoner's planning, but also all other facets of its behavior. * It is a kind of reactive reasoning that the author calls adaptive problem solving -- the reasoner maintains commitments to future goals but is able to change its focus of attention as the problem-solving situation requires. * It is a context-sensitive reasoning method. Every decision it makes relies on the use of contextual knowledge to be appropriate for the current problem-solving situation. Furthermore, context is represented explicitly; by always keeping a current representation of the context in mind, the reasoner's behavior is automatically sensitive to the context with very little work needed per decision. * Schema-based reasoning -- a generalization of case-based reasoning -- extends the usual idea of case-based reasoning to encompass all aspects of the reasoner's behavior, and it extends it to make use of generalized "cases" (i.e., schemas) rather than particular cases, thus saving effort needed to transfer knowledge from an old case to a new situation. Though the work originated in the domain of medical diagnostic problem solving, treating diagnosis as a planning task, it is even more appropriate for controlling autonomous systems. The author is currently extending the approach by creating a robust controller for long-range autonomous underwater vehicles that will be used to carry out ocean science missions.

This book describes a method for building real-world problem solving systems such as medical diagnostic procedures and intelligent controllers for autonomous underwater vehicles (AUVs) and other robots. The approach taken is different from other work reported in the artificial intelligence literature in several respects: * It defines schema-based reasoning, in which schemas -- explicitly declared packets of related knowledge -- are used to control not only the reasoner's planning, but also all other facets of its behavior. * It is a kind of reactive reasoning that the author calls adaptive problem solving -- the reasoner maintains commitments to future goals but is able to change its focus of attention as the problem-solving situation requires. * It is a context-sensitive reasoning method. Every decision it makes relies on the use of contextual knowledge to be appropriate for the current problem-solving situation. Furthermore, context is represented explicitly; by always keeping a current representation of the context in mind, the reasoner's behavior is automatically sensitive to the context with very little work needed per decision. * Schema-based reasoning -- a generalization of case-based reasoning -- extends the usual idea of case-based reasoning to encompass all aspects of the reasoner's behavior, and it extends it to make use of generalized "cases" (i.e., schemas) rather than particular cases, thus saving effort needed to transfer knowledge from an old case to a new situation. Though the work originated in the domain of medical diagnostic problem solving, treating diagnosis as a planning task, it is even more appropriate for controlling autonomous systems. The author is currently extending the approach by creating a robust controller for long-range autonomous underwater vehicles that will be used to carry out ocean science missions.

Case-Based Reasoning to User Interface Software Tools

The biennial International Conference on Case-Based Reasoning (ICCBR) - ries, which began in Sesimbra, Portugal, in 1995, was intended to provide an international forum for the best fundamental and applied research in case-based reasoning (CBR). It was hoped that such a forum would encourage the g- wth and rigor of the eld and overcome the previous tendency toward isolated national CBR communities. The foresight of the original ICCBR organizers has been rewarded by the growth of a vigorous and cosmopolitan CBR community. CBR is now widely recognized as a powerful and important computational technique for a wide range of practical applications. By promoting an exchange of ideas among CBR researchers from across the globe, the ICCBR series has facilitated the broader acceptance and use of CBR. ICCBR-99 has continued this tradition by attracting high-quality research and applications papers from around the world. Researchers from 21 countries submitted 80 papers to ICCBR-99. From these submissions, 17 papers were selected for long oral presentation, 7 were accepted for short oral presentation, and 19 papers were accepted as posters. This volume sets forth these 43 papers, which contain both mature work and innovative new ideas.

This book constitutes the refereed proceedings of the First International Conference on Case-Based Reasoning, ICCBR-95, held in Sesimbra, Portugal, in October 1995. The 52 revised papers included are classified as scientific papers , application papers , and posters . All current aspects of research and development aiming at industrial applications in CBR are addressed. Among the topical sections are case and knowledge representation, case retrieval, nearest neighbour methods, case adaption and learning, cognitive modelling, integrated reasoning methods, and application-oriented methods: planning, decision making, diagnosis, interpretation, design, etc.

"This 10-volume compilation of authoritative, research-based articles contributed by thousands of researchers and experts from all over the world emphasized modern issues and the presentation of potential opportunities, prospective solutions, and future directions in the field of information science and technology"--Provided by publisher.

This book constitutes the proceedings of the 10th International and Interdisciplinary Conference on Modeling and Using Context, CONTEXT 2017, held in Paris, France, in June 2017. The 26 full papers and 15 short papers presented were carefully reviewed and selected from 88 submissions. The papers feature research in a wide range of disciplines related to issues of context and contextual knowledge and discuss commonalities across and differences between the disciplines' approaches to the study of context. They are organized in the following topical sections: context in representation; context modeling of human activities; context in communication; context awareness; and various specific topics.