Model-based reasoning

About: Model-based reasoning is a research topic. Over the lifetime, 4905 publications have been published within this topic receiving 125223 citations.

Case-based reasoning: foundational issues, methodological variations, and system approaches

Abstract: Case-based reasoning is a recent approach to problem solving and learning that has got a lot of attention over the last few years. Originating in the US, the basic idea and underlying theories have spread to other continents, and we are now within a period of highly active research in case-based reasoning in Europe, as well. This paper gives an overview of the foundational issues related to case-based reasoning, describes some of the leading methodological approaches within the field, and exemplifies the current state through pointers to some systems. Initially, a general framework is defined, to which the subsequent descriptions and discussions will refer. The framework is influenced by recent methodologies for knowledge level descriptions of intelligent systems. The methods for case retrieval, reuse, solution testing, and learning are summarized, and their actual realization is discussed in the light of a few example systems that represent different CBR approaches. We also discuss the role of case-based methods as one type of reasoning and learning method within an integrated system architecture.

Case-based reasoning

Abstract: Case-based reasoning is one of the fastest growing areas in the field of knowledge-based systems and this book, authored by a leader in the field, is the first comprehensive text on the subject. Case-based reasoning systems are systems that store information about situations in their memory. As new problems arise, similar situations are searched out to help solve these problems. Problems are understood and inferences are made by finding the closest cases in memory, comparing and contrasting the problem with those cases, making inferences based on those comparisons, and asking questions when inferences can't be made. This book presents the state of the art in case-based reasoning. The author synthesizes and analyzes a broad range of approaches, with special emphasis on applying case-based reasoning to complex real-world problem-solving tasks such as medical diagnosis, design, conflict resolution, and planning. The author's approach combines cognitive science and engineering, and is based on analysis of both expert and common-sense tasks. Guidelines for building case-based expert systems are provided, such as how to represent knowledge in cases, how to index cases for accessibility, how to implement retrieval processes for efficiency, and how to adapt old solutions to fit new situations. This book is an excellent text for courses and tutorials on case-based reasoning. It is also a useful resource for computer professionals and cognitive scientists interested in learning more about this fast-growing field.

Reasoning About Knowledge

Abstract: A model for knowledge and its properties completeness and complexity - results and techniques knowledge in distributed systems actions and protocols common knowledge, co-ordination and agreement evolving knowledge dealing with logical omniscience knowledge and computation common knowledge revisited.

Introduction To Expert Systems

Abstract: From the Publisher: The third edition of Peter Jackson's book, Introduction to Expert Systems, updates the technological base of expert systems research and embeds those results in the context of a wide variety of application areas. The earlier chapters take a more practical approach to the basic topics than the previous editions, while the later chapters introduce new topic areas, such as case-based reasoning, connectionist systems, and hybrid systems. Results in related areas, such as machine learning and reasoning with uncertainty are also accorded a thorough treatment.