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

Using causal knowledge to improve retrieval and adaptation in case-based reasoning systems for a dynamic industrial process

Case-based reasoning (CBR) is a reasoning paradigm that starts the reasoning process by examining past similar experiences. The motivation behind this thesis lies in the observation that causal knowledge can guide case-based reasoning in dealing with large and complex systems as it guides humans. In this thesis, case-bases used for reasoning about processes where each case consists of a temporal sequence are considered. In general, these temporal sequences include persistent and transitory (non-persistent) attributes. As these sequences tend to be long, it is unlikely to find a single case in the case-base that closely matches the problem case. By utilizing causal knowledge in the form of a dynamic Bayesian network (DBN) and exploiting the independence implied by the structure of the network and known attributes, this system matches independent portions of the problem case to corresponding sub-cases from the case-base. However, the matching of sub-cases has to take into account the persistence properties of attributes. The approach is then applied to a real life temporal process situation involving an automotive curing oven, in which a vehicle moves through stages within the oven to satisfy some thermodynamic relationships and requirements that change from stage to stage. In addition, testing has been conducted using data randomly generated from known causal networks. (Abstract shortened by UMI.) Paper copy at Leddy Library: Theses & Major Papers - Basement, West Bldg. / Call Number: Thesis2005 .T54. Source: Masters Abstracts International, Volume: 45-01, page: 0366. Thesis (M.Sc.)--University of Windsor (Canada), 2006

Retrieval, reuse, revision and retention in case-based reasoning

El original está disponible en www.journals.cambridge.orgCase-based reasoning (CBR) is an approach to problem solving that emphasizes the role of prior experience during future problem solving (i.e., new problems are solved by reusing and if necessary adapting the solutions to similar problems that were solved in the past). It has enjoyed considerable success in a wide variety of problem solving tasks and domains. Following a brief overview of the traditional problem-solving cycle in CBR, we examine the cognitive science foundations of CBR and its relationship to analogical reasoning. We then review a representative selection of CBR research in the past few decades on aspects of retrieval, reuse, revision, and retention.Peer reviewe

A Comparative Review on Computational Modeling Paradigms. A Study on Case-Based Modeling and Political Terrorism

We review the advances in Case-Based Computational Modeling on Political Analysis issues. Starting in early „70s, the research on political terrorism has been challenged by the latest advances of terrorism computational modeling research. Nowadays Political Analysis community has a wider perspective over the terrorism research aims, methodology and instruments. Widening up this perspective is not a matter of political analysis and research only, it is as well a long-term effect of an interdisciplinary style which has been adopted within the area by acknowledging the scientific advances and support of the Computational Modeling and Simulation as a specific scientific research method. Computational Modeling includes several research frameworks. The Case-Based Modeling is analysed and evaluated on a comparative basis with Agent-Based Modeling in a study on political terrorism phenomena

A Domain-Independent Algorithm for Plan Adaptation

The paradigms of transformational planning, case-based planning, and plan debugging all involve a process known as plan adaptation - modifying or repairing an old plan so it solves a new problem. In this paper we provide a domain-independent algorithm for plan adaptation, demonstrate that it is sound, complete, and systematic, and compare it to other adaptation algorithms in the literature. Our approach is based on a view of planning as searching a graph of partial plans. Generative planning starts at the graph's root and moves from node to node using plan-refinement operators. In planning by adaptation, a library plan - an arbitrary node in the plan graph - is the starting point for the search, and the plan-adaptation algorithm can apply both the same refinement operators available to a generative planner and can also retract constraints and steps from the plan. Our algorithm's completeness ensures that the adaptation algorithm will eventually search the entire graph and its systematicity ensures that it will do so without redundantly searching any parts of the graph.Comment: See http://www.jair.org/ for any accompanying file

Case-Based Reasoning Systems: From Automation to Decision-Aiding and Stimulation

Over the past decade, case-based reasoning (CBR) has emerged as a major research area within the artificial intelligence research field due to both its widespread usage by humans and its appeal as a methodology for building intelligent systems. Conventional CBR systems have been largely designed as automated problem-solvers for producing a solution to a given problem by adapting the solution to a similar, previously solved problem. Such systems have had limited success in real-world applications. More recently, there has been a search for new paradigms and directions for increasing the utility of CBR systems for decision support. This paper focuses on the synergism between the research areas of CBR and decision support systems (DSSs). A conceptual framework for DSSs is presented and used to develop a taxonomy of three different types of CBR systems: 1) conventional, 2) decision-aiding, and 3) stimulative. The major characteristics of each type of CBR system are explained with a particular focus on decision-aiding and stimulative CBR systems. The research implications of the evolution in the design of CBR systems from automation toward decision-aiding and stimulation are also explored