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Our instructional program, CATO, uses a model of case-based legal argument to teach law students basic skills of making arguments with cases. CATO represents abstract knowledge about the meaning of the similarities and differences between cases in a Factor Hierarchy, in which the ‘factors’ used to represent case facts are linked to higher level concerns and legal issues. The Factor Hierarchy enables...
We present the current status of a recipe planner for bioprocesses. This case-based reasoner adapts previously successful recipes for each batch of the process. In this domain, recipe planning is difficult as actual numerical values of recipe parameters are crucial but quantitative information is scarce. However, adaptation, although far from trivial, is less complicated than planning from scratch...
This paper addresses the role of abstraction in case-based reasoning. We develop a general framework for reusing cases at several levels of abstraction, which is particularly suited for describing and analyzing existing and designing new approaches of this kind. We argue that in synthetic tasks (e.g. configuration, design, and planning), abstraction can be successfully used to improve the efficiency...
The conflict resolution task performed by air-traffic controllers appears a suitable task for automation using CBR. This is because human competence seems to involve recognising situations and reusing solutions. In this paper we present our experiences in developing a CBR system to support this conflict resolution task. We discuss the problems of case representation: the macro problem of what should...
Most commonly, case-based reasoning is applied in domains where attribute value representations of cases are sufficient to represent the features relevant to support classification, diagnosis or design tasks. Distance functions like the Hamming-distance or their transformation into similarity functions are applied to retrieve past cases to be used to generate the solution of an actual problem. Often,...
Document drafting—an important problem-solving task of professionals in a wide variety of fields—typifies a design task requiring complex adaptation for case reuse. This paper proposes a framework for document reuse based on an explicit representation of the illocutionary and rhetorical structure underlying documents. Explicit representation of this structure facilitates (1) interpretation of previous...
In this paper we describe a methodological analysis of EBMT (Example-Based Machine Translation) based on a CBR (Case-Based Reasoning) perspective. This analysis focuses on adaptation. We argue that, just as in CBR, the overall power of an EBMT system is its ability to adapt examples retrieved to suit the new problem translation. Here we describe a technique whereby reusability is a function of the...
Efficiently indexing and retrieving cases from a very large case library are major concerns when building a Case-Based Reasoning (CBR) system. Most CBR research has focused on representation of cases, how to identify features that should be used for retrieval; and similarity measurement between values of attributes. In this paper, we propose a method for dynamically creating indices, and, also different...
Since the advent of case-based reasoning (CBR) in the early eighties, two schools seem to have emerged: One that is more concerned with the cognitive aspects of CBR, and another, arguably more applied school, which views CBR as an AI ‘workhorse’, as it were, that can provide solutions to real-world problems. The former school is sometimes associated with the US, and the latter with Europe. This work,...
In this paper we address the problem of locating the appropriate component in an object-oriented software repository along with the issue of extending the component to adapt it to particular requirements. In order to give support to both types of tasks, we use a CBR approach which allows to profit from past experiences with the use of the repository, and to integrate different knowledge sources under...
AI research on case-based reasoning has led to the development of many laboratory case-based systems. As we move towards introducing these systems into work environments, explaining the processes of case-based reasoning is becoming an increasingly important issue. In this paper we describe the notion of a meta-case for illustrating, explaining and justifying case-based reasoning. A meta-case contains...
The paper describes a novel architecture for image understanding. It is based on acquisition of radiologist knowledge, and combines low-level structure analysis with high-level interpretation of image content, within a task-oriented model. A case based reasoner working on a segment case-base contains the individual image segments. These cases with labels are considered indexes for another case based...
A major challenge for case-based reasoning (CBR) is to overcome the knowledge-engineering problems incurred by developing adaptation knowledge. This paper describes an approach to automating the acquisition of adaptation knowledge overcoming many of the associated knowledge-engineering costs. This approach makes use of inductive techniques, which learn adaptation knowledge from case comparison. We...
This paper proposes an agent model of case based classification. The idea is to allow cases, and more generally memorized problem solving experiences, to take a more active role in future problem solving. This is achieved through the so called memory agents which are selected cases with their own reasoning mechanisms. The proposed model of memory agents enables context sensitive classification, leading...
In this paper we argue that description logics with their object-oriented representation based on a declarative semantics and their powerful inferences are a good base for building similarity-based systems. But in existing description logic systems it is not possible to formulate and use knowledge about concrete domains (e.g. data types like numbers, strings, sets of symbols). Based on Baader and...
This paper presents Objectdirected Case Retrieval Nets, a memory model developed for an application of Case-Based Reasoning to the task of technical diagnosis. The key idea is to store cases, i.e. observed symptoms and diagnoses, in a network and to enhance this network with an object model encoding knowledge about the devices in the application domain.
This paper describes a representation of plan cases as a structured set of goals and actions. These goals and actions are the unit pieces that form a case. These case pieces are related each other by hierarchical and temporal links (explanations) forming a tree-like network. We give importance not just to explicit links, i.e., links between case pieces which are concretely known, but also to implicit...
Analogical reuse, as one form of case-based reasoning, has been shown to aid specification of requirements for computer systems. This paper reports an investigation in which 5 inexperienced software engineers transferred a reusable specification to produce a solution for an analogical software engineering problem. The software engineers exhibited mental laziness as well as analogical reasoning during...
One problem posed by the unsupported navigation in a hypermedia system is that users often tend to get lost in the hyperspace. In this paper we describe HYPERCASE, a system for guided knowledge navigation in a hyperspace using a Case-Based Reasoning approach. In the presentation we stress the innovative technique, based on a sub-symbolic approach, we have used to retrieve cases from a case library,...
We present a similarity criterion based on feature weighting. Feature weights are recomputed dynamically according to the performance of cases during planning episodes. We will also present a novel algorithm to analyze and explain the performance of the retrieved cases and to determine the features whose weights need to be recomputed. Experiments show that the integration of our similarity criterion...
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