We can learn a lot about what features are important for retrieval by comparing similar cases in a case-base. We can determine which features are important in predicting outcomes and we can assign weights to features accordingly. In the same manner we can discover which features are important in specific contexts and determine localised feature weights that are specific to individual cases. In this paper we describe a comprehensive set of techniques for learning local feature weights and we evaluate these techniques on a case-base for conflict resolution in air traffic control. We show how introspective learning of feature weights improves retrieval and how it can be used to determine context sensitive local weights. We also show that introspective learning does not work well in case-bases containing only pivotal cases because there is no redundancy to be exploited. 2 Using introspective learning to improve retrieval in CBR: A case study in air traffic control 2 2 This...

This paper deals with the very fundamentals of a socalled "Turing Test Methodology" for expert system validation which was recently proposed by [KP96]. It is more intended to provide a firm basis for discussions among the engaged scientists than to become a conference contribution. For this purpose, emphasis is put on a detailed and, perhaps, a little long-winded discussion, in some places. There might be future versions of this report to reflect the ongoing progress of the investigation. First, we survey several concepts of verification and validation. Our favoured concepts are lucidly characterized by the words that verification guarantees to build the system right whereas validation deals with building the right system. Next, we critically inspect the thought-experiment called the TURING test. It turns out that, although this approach may not be sufficient to reveal a system 's intelligence, it provides a suitable methodological background to certify a system's validity. The prin...

. There is considerable enthusiasm about Case-Based Reasoning as a means of developing knowledge-based systems. There are two broad reasons for this enthusiasm. First, it is evident that much of human expert competence is experience based and it makes sense to adopt a reuse-based methodology for developing knowledge based systems. The other reason is the expectation that using Case-Based Reasoning to develop knowledge based systems will involve less knowledge engineering than alternative `first-principles' based approaches. In this paper I explore the veracity of this assertion and outline the types of situation in which it will be true. CBR is perceived to have this knowledge engineering advantage because it allows the development of knowledge based systems in weak theory domains. If CBR can work without formalising a domain theory then there is a question about the quality of solutions produced by case-based systems. This is the other issue discussed in this paper and situations wher...

This paper deals with problems of advanced knowledge representation for case-based reasoning. Beyond classical attribute/value pair representations, there is a more sophisticated perspective of cases as structured objects. Structural knowledge representation and reasoning allows for an immediate transfer of the results of computing similarity to case adaptation, which is currently the most crucial part both in casebased reasoning theory and in applications. The concepts of structural similarity are motivated by the authors' experience in a large applicationoriented research and development project. The key ideas and concepts underlying structural similarity concepts are introduced. Based on these conceptual preliminaries, there is developed some approach towards axiomatization of structural similarity. Similarity concepts are formalized as operators with basic properties which can be lucidly axiomatized. The authors derive a few results exhibiting the power and limitations of fundament...

. In this paper we present a Case-Based Reasoning system that helps air traffic controllers to solve aircraft conflicts. In particular, we focus on the hierarchical aspect of the CBR system which is able to solve multiple aircraft conflicts, i.e. conflicts that involve three or more aircraft. It is not practical to build a case-base for the different multiple aircraft possibilities as has been done for two aircraft conflicts. Instead we explore the possibility of using case fragments from two aircraft conflicts in multiple aircraft situations. The hierarchical structure that we describe here makes this possible. This involves the use of some high-level analysis of the solutions coming from the case base because the solution to a multiple aircraft conflict is not necessarily one of the solutions of the component two aircraft conflicts. The hierarchical structure allows the use of the same case-base for both two aircraft conflicts and multiple aircraft conflicts with big savings in space...

Case-Based Reasoning (CBR) has emerged from research in cognitive psychology as a model of human memory and remembering. It has been embraced by researchers of AI applications as a methodology that avoids some of the knowledge acquisition and reasoning problems that occur with other methods for developing knowledge-based systems. In this paper we propose that, in developing knowledge based systems, knowledge engineering addresses two tasks. There is a problem analysis task that produces the problem representation and there is the task of developing the inference mechanism. CBR has an impact on the second of these tasks but helps less with the first. We argue that in some domains this problem analysis process can be significant and propose an iterative methodology for addressing it. To evaluate this, we describe the application of case-based reasoning to the problem of aircraft conflict resolution in a system called ISAC. We describe the application of this iterative methodolo...

The work described in this thesis is, except where otherwise stated, entirely that of the author and has not been submitted as an exercise for a degree at this or any other university.