Exploiting domain knowledge for approximate diagnosis

The AI literature contains many definitions of diagnostic reasoning most of which are defined in terms of the logical entailment relation. We use existing work on approximate entailment to define notions of approximation in diagnosis. We show how such a notion of approximate diagnosis can be exploited in various diagnostic strategies. We illustrate these strategies by performing diagnosis in a small car domain example

Validation and verification of conceptual models of diagnosis

Traditional approaches to validation and verification of KBS aim at investigating properties of a KBS which are independent of the particular task of the KBS, and are phrased in terms of the implementation language of the final system. In contrast to this, we propose an approach to validation and verification of KBS which exploits task-specific properties of a KBS, and which is based on an implementation-independent conceptual model of the system

Using domain knowledge to select solutions in abductive diagnosis

This paper presents a novel extension to abductive reasoning in causal nets, namely the use of domain knowledge to select among alternative diagnoses. We describe how preferences among multiple causes of a given state can be expressed in terms of causal nets, and how these preferences can be used to select among alternative diagnoses. We investigate this new extension by proving a number of properties, and show how our preference scheme interacts with conventional ways of choosing among competing diagnoses. Our extension increases the expressive power of causal nets, enjoys a number of desirable properties, and compares favourably with existing proposals for expressing preferential knowledge in causal nets

An extended spectrum of logical definitions for diagnostic sytems

The goal of this work is to develop a single uniform theory, which enables us to describe many different diagnostic systems. We will give a general definition of diagnostic systems. Our claim is that a large number of very different diagnostic systems can be described by this definition by choosing the right values for six parameters in this definition. Our work is an extension of the spectrum of logical definitions of Console and Torasso

Approximations in diagnosis: motivations and techniques

We argue that diagnosis should not be seen as solving a problem with a unique definition, but rather that there exists a whole space of reasonable notions of diagnosis. These notions can be seen as mutual approximations. We present a number of reasons for choosing among different notions of diagnosis. We also present an exhaustive categorisation of techniques that can be employed to obtain approximations, as well as a number of specific example techniques for each category. We also show that it is possible to characterise the relations between the approximations obtained by these techniques