Representing and reasoning about incomplete and indefinite qualitative temporal information is an essential part of many artificial intelligence tasks. An interval-based framework and a point-based framework have been proposed for representing such temporal information. In this paper, we address two fundamental reasoning tasks that arise in applications of these frameworks: Given possibly indefinite and incomplete knowledge of the relationships between some intervals or points, (i) find a scenario that is consistent with the information provided, and (ii) find the feasible relations between all pairs of intervals or points. For the point-based framework and a restricted version of the intervalbased framework, we give computationally efficient procedures for finding a consistent scenario and for finding the feasible relations. Our algorithms are marked improvements over the previously known algorithms. In particular, we develop an O(n 2 ) time algorithm for finding one co...

Patdex is an expert system which carries out case-based reasoning for the fault diagnosis of complex machines. It is integrated in the Moltke workbench for technical diagnosis, which was developed at the university of Kaiserslautern over the past years, Moltke contains other parts as well, in particular a model-based approach; in Patdex where essentially the heuristic features are located. The use of cases also plays an important role for knowledge acquisition. In this paper we describe Patdex from a principal point of view and embed its main concepts into a theoretical framework 1 General Considerations Patdex 1 is an expert system which carries out case-based reasoning for the fault diagnosis of complex machines. It is integrated in the Moltke workbench 2 for technical diagnosis, which was developed at the university of Kaiserslautern over the past years (cf. e.g. [4, 5, 23]), Moltke contains other parts as well (cf. e.g. [16]), in particular a model-based approach (cf. [21, ...

Time is an important aspect of information in medical domains. In this paper, we adopt Allen's influential interval algebra framework for representing temporal information. The interval algebra allows the representation of indefinite and incomplete information which is necessary in many applications. However, answering interesting queries in this framework has been shown to be almost assuredly intractable. We show that when the representation language is sufficiently restricted we can develop efficient algorithms for answering interesting classes of queries including: (i) determining whether a formula involving temporal relations between events is possibly true and necessarily true; and (ii) answering aggregation questions where the set of all events that satisfy a formula are retrieved. We also show, by examining applications of the interval algebra discussed in the literature, that our restriction on the representation language often is not overly restrictive in practice. 1 Introduct...

Model-based diagnosis (MBD) tackles the problem of troubleshooting systems starting from a description of their structure and function (or behavior). Time is a fundamental dimension in MBD: the behavior of most systems is time-dependent in one way or another. Temporal MBD, however, is a difficult task and indeed many simplifying assumptions have been adopted in the various approaches in the literature. These assumptions concern different aspects such as the type and granularity of the temporal phenomena being modeled, the definition of diagnosis, the ontology for time being adopted. Unlike the atemporal case, moreover, there is no general "theory" of temporal MBD which can be used as a knowledge-level characterization of the problem. In this paper we present a general characterization of temporal model-based diagnosis. We distinguish between different temporal phenomena that can be taken into account in diagnosis and we introduce a modeling language which can capture all such phenomena...

Several approaches have been proposed to deal with time in diagnosis. The goal of this paper is to propose a logical characterization of diagnosis with temporal knowledge, and, specifically, diagnosis with temporal constraints on the evolution of the system to be diagnosed. The characterization is independent of the specific temporal constraint language being used and is an extension of an abductive characterization of atemporal diagnosis. In a companion paper [ 4 ] we discuss a computational characterization of a restriction of the framework, based on the co-operation of an abductive and a temporal reasoner. 1 Introduction The need of taking into account the temporal dimension in model-based diagnosis has been advocated by many researchers (see, e.g., chapter 6 in [ 17 ] ). While a static model describes the correct and/or faulty behavior of a system (or of its components), at least two different (but related) dimensions of time have been considered in the approaches proposed so far:...

In this paper interval logic (Allen 1983) with some extensions is proposed for knowledge-based scheduling of production processes. The logic is embedded in a three-layered architecture consisting of the knowledge representation of the production process, the constraint checking and propagation mechanism, and a heuristic algorithm that determines which jobs are scheduled when. The representation of the production process, i.e. the required resources and their allocations by operations is object-based and facilitates so the structuring of the knowledge base. Furthermore, this object structure improves temporal reasoning since the propagation of temporal constraints can be localized partly to these objects. The explicit knowledge representation and the object-oriented structure of the whole software design promise an easier reuse of software artifacts. 1.

As noticed in some recent work, describing and diagnosing temporal behavior and faults is an important, but complex task. In this paper we discuss a new approach based on a subset of Allen's interval algebra, which extends previous work by allowing both the representation of qualitative temporal behavior (including symptoms over time) and the diagnosis of these systems.

Abstract not found

Abstract not found

In this paper we will present a design model (in the sense of KADS) for the domain of technical diagnosis. Based on this we will describe the fully implemented expert system shell MOLTKE 3.0, which integrates common knowledge acquisition methods with techniques developed in the fields of Model-Based Diagnosis and Machine Learning, especially Case-Based Reasoning. 1. Introduction When starting a real world expert system project knowledge acquisition is the bottleneck. A main reason for this is the gap between the languages of the domain expert and that used for the implementation by the knowledge engineer. This gap reflects the difference between the cognitive models of the application area the two involved persons have. According to the KADS-group to overcome these shortcomings a conceptual model of the domain has to be developed [1]. Then this conceptual model has to be transfered into a design model. We think that developing a conceptual model has a few drawbacks: . The know...