Integrating logic, object-oriented and procedural paradigms in a fault diagnosis and monitoring system

Fleischanderl, Gerhard; Friedrich, Gerhard; Neijdl, Wolfgang; Retti, Johannes

doi:10.1145/66617.66641

Cited by 3 publications

(2 citation statements)

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“…In the second stream, the systems developed are objectoriented and intelligent but are not distributed either in terms of knowledge sources or control (across more than one processor). Basically, each can be considered as a single-agent system comprised of objects [14][15][16][17][18][19][20][21][22].…”

Section: Introductionmentioning

confidence: 99%

A Development System for Intelligent Agent Manufacturing Software

Kwok

Norrie²

1994

Integrated Manufacturing Systems

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The intelligent agent object (IAO) system is a multi‐paradigm development environment which can be used to create intelligent agent systems for manufacturing or other domains. The IAO system was developed from the rule‐based object (RBO) system which is a programming environment integrating both the rule‐based and object‐oriented paradigms. Propagation‐oriented programming, access‐oriented programming and group‐oriented programming are among the extensions included in the IAO system. Its most unusual contribution is the propagation‐oriented programming paradigm which is not found in most systems. A key application is the messenger inferencing structure which is a user‐extendable framework supporting multiple knowledge representation, meta‐inference control, and distributed inference. This allows the IAO system to go beyond predicate logic based production rule programming. New developments are also introduced for access‐oriented programming. The IAO system can be used to develop integrated manufacturing systems such as the prototype automated guided vehicle planning and control system, which is briefly described.

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Section: Introductionmentioning

confidence: 99%

A Development System for Intelligent Agent Manufacturing Software

Kwok

Norrie²

1994

Integrated Manufacturing Systems

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“…We are currently using it to model a audio signal switching system, which we have previously described in [19], [8].…”

Section: Resultsmentioning

confidence: 99%

MOMO-model-based diagnosis for everybody

Friedrich

Nejdl

Sixth Conference on Artificial Intelligence for Applications

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In this paper we show how to implement the core of a model-based diagnosis system by a small hyperresolution-based procedure using Prolog. The algorithm is able to find all possible diagnosis candidates. The MOMO algorithm has a well-defined semantics and allows the description of models using general range-restricted clauses in contrast to earlier syst e m , which only allow a Horn clause description. We can model a large class of systems and incorporate different types of behavioral models such as correct behavior models, fault models, alibis, physical necessity rules etc.As the basic algorithm can be easily implemented by a few Prolog clauses as described in the paper it can serve as a test bed for various ideas concerning model-based diagnosis without using a full-fledged environment incorporating ATMS techniques. We have tested the algorithm using several models including well-known example systems as well as various modeling assumptions.Impact: Because of its generality and simplicity MOMO is an ideal research and prototyping tool for model-based diagnosis to evaluate various behavioral models and modeling assumptions. AbstractIn this paper we show how to implement the core of a model-based diagnosis system by a small hyperresolution-based procedure using Prolog. The algorithm is able to find all possible diagnosis candidates. The MOMO algorithm has a well-defined semantics and allows the description of models using general rangerestricted clauses in contrast t o earlier systems, which only allow a Horn clause description. We can model a large class of systems and incorporate different types of behavioral models such as correct behavior models, fault models, alibis, physical necessity rules etc.As the basic algorithm can be easily implemented by a few Prolog clauses as described in the paper it can serve as a test bed for various ideas concerning model-based diagnosis without using a full-fledged environment incorporating ATMS techniques. We have tested the algorithm using several models including well-known example systems as well as various modeling assumptions.

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