Knowledge attributes: fuzzy application of temporal constraints in active expert database systems

Bechtold, Richard

doi:10.1109/dmesp.1991.171766

Cited by 2 publications

(2 citation statements)

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“…Fuzzy logic is particularly useful when reliability models must capture and use imprecise, ambiguous, uncertain or otherwise connotatively flexible terms in algorithmic reasoning. Thus, reliability modelling is simply one of numerous applications for which there is the potential for automated reasoning based on representations of human knowledge and for the fuzzy interpretation and application of attributes associated with such knowledge [13]. Gao et al [14] introduced the concept of using Fuzzy Petri nets (FPNs) to evaluate the reliability of communication diagrams.…”

Section: Introductionmentioning

confidence: 99%

Fuzzy stochastic Petri nets and analysis of the reliability of multi‐state systems

Zhang

Yao

2015

IET softw.

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Commonly used software may be inadequate for analysing multi-state systems because of their increasing complexity. Here, a general method of analysing the reliability of systems using fuzzy stochastic Petri nets (FSPNs) based on the fuzzy parameters of FSPNs is proposed. First, the authors' present the degree of importance and node-to-node system dependence definitions and access rules and classify the state space using the weighted state norm. Second, using the firing rule of the FSPNs and the concept of hierarchical sub-nodes, the authors' propose an algorithm for the analysis of adaptive FSPNs and their reliability. An adaptive simulation system based on FSPNs is also proposed. Estimates are applied to the adaptive simulation system, rendering the multi-state system more reliable and enabling the use of a structured cooperative communication network of users to request services. The results of the case study demonstrate the validity of the proposed approach.

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Section: Introductionmentioning

confidence: 99%

Fuzzy stochastic Petri nets and analysis of the reliability of multi‐state systems

Zhang

Yao

2015

IET softw.

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“…This need for fuzzy logic is largely driven by the fact that the vast majority of human knowledge exhibits one or more of these characteristics (that is, it is imprecise, ambiguous, uncertain, etc.). Consequently, software process modelling is just one of numerous applications where there is a potential for automated reasoning based on representations of human knowledge, or on the fuzzy interpretation and application of attributes associated with such knowledge [1].…”

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confidence: 99%

Software engineering process support based on fuzzy imploding Petri nets

Bechtold

1992

Proceedings of the 30th Annual Southeast Regional Conference on - ACM-SE 30

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Software engineering process management and control is increasingly recognized as critical to the timely engineering of quality products. Automated process support within an integrated environment provides otherwise unachievable levels of insight, coordination, control, and flexibility in governing the activities and resources that comprise the engineering process. Crucial to process flexibility and robustness is the ability of a process to both withstand and react to the impact of unexpected or uncontrollable events. Using Petri nets as a basis for process modelling, this paper proposes an extension to fuzzy Petri nets which meaningfully models the impact of and response to the chaotic events that typically disrupt any complex process. Concepts introduced include: transition release thresholds, transition recall thresholds, transition backfiring, token recalls, token maturity, and token chains. OVERVIEWVirtually all complex software engineering efforts require coordinating, scheduling, and managing highly interactive resources. Efficient and successful software engineering efforts are often based on the preplanned institution of one or more processes which serve to govern the activities and interactions of involved personnel and resources. There are. however, aspects of software engineering process support that are not characterized by the orderliness and precision needed by most automated information systems. When a large-scale engineering operation is subjected to an excessive number of unanticipated events, this can potentially exceed the ability of an automated environment to continue exerting overall control. Chaos develops and all too readily becomes self-perpetuating. Consequently, investigation into software process models in general and into the possibilities for providing automated or environment based support to the software engineering process must include investigations into maintaining process support under highly volatile or even hostile conditions. Furthermore, in addition to the need for process models and their environments to be dynamically flexible, it is also important for such models to be capable of handling the uncertainty and unpredictability inherent in attempting to anticipate the consequences of actually instantiating a given process. Although various static approaches exist for better understanding the uncertainty in software development, dynamically changing uncertainty within the software engineering process is an area still largely unaddressed.Fuzzy logic can be leveraged to make contributions specifically in this area--an area where computers are traditionally most inept. That is, fuzzy logic does not insist on absolute certainty, nor on total confidence, nor on complete information. From this perspective, there is considerable potential for the application of fuzzy logic principles in the robust support and enforcement of software engineering processes--particularly if there are reasonable expectations that such processes are at risk of disorganizing influences from either unpre...

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Knowledge attributes: fuzzy application of temporal constraints in active expert database systems

Cited by 2 publications

References 19 publications

Fuzzy stochastic Petri nets and analysis of the reliability of multi‐state systems

Fuzzy stochastic Petri nets and analysis of the reliability of multi‐state systems

Software engineering process support based on fuzzy imploding Petri nets

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