scholarly journals A Method of E-Service Workflow Composition Based on Linear Logic Inference Rules

Author(s):  
Shan Zhou ◽  
Fangyu Zhang
2013 ◽  
Vol 756-759 ◽  
pp. 2120-2124
Author(s):  
Shan Zhou ◽  
Fang Yu Zhang

The paper proposes a method for semantic message matching in automatic service composition. It develops a framework in which the exported message description and behavior description of a service, and represents the behavior of a service with a finite state machine. Since the service interface definition can be represented by ontology concepts, the internal representation language enables us to define some issues required by service composition formally, qualitative and quantitative constraints plus reasoning on concepts, and the service behavior can be represented using linear logic formulas, so the inference rules of linear logic can check the match-ability and satisfy-ability of service message.


2019 ◽  
Vol 1 (2) ◽  
pp. 205-219 ◽  
Author(s):  
Malusi Sibiya ◽  
Mbuyu Sumbwanyambe

This paper explains a proposed algorithm for severity estimation of plant leaf diseases by using maize leaf diseased samples. In the literature, a number of researchers have addressed the problem of plant leaf disease severity estimation, but a few, such as Sannakki et al., have used fuzzy logic to determine the severity estimations of the plant leaf diseases. The present paper aims to update the current algorithm used in the “Leaf Doctor” application that is used to estimate the severities of the plant leaf diseases by introducing the benefits of fuzzy logic decision making rules. This method will contribute to precision agriculture technology as it introduces an algorithm that may be embedded in smartphone devices and used in applications, such as a “Leaf Doctor” application. The applications designed based on the algorithm proposed in this study will help users who are inexperienced and not plant pathologists understand the level of the estimated disease severity. The use of fuzzy logic inference rules along with image segmentation determines the novelty of this approach in comparison with the available methods in the literature.


2000 ◽  
Vol 65 (3) ◽  
pp. 979-1013 ◽  
Author(s):  
Giovanni Sambin ◽  
Giulia Battilotti ◽  
Claudia Faggian

AbstractWe introduce a sequent calculus B for a new logic, named basic logic. The aim of basic logic is to find a structure in the space of logics. Classical, intuitionistic. quantum and non-modal linear logics, are all obtained as extensions in a uniform way and in a single framework. We isolate three properties, which characterize B positively: reflection, symmetry and visibility.A logical constant obeys to the principle of reflection if it is characterized semantically by an equation binding it with a metalinguistic link between assertions, and if its syntactic inference rules are obtained by solving that equation. All connectives of basic logic satisfy reflection.To the control of weakening and contraction of linear logic, basic logic adds a strict control of contexts, by requiring that all active formulae in all rules are isolated, that is visible. From visibility, cut-elimination follows. The full, geometric symmetry of basic logic induces known symmetries of its extensions, and adds a symmetry among them, producing the structure of a cube.


1982 ◽  
Vol 21 (03) ◽  
pp. 127-136 ◽  
Author(s):  
J. W. Wallis ◽  
E. H. Shortliffe

This paper reports on experiments designed to identify and implement mechanisms for enhancing the explanation capabilities of reasoning programs for medical consultation. The goals of an explanation system are discussed, as is the additional knowledge needed to meet these goals in a medical domain. We have focussed on the generation of explanations that are appropriate for different types of system users. This task requires a knowledge of what is complex and what is important; it is further strengthened by a classification of the associations or causal mechanisms inherent in the inference rules. A causal representation can also be used to aid in refining a comprehensive knowledge base so that the reasoning and explanations are more adequate. We describe a prototype system which reasons from causal inference rules and generates explanations that are appropriate for the user.


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