Distributed Reasoning for Restricted Weakly-Linear Disjunctive Tuple-Generating Dependencies

2020 ◽  
pp. 140-149
Author(s):  
Arash Karimi ◽  
Jia-Huai You
Keyword(s):  
Distributed Reasoning ◽  
Weakly Linear

Weakly linear systems for matrices over the max-plus quantale

2021 ◽  
Author(s):  
Aleksandar Stamenković ◽  
Miroslav Ćirić ◽  
Dragan Djurdjanović
Keyword(s):  
Linear Systems ◽  
Weakly Linear

scholarly journals Cooperative Decision Support Systems

2011 ◽  
pp. 109-115 ◽  
Author(s):  
Pascale Zaraté
Keyword(s):  
Decision Making ◽  
Search Space ◽  
Optimal Decision ◽  
Decision Making Process ◽  
Collaborative Decision Making ◽  
Distributed Reasoning ◽  
Cooperative Decision Making ◽  
Optimal Decision Making ◽  
Analytical Tools ◽  
Collaborative Decision

The subject of our research aims to support in the most suitable way the collaborative decision-making process. Several scientific approaches deal with collaborative decision-making: decision analysis (Carlsson & Turban, 2002; Doyle & Thomason, 1999; Keeney & Raiffa, 1976) developing different analytical tools for optimal decision-making; in management sciences the observation of decision-making styles activity (Nuut, 2005; Fong, Wyer, & Robert 2003); decision-making as a group work (Esser, 1998; Matta & Corby, 1997); studies concerning different types of decisions focalised on number of actors: individual (Keeney & Raiffa, 1976), group (Shim, Warkentin, Courtney, Power, Sharda, & Carlsson, 2002), cooperative (Zaraté, 2005), and collaborative (Karacapilidis & Papadias, 2001). For the collaborative decision-making field, the situation is clear. In most of research studies, the concept of collaborative decision-making is used as a synonym for cooperative decision-making. Hence, the collaborative decision-making process is considered to be distributed and asynchronous (Chim, Anumba, & Carillo, 2004; Cil, Alpturk, & Yazgan, 2005). However, we can stand out several works, having different research approaches, considering collaborative decision-making process as multi-actor decision-making process, where actors have different goals. Considering (Panzarasa, Jennings, & Norman, 2002) the collaborative decision-making process is seen as “a group of logically decentralised agents that cooperate to achieve objectives that are typically beyond the capacities of an individual agent. In short, the collaborative decision-making has generally been viewed and modelled as a kind of distributed reasoning and search, whereby a collection of agents collaboratively go throughout the search space of the problem in order to find a solution.” The main interrogation of this article is to study the best way to support collaborative decision-making process.

A COLLABORATIVE APPROACH TO CONSTRUCTION OF LARGE SCALE DISTRIBUTED REASONING SYSTEMS

2011 ◽  
Vol 20 (06) ◽  
pp. 1083-1106 ◽  
Author(s):  
ATE PENDERS ◽  
GREGOR PAVLIN ◽  
MICHIEL KAMERMANS
Keyword(s):  
Collaborative Design ◽  
Service Discovery ◽  
Large Scale ◽  
Collaborative Approach ◽  
Complex Situation ◽  
Distributed Reasoning ◽  
Heterogeneous Services ◽  
Efficient Construction ◽  
Service Oriented ◽  
Definition Of

This paper introduces a new collaborative approach to construction of large scale service oriented systems supporting distributed reasoning. In particular, we assume systems in which complex situation assessment is carried out through composition of heterogeneous services, each specialized for a particular type of analysis. Different services are composed automatically by using service discovery and negotiation. One of the major challenges in such settings is efficient definition of a large number of different types of services. The presented solution supports efficient definition of services by using a combination of light weight service ontologies, efficient construction procedures and tools. In particular, machine-understandable descriptions of heterogeneous services with well defined syntax and semantics can be created by multiple designers, without complex coordination of collaborative design processes and without any knowledge of formal ontologies.

Stabilization of weakly linear systems

1989 ◽  
Vol 53 (6) ◽  
pp. 703-707
Author(s):  
V.A. Kolosov
Keyword(s):  
Linear Systems ◽  
Weakly Linear

scholarly journals Weakly linear systems of fuzzy relation inequalities and their applications: A brief survey

Filomat ◽  
2012 ◽  
Vol 26 (2) ◽  
pp. 207-241 ◽  
Author(s):  
Jelena Ignjatovic ◽  
Miroslav Ciric
Keyword(s):  
Social Network ◽  
Network Analysis ◽  
Linear Systems ◽  
Fuzzy Relation ◽  
General Aspect ◽  
State Reduction ◽  
Fuzzy Relation Equations ◽  
The Social ◽  
Fuzzy Automata ◽  
Weakly Linear

Weakly linear systems of fuzzy relation inequalities and equations have recently emerged from research in the theory of fuzzy automata. From the general aspect of the theory of fuzzy relation equations and inequalities homogeneous and heterogeneousweakly linear systems have been discussed in two recent papers. Here we give a brief overview of the main results from these two papers, as well as from a series of papers on applications of weakly linear systems in the state reduction of fuzzy automata, the study of simulation, bisimulation and equivalence of fuzzy automata, and in the social network analysis. Especially, we present algorithms for computing the greatest solutions to weakly linear systems.

Weakly linear quantum MV-algebras

2005 ◽  
Vol 53 (1) ◽  
pp. 45-72 ◽  
Author(s):  
Roberto Giuntini
Keyword(s):  
Mv Algebras ◽  
Weakly Linear ◽  
Linear Quantum

Distributed Reasoning and Planning

1993 ◽  
Author(s):  
Kurt G. Konolige
Keyword(s):  
Distributed Reasoning
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