Distributed Reasoning

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.

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Distributed reasoning with coupled ontologies: the $$E\text {-}{\mathcal {SHIQ}}$$ E - SHIQ representation framework

Knowledge and Information Systems ◽

10.1007/s10115-014-0807-2 ◽

2014 ◽

Vol 45 (2) ◽

pp. 491-534 ◽

Cited By ~ 3

Author(s):

Georgios Santipantakis ◽

George A. Vouros

Keyword(s):

Distributed Reasoning

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A COLLABORATIVE APPROACH TO CONSTRUCTION OF LARGE SCALE DISTRIBUTED REASONING SYSTEMS

International Journal of Artificial Intelligence Tools ◽

10.1142/s021821301100053x ◽

2011 ◽

Vol 20 (06) ◽

pp. 1083-1106 ◽

Cited By ~ 3

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.

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