A multi-agent system for web-based risk management in small and medium business

Abstract

Business Intelligence has gained relevance during the last years to improve business decision making. However, there is still a growing need of developing innovative tools that can help small to medium sized enterprises to predict risky situations and manage inefficient activities. This article present a multiagent system especially conceived to detect risky situations and provide recommendations to the internal auditors of SMEs. The core of the multiagent system is a type of agent with advanced capacities for reasoning to make predictions based on previous experiences. This agent type is used to implement an evaluator agent specialized in detect risky situations and an advisor agent aimed at providing decision support facilities. Both agents incorporate innovative techniques in the stages of the CBR system. An initial prototype was developed and the results obtained related to small and medium enterprises in a real scenario are presented.

Self-Organizing Architecture for Information Fusion in Distributed Sensor Networks

“…This procedure is similar to the one used for the mixture of experts [32,33]. The layer incorporates different statistical techniques based on linear programming [34], neural networks [32], and classifiers [34]. Thus, specialized agents in information fusion can combine different sensing technologies that provide heterogeneous data and provide more accurate information to the upper layer services.…”

“…For this purpose, roles that allow merging information automatically through supervised learning and previous training are included. This procedure is similar to the one used for the mixture of experts [32,33]. The layer incorporates different statistical techniques based on linear programming [34], neural networks [32], and classifiers [34].…”

“…A complete description of PANGEA can be found in [31]. The new architecture is organized in 4 layers as can be seen in Figure 1 and is developed according to the classification of the JDL fusion model [31,32]. The platform will facilitate the integration of sensing technologies, regardless of their nature, and will provide an openness environment that allows dynamic [10,[29][30][31].…”

Generating project risk response strategies based on CBR: A case study

“…Over the decades, CBR was widely applied in various areas such as medicine (El-Fakdi, Gamero, Meléndez, Auffret, & Haigron, 2014;Guessoum, Laskri, & Lieber, 2014;Ting, Wang, Kwok, Tsang, & Lee, 2010;Zhuang, Churilov, Burstein, & Sikaris, 2009), manufacturing industry (Kuo, 2010;Wu, Lo, & Hsu, 2008) and business (Carmona, Barbancho, Larios, & León, 2013;Li, Adeli, Sun, & Han, 2011), etc. It can been found that there are some studies on risk management based on CBR (Aarts, 1998;Bajo, Borrajo, De Paz, Corchado, & Pellicer, 2012;Chang, Ma, Song, & Gao, 2010;Dingwei & Xinping, 2011;Goh & Chua, 2009;Jung, Han, & Suh, 1999;Kumar & Viswanadham, 2007;Li, Yu, Zhou, & Cai, 2013;Lu, Li, & Xiao, 2013;Yao, Chen, & Yang, 2014). For example, Kumar and Viswanadham (2007) develop a CBR-based framework of the decision support system to support the risk management of construction supply chains.…”

Unified Fingerprinting/Ranging Localization in Harsh Environments

“…The virtual organization of agents manages the resources of the Cloud system in which it is deployed. It was created with the PANGEA platform that facilitates the development of agents in light devices and the integration of different hardware [41,42]. The architecture is organized in 4 layers as can be seen in Figure 1 and that is what we briefly describe here for completeness [40].…”