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.

Abstract.A novel hybrid artificial intelligent system for Intrusion Detection, called MOVIH-IDS, is presented in this study. A hybrid model built by means of a multiagent system that incorporates an unsupervised connectionist Intrusion Detection System (IDS) has been defined to guaranty an efficient computer network security architecture. This hybrid IDS facilitates the intrusion detection in dynamic networks, in a more flexible and adaptable manner. The proposed improvement of the system in this paper includes deliberative agents characterized by the use of an unsupervised connectionist model to identify intrusions in computer networks. This hybrid IDS has been probed through several real anomalous situations related to the Simple Network Management Protocol as it is potentially dangerous. Experimental results probed the successful detection of such attacks through MOVIH-IDS.

A multiagent system that incorporates an Artificial Neural Networks based Intrusion Detection System (IDS) has been defined to guaranty an efficient computer network security architecture. The proposed system facilitates the intrusion detection in dynamic networks. This paper presents the structure of the Mobile Visualization Connectionist Agent-Based IDS, more flexible and adaptable. The proposed improvement of the system in this paper includes deliberative agents that use the artificial neural network to identify intrusions in computer networks. The agent based system has been probed through anomalous situations related to the Simple Network Management Protocol.

Firms need a control mechanism in order to analyse whether they are achieving their goals. A tool for the decision support process has been developed on the basis of a multi-agent system that incorporates a casebased reasoning (CBR) system and automates the business control process. The CBR system automates the organization of cases and the retrieval stage by means of a maximum likelihood Hebbian learning-based method, an extension of the principal component analysis that groups similar cases, identifying clusters automatically in a data set in an unsupervised mode. The system has been tested in 10 small and medium companies in the textile sector, located in the northwest of Spain and the results obtained have been very encouraging.

The paper presents an agent-based engineering system developed for mobile devices. The proposed system has been used for constructing a wireless tourist guide application that incorporates cooperative agents with the learning capabilities. It is shown how to construct cooperative agents with a goal driven design using a case-based reasoning methodology. The resulting architecture has been tested by real users during six months and the results obtained are here presented.