Knowledge Modelling in Multiagent Systems: The Case of the Management of a National Network

This paper presents the knowledge model of a distributed decision support system, that has been designed for the management of a national network in Ukraine. It shows how advanced Artificial Intelligence techniques (multiagent systems and knowledge modelling) have been applied to solve this real-world decision support problem: on the one hand its distributed nature, implied by different loci of decision-making at the network nodes, suggested to apply a multiagent solution; on the other, due to the complexity of problem-solving for local network administration, it was useful to apply knowledge modelling techniques, in order to structure the different knowledge types and reasoning processes involved. The paper sets out from a description of our particular management problem. Subsequently, our agent model is described, pointing out the local problem-solving and coordination knowledge models. Finally, the dynamics of the approach is illustrated by an example.

Multiagent realization of prediction-based diagnosis and loss prevention

A multiagent diagnostic system implemented in a Protege-JADE-JESS environment interfaced with a dynamic simulator and database services is described in this paper. The proposed system architecture enables the use of a combination of diagnostic methods from heterogeneous knowledge sources. The process ontology and the process agents are designed based on the structure of the process system, while the diagnostic agents implement the applied diagnostic methods. A specific completeness coordinator agent is implemented to coordinate the diagnostic agents based on different methods. The system is demonstrated on a case study for diagnosis of faults in a granulation process based on HAZOP and FMEA analysis.

Analysis of P-Systems under a Multiagent Systems Perspective

Membrane computing is a recent area that belongs to natural computing. This field works on computational models based on nature's behavior to process the information. Recently, numerous models have been developed and implemented with this purpose. P-systems are the structures which have been defined, developed and implemented to simulate the behavior and the evolution of membrane systems which we find in nature. What we show in this paper is an application capable to simulate the P-systems based on a multiagent systems (MAS) technology. The main goal we want to achieve is to take advantage of the inner qualities of the multiagent systems. This way we can analyse the proper functioning of any given p-system. When we observe a P-system from a different perspective, we can be assured that it is a particular case of the multiagent systems. This opens a new possibility, in the future, to always evaluate the P-systems in terms of the multiagent systems technology.

Techniques for distributed theory synthesis in multiagent systems

Data sources are often dispersed geographically in real life applications. Finding a knowledge model may require to join all the data sources and to run a machine learning algorithm on the joint set. We present an alternative based on a Multi Agent System (MAS): an agent mines one data source in order to extract a local theory (knowledge model) and then merges it with the previous MAS theory using a knowledge fusion technique. This way, we obtain a global theory that summarizes the distributed knowledge without spending resources and time in joining data sources. New experiments have been executed including statistical significance analysis. The results show that, as a result of knowledge fusion, the accuracy of initial theories is significantly improved as well as the accuracy of the monolithic solution.