TOWARDS IMPROVING MULTI-AGENT SIMULATION IN SAFETY MANAGEMENT AND HAZARD CONTROL ENVIRONMENTS Dionisis Kechagias Andreas L. Symeonidis Department of Electrical and Computer Engineering Aristotle University of Thessaloniki & Intelligent Systems and Data Engineering Lab, Informatics and Telematics Institute/ Center for Research and Technology Hellas (ITI/CERTH) Thessaloniki, Greece Pericles A. Mitkas Department of Electrical and Computer Engineering Aristotle University of Thessaloniki & Intelligent Systems and Data Engineering Lab, Informatics and Telematics Institute/ Center for Research and Technology Hellas (ITI/CERTH) Thessaloniki, Greece Miguel Alborg IDI EIKON, Parque Tecnológico de Valencia, C/Benjamín Franklin, 27,46980 Paterna Valencia, Spain ABSTRACT This paper introduces the capabilities of Agent Academy in the area of Safety Management and Hazard Control Systems. Agent Academy is a framework under develop- ment, which uses data mining techniques for training in- telligent agents. This framework generates software agents with an initial degree of intelligence and trains them to manipulate complex tasks. The agents, are further integrated into a simulation multi-agent environment ca- pable of managing issues in a hazardous environment, as well as regulating the parameters of the safety manage- ment strategy to be deployed in order to control the haz- ards. The initially created agents take part in long agent- to-agent transactions and their activities are formed into behavioural data, which are stored in a database. As soon as the amount of collected data increases sufficiently, a data mining process is initiated, in order to extract specific trends adapted by agents and improve their intelligence. The result of the overall procedure aims to improve the simulation environment of safety management. The com- munication of agents as well as the architectural charac- teristics of the simulation environment adheres to the set of specifications imposed by the Foundation for Intelli- gent Physical Agents (FIPA). Keywords: agents, multi-agent systems, hazard control, data mining, Agent Academy 1 INTRODUCTION Intelligent agent (IA) technology promises to enable an enormous explosion of brand new computer-based ser- vices. The use of IAs introduces referencing capabilities to software, transforming computers into personal col- laborators that can provide active assistance and even take the initiative in decision-making processes. What makes an intelligent agent different from a typical computational procedure is the fact that intelligent agents posses among other characteristics, the ability to satisfy their goals and create new ones, based upon their former interaction with other agents. To accomplish this IAs must be capable of learning [MAE94], in order to modify their behaviour and make new decisions based on the certain goals, which they try to satisfy. For the purpose of learning, some tech- niques arisen from the area of Artificial Intelligence can be used. Keeping this in mind, we designed Agent Acad- emy (AA), a framework for training agents in order to make them “smarter”, using data mining (DM) tech- niques. The data mining approach introduces a new set of tools and methodologies for discovering knowledge and patterns in large databases [FAY98]. Mining information and knowledge has been recognized by many researchers as a key research topic in database systems and machine learning. Collecting, therefore, the content of messages exchanged between agents in a large data repository, has