Novel Ontology Model for Communicating Heterogeneous Negotiation Mobile-Agent in a Transport Environment
Sawsan SAAD, Hayfa ZGAYA
LAGIS UMR 8146, Ecole Central de Lille, France
LAGIS UMR 9146, Ecole Central de Lille, France
Abstract: In this paper, we address the problem of negotiation process in a multi-agents system by using ontologies. Therefore, we present an ontology solution based on the knowledge management system for semantic heterogeneity. The proposed solution prevents the misunderstanding during the negotiation process through the agents’ communications. Our approach aims to enable agents able to understand each other when using these ontologies. Thus, we propose a general architecture for Negotiation process which uses Ontology-based Knowledge Management System (NOKMS). This architecture consists of three layers: the Negotiation Layer (NL) that describes the negotiation process between the Initiator Static Agents (ISAs) and the Participant Mobile Agents (PMAs) by using suitable ontologies, the Semantic Layer (SEL) contains the semantic translator which uses in the case of misunderstanding of the sent messages between the agents, and the last one is the Knowledge Management Systems Layer (KMSL) which bases on the Intelligent Knowledge Base (IKB) to give the flexibility to our negotiation ontology. In addition, we will illustrate an agent architecture which helps our architecture on applying the different operations in the different layers. Finally, we present a case study which applies our architecture on the Multimodal Transport Information System (MTIS) project where we will show two scenarios applicable: the first uses our negotiation ontology architecture in one transport system, and the second applies this architecture on the multi-transport systems. These case studies show that the proposed NOKMS improves the execution of negotiation process in multi-agents systems in order to satisfy the transport customers.
Keywords: Multi-Agents Systems, Negotiation, Ontology, Knowledge Management System, Transport Information System.
Sawsan Saad is currently a doctorate in LAGIS laboratory at the high France School, Ecole Centrale de Lille (EC-Lille) in the Optimization, the Artificial Intelligence and the Logistic field. Born in Salamieh (Syria) in 1979. She received the BSc degree in Computer Engineering from Al-Baath University (Syria) in 2003. She obtained her MSc degree in Information Systems in 2007 from INSA de Lyon (France). She was member of the Organizing Committees of international congress on “Logistic and Transport” LT’2007.
Dr. Hayfa Zgaya is teacher-researcher in Computing Science in LAGIS laboratory within the high France School Ecole Centrale de Lille (EC-Lille) where she obtained her PhD degree in July 2007. Her main research areas are the Optimization, the Artificial Intelligence and the Logistic field. Born in Tunis in 1978, she obtained her master’s degree in computer science in November 2001 from the France polytechnic high school of Nantes University. She takes an active part in the national working group ORT of GDR MACS and she was responsible of the Organizing Committees of international workshops MHOSI’ 2005, LT’2006 and LT’2007.
Slim Hammadi is a full Professor of production planning and control at the Ecole Centrale de Lille (French “Grande Ecole”). Born in Gafsa (Tunisia) in 1962, he has obtained by 1988 the Master degree in Computer science from the University of Lille (France). Pr Hammadi obtained a P.h.D degree in job-shop scheduling and control in 1991 at Ecole Centrale de Lille. He is a senior member of IEEE/SMC and has served as a referee for numerous journals including the IEEE Transactions on SMC. Pr. S. Hammadi was Co-Organizer of a Symposium (IMS) of the IMACS/IEEE SMC Multi conference CESA’98 held in Hammamet (Tunisia) in April 1998. He has organized several invited sessions in different SMC conferences where he was session chairman. He was chairman of the International congress on “Logistic and Transport” LT’2004, MHOSI’2005, LT’2006 and LT’2007. His teaching and research interests focus on the areas of production control, production planning, computer science, discrete and dynamic programming and computer integrated manufacturing.
CITE THIS PAPER AS:
Sawsan SAAD, Hayfa ZGAYA, Slim HAMMADI, Novel Ontology Model for Communicating Heterogeneous Negotiation Mobile-Agent in a Transport Environment, Studies in Informatics and Control, ISSN 1220-1766, vol. 17 (4), pp. 333-352, 2008.
This work belongs to the French national project VIATIC.MOBILITE from the industrial cluster I-TRANS “France highlights leading-edge technology in rail systems and innovative transport” . In fact; it has become much more important for public transportation companies to provide an adequate Level Of Service to their customers. This is due to the growing competition in the public transport market and also to the privatization of the transport companies. The great difficulty related to the traffic management in such systems is allied to the respect of the planned departure and arrival times of the vehicles at the different stops of the network. In fact, many incidents can occur and force a customer to wait longer, which decreases the level of service. Hence, operational decisions have to be taken in real-time by human decision makers, called regulators. However, these operators are overloaded with information that they have to treat immediately in order to find the relevant decisions that result in new vehicle schedules. Decision Support Systems (DSS) are computer technology solutions that can be used to support complex decision making and problem solving. Decision making is the study of how decisions are actually taken and how they can be better or more successfully taken. In order to realize the necessity of a DSS for a Transportation System, it is important to grasp the problems related to a real-time management of the traffic .
In our Multimodal Transport Information Syste (MTIS)  project, we presented a Multi-Agent Decision Support Systems that provides the regulators with the relevant decisions to undertake in case of disturbances. In fact, scheduling can be defined as a problem of finding the optimal sequence for executing a finite set of operations under a certain set of constraints which must be satisfied. A scheduler usually attempts to maximize the utilization of individuals and / or machinery and minimize the time required to complete the entire process being scheduled. Therefore the scheduling problem is very hard to solve . A Genetic Algorithms (GAs) have been used to solve this problems in our MTIS. The proposed multi-agent system is based on metaheuristics for the research and the composition of the services; services research is based on the Mobile Agent paradigm (MA) using this dynamic optimization algorithm for the MA Workplans design. The first step of optimization prepares the MA routes, taking into account the network state. The services composition uses evolutionary algorithms to optimize the responses in terms of costs and delays, knowing that a response to a user request must respect a fixed due date with a reasonable cost. We also designed and optimized the management of the data flow of the users’ requests, which can be simultaneous and numerous. We developed also a negotiation protocol intended for the transport area which permits the agents to negotiate when perturbations may exist and as a result the system needs to reassign news nodes. The negotiation protocol uses messages to exchange the information. Those messages are exchanged between the Scheduler Agents (SAs), representing the initiators of the negotiation, and the Intelligent Collector Agents (ICAs), representing the participants of the negotiation. This protocol has studied before only the cases of the simple messages and it proposed ontology without illustrating it, and this later didn’t include the solutions when the agents ICAs did not understand the messages sent from the SA agent In this paper, we propose an approach that aims to improve the protocol of the negotiation of the multi-agents systems which has been proposed in the previous work ; we present an ontology solution based on the knowledge management system for semantic heterogeneity. The proposed solution prevents the misunderstanding during the negotiation through the agents’ communications. Our approach aims to make the agents able to understand each other when using these ontologies.
The rest of this paper is organized as follows: firstly, we discuss some related work (Section 2). Then, we discuss the role of the ontology in multi-agent systems (Section 3), ontologies and their combination problems will present in (Section 4). And we will present a negotiation ontology based on knowledge management system proposal (Section 5). An agent architecture based knowledge model (Section 6), and a real transport case study is illustrated in (Section 7); finally conclusion and future work are presented (Section 8).
8. Conclusion and Future Works
In this paper we have presented a new solution for the problem of language interoperability between negotiation agents, by incorporating architecture for Negotiation process which uses an Ontology-based Knowledge Management System (NOKMS). The proposed solution prevent the misunderstanding during the negotiation process though the agents’ communications. The architecture consists of three layers: the Negotiation Layer (NL) that describes the negotiation process between Initiator agents and Participant Agents by using suitable ontologies, the Semantic Layer (SEL) contains the semantic translator which uses in the case of when the agents didn’t understand the negotiation messages , and the last one is the Knowledge Management Systems Layer (KMSL) which base on the Intelligent Knowledge Base (IKB) to give the flexibility to our negotiation ontology. Finally, we present a case study which applies our architecture on the Multimodal Transport Information System (MTIS) project where we illustrated two scenarios applicable: the first use our negotiation ontology architecture in one transport system (French transport system), and the second apply this architecture on the multi-transport systems (French and English transport systems). These scenarios presented that the proposed NOKMS improves the communications between heterogeneous of negotiation process in multi-agents systems in order to satisfy the transport customers.
In this paper, we have presented only the different ontology combinations problems but in the future, we will try to find a novel method for ontology (mepping, merging, alignment) negotiation selon our project, in which agents are able to achieve. We will try to implement this architecture by using Java Agent DEvelopment framework (JADE) which includes a proficient support for content languages and ontologies.
- ZGAYA, H., SLIM HAMMADI, Conception et distribuée d’un système d’information d’aid à la mobilité urbaine: Une approche multi-agent pour la recherche et la composition des services liés au transport, PHD, EC-Lille, Lille, France, July 6, 2007.
- ZGAYA H., and S. HAMMADI, Multi-Agent Information System Using Mobile Agent Negotiation Based on a Flexible Transport Ontology, In Proceedings of the 2007 International Conference on Autonomous Agents and Multi-agent Systems (AAMAS’2007). May 14-18, Honolulu, Hawaii, 2007.
- ABOU ASSALI, A., D. LENNE and B. DEBRAY: KoMIS: An Ontology-Based Knowledge Management System For Industrial Safety. (Database and Expert Systems Applications DEXA’2007), Regensburg, Germany, 3-7 September, 2007.
- DIGGELEN, J. VAN, BEUN, R.J., DIGNUM, F.P.M., EIJK, R.M. VAN, MEYER, J-J. CH. Ontology Negotiation in Heterogeneous Multi-agent Systems: the ANEMONE System, IOS Press 2007.
- SH. JI, T. QIJIA, Y. LIANG, H.YANG, An Ontology Framework for EC Automated Negotiation Protocol, Networking, and Parallel / Distributed Computing, 2007. SNPD 2007, July 30, 2007 – Aug. 2007.
- SCHIEMANN, B., OWL DL as a FIPA ACL content language, Ulf Schreiber Artificial Intelligence Division, Department of Computer Science, 2006.
- DAVIES, J., R. STUDER, P. WARREN, Semantic Web Technologies: Trends and Research in Ontology-based Systems, April 2006.
- LACLAVIK, M., Ontology and Agent based Approach for Knowledge Management PhD, Institute of Informatics, Slovak Academy of Sciences, 2006.
- DIGGELEN, J. VAN, BEUN, R.J., DIGNUM, F.P.M., EIJK, R.M. VAN, MEYER, J-J.CH. Optimal Communication Vocabularies and Heterogeneous Ontologies, IOS Press 2005.
- BRAVO, M.C., J. PEREZ, V.J. SOSA, A. MONTES, G .REYES, Ontology support for communicating agents in negotiation processes, Hybrid Intelligent Systems, 6-9 November, 2005.
- ZGAYA, H., S. HAMMADI, K. GHÉDIRA, Evolutionary method to optimize Workplan mobile agent for the transport network application, In Proceedings of International Conference on Systems, Man and Cybernetics (IEEE SMC’2005), Hawaii, USA, October 10-12, 2005.
- ZGAYA, H., S. HAMMADI, K. GHÉDIRA, Workplan Mobile Agent for the Transport Network Application, In Proc. of 17th IMACS World Congress Scientific Computation, Applied Mathematics and Simulation (IMACS’2005), Paris, France, July 11-15, 2005.
- SRIDHARAN, B., A. TRETIAKOV and KINSHUK, Application of Ontology to Knowledge Management in Web based Learning, ICALT 2004.
- SINTEK, M., OntoViz Tab: Visualizing Protégé Ontologies, 2004, http://protege.stanford.edu/plugins/ontoviz/ontoviz.html
- OBITKO, V., MARÍK, OWL Ontology Agent based on FIPA proposal, Znalosti; 2004, Brno, Czech Republic, 2004.
- JADE – Java Agent Development Framework, http://jade.tilab.com/, Accessed July 2004.
- RAZMERITA, L., A. ANGEHRN and A. MAEDCHE, Ontology-Based User Modeling for Knowledge Management Systems, DOCIS, 2003.
- MATHIEU, M., H. VERRONS, ANTS: an API for creating negotiation applications, Conf. CE2003.
- BAADER, F., D. MCGUINNESS, D. NARDI, The Description Logic Handbook, ISBN: 0521781760, January 9, 2003.
- BORNE; P., B. FAYECH; S. HAMMADI, S. MAOUCHE, Decision support system for urban transportation networks, Systems, Man, and Cybernetics, Part C: Applications and Reviews, IEEE Transactions on Volume 33, Issue 1, Feb. 2003, pp. 67 – 77.
- TAMMA, V., and T.J.M., An ontology model to facilitate knowledge sharing in multi-agent systems, In Knowledge Engineering Review, 17(1), Bench-Capon, 2002, pp. 41-60.
- Pellucid Consortium, Pellucid IST Project Website, http://www.sadiel.es/ Europa/ pellucid/, 2002.
- University of Amsterdam, Ontology Bean Generator for JADE, http://www.swi.psy.uva.nl/usr/aart/beangenerator/ 2002
- FENSEL, D., Ontologies: Silver Bullet for Knowledge Management and Electronic Commerce, 2001.
- KLEIN, M., Combining and relating ontologies: an analysis of problems and solutions, In IJCAI-2001 Workshop on Ontologies and Information Sharing, Seattle, WA, 2001, pp. 53-62.
- STUDER, R., V. BENJAMINS, and D. FENSEL, Knowledge engineering, principles and methods, Data and Knowledge Engineering, 25(1-2):161-197, 1998.
- MESGHOUNI, K., S. HAMMADI and P. BORNE, On modeling genetic algorithm for flexible job-shop scheduling problem, Studies in Informatics and Control Journal, Vol. 7, 1998, pp. 37-47.
- FALASCONI, S., G. LANZOLA, and M. STEFANELLI, Using ontologies in multi-agent systems, In Proceedings of Tenth Knowledge Acquisition for Knowledge-Based Systems Workshop (KAW), Banff, Alberta, Canada, 1996.
- GEIGER, K., Inside ODBC. Microsoft Press, 1995.
- GRUBER, TH. R., A Translation Approch to Portable Ontology Specification, Knowledge Acquisition, April, 1993.
- FIPA0081: FIPA ACL Message Structure Specification, http://www.fipa.org/specs/fipa00061/index.html .
- FIPA0008: FIPA SL Content Language Specification, http://www.fipa.org/specs/fipa00008/index.htm.
- FIPA, Foundation for Intelligent Physical Agents website, 2002, http://www.fipa.org/
- FIPA. FIPA0086: FIPA Ontology Service Specification, http://www.fipa.org/specs/fipa00086/index.html