Abstract
All the scientific community cares about is understanding the complex systems, and explaining their emergent behaviors. We are interested particularly in Multi-Agent Systems (MAS). Our approach is based on three steps : observation, modeling and explanation. In this paper, we focus on the second step by offering a model to represent the cause and effect relations among the diverse entities composing a MAS. Thus, we consider causal reasoning of great importance because it models causalities among a set of individual and social concepts. Indeed, multiagent systems, complex by their nature, their architecture, their interactions, their behaviors, and their distributed processing, needs an explanation module to understand how solutions are given, how the resolution has been going on, how and when emergent situations and interactions have been performed. In this work, we investigate the issue of using causal maps in multi-agent systems in order to explain agent reasoning.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Hedhili, A.: Explication du raisonnement dans les systèmes multi-agents par l’observation. Report of Research Master degree. National School of Computer Studies, Tunis (2009)
Brahim, C.-D.: Causal Maps: Theory, Implementation and Practical Applications in Multiagent Environments. IEEE Transactions on Knowledge and Data Engineering 14, 1201–1217 (2002)
Basu, S., Biswas, G.: Multiple Representations to Support Learning of Complex Ecological Processes in Simulation Environments. In: Proceedings of the 19th International Conference on Computers in Education, Chiang Mai, Thailand (2011)
Daniel, M.: Emergence et niveaux d’explication. Journées thématiques de l’ARC (émergence et explication) (1996)
Dieng, R.: Explanatory Knowledge tools for expert systems. In: 2nd International Conference on Applications of A.I. to Engineering, Cambridge, M.A., USA (1987)
Druckenmiller, D.A., Acar, W.: Exploring agent-based simulation of causal maps: toward a strategic decision support tool. Doctoral Dissertation, Kent State University Kent, OH, USA (2005)
Spinelli, M., Schaaf, M.: Towards explanations for CBR-based applications. In: Hotho, A., Stumme, G. (eds.) Proceedings of the LLWA Workshop, Germany, pp. 229–233 (2003)
Nagel, K., Axhausen, K.W., Balmer, M., Meister, K., Rieser, M.: Agent-based simulation of travel demand, Structure and computational performance of MATSim-T. In: 2nd TRB Conference on Innovations in Travel Modeling, Portland (2008)
Ludwig, B., Schiemann, B., et al.: Self-describing Agents. Department of Computer Science 8. University Erlangen-Nuremberg (2008)
Swartout, W., Moore, J.: Explanation in second generation expert systems. In: David, J.-M., Krivine, J.-P., Simmons, R. (eds.) Second Generation Expert Systems, pp. 543–585. Springer (1993)
Lewis Johnson, W.: Agents that Explain Their Own Actions. In: Proceedings of the Fourth Conference on Computer Generated Forces and Behavioral Representation (1994)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2013 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Hedhili, A., Chaari, W.L., Ghédira, K. (2013). Causal Maps for Explanation in Multi-Agent System. In: Abraham, A., Thampi, S. (eds) Intelligent Informatics. Advances in Intelligent Systems and Computing, vol 182. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-32063-7_21
Download citation
DOI: https://doi.org/10.1007/978-3-642-32063-7_21
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-32062-0
Online ISBN: 978-3-642-32063-7
eBook Packages: EngineeringEngineering (R0)