Causal Maps for Explanation in Multi-Agent System
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.
KeywordsMulti-Agent Systems Explanation Reasoning Causal Maps
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- 1.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)Google Scholar
- 3.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)Google Scholar
- 4.Daniel, M.: Emergence et niveaux d’explication. Journées thématiques de l’ARC (émergence et explication) (1996)Google Scholar
- 5.Dieng, R.: Explanatory Knowledge tools for expert systems. In: 2nd International Conference on Applications of A.I. to Engineering, Cambridge, M.A., USA (1987)Google Scholar
- 6.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)Google Scholar
- 7.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)Google Scholar
- 8.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)Google Scholar
- 9.Ludwig, B., Schiemann, B., et al.: Self-describing Agents. Department of Computer Science 8. University Erlangen-Nuremberg (2008)Google Scholar
- 10.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)Google Scholar
- 11.Lewis Johnson, W.: Agents that Explain Their Own Actions. In: Proceedings of the Fourth Conference on Computer Generated Forces and Behavioral Representation (1994)Google Scholar