Artificial Intelligence
Constructing the Pignistic Probability Function in a Context of Uncertainty
UAI '89 Proceedings of the Fifth Annual Conference on Uncertainty in Artificial Intelligence
GAMA: An Environment for Implementing and Running Spatially Explicit Multi-agent Simulations
Agent Computing and Multi-Agent Systems
Using Participatory Paradigm to Learn Human Behaviour
KSE '09 Proceedings of the 2009 International Conference on Knowledge and Systems Engineering
Designing Agent Behaviour in Agent-Based Simulation through Participatory Method
PRIMA '09 Proceedings of the 12th International Conference on Principles of Practice in Multi-Agent Systems
PRIMA'10 Proceedings of the 13th international conference on Principles and Practice of Multi-Agent Systems
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Multi-agent simulations are powerful tools to study complex systems. However, a major difficulty raised by these simulations concerns the design of the agent behavior. Indeed, when the agent behavior is lead by many conflicting criteria (needs and desires), its definition is very complex. In order to address this issue, we propose to use the belief theory to formalize the agent behavior. This formal theory allows to manage the criteria incompleteness, uncertainty and imprecision. The formalism proposed divides the decision making process in three steps: the first one consists in computing the basic belief masses of each criterion; the second one in merging these belief masses; and the last one in making a decision from the merged belief masses. An application of the approach is proposed in the context of a model dedicated to the study of the avian flu propagation.