Using Belief Theory to formalize the agent behavior : application to the simulation of avian flu propagation

• Main Authors: Taillandier, Patrick, Amouroux, Edouard, Vo, Duc-An, Olteanu, Ana-Maria
• Other Authors: Unité de modélisation mathématique et informatique des systèmes complexes Bondy (UMMISCO), Université Gaston Berger de Saint-Louis Sénégal (UGB)-Institut de Recherche pour le Développement (IRD)-Université Pierre et Marie Curie - Paris 6 (UPMC)-Université de Yaoundé I (UY1)-Institut de la francophonie pour l'informatique-Université Cadi Ayyad Marrakech (UCA)-Université Cheikh Anta Diop de Dakar Sénégal (UCAD), Conception Objet et Généralisation de l'Information Topographique (COGIT), Ecole nationale des sciences géographiques (ENSG), Institut géographique national IGN (IGN)-Institut géographique national IGN (IGN)
• Format: Conference Object
• Language: English
• Published: HAL CCSD 2010
• Subjects: multi-agent simulation; agent behavior formalization; belief theory; avian flu propagation; [INFO.INFO-MO]Computer Science [cs]/Modeling and Simulation; [INFO.INFO-MA]Computer Science [cs]/Multiagent Systems [cs.MA]; Avian flu
• Online Access: https://hal.science/hal-00691406; https://hal.science/hal-00691406/document; https://hal.science/hal-00691406/file/PRACSYS-2010_Taillandier_et_al.pdf; https://doi.org/10.1007/978-3-642-25920-3_42

International audience 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.