The Decision team works on the development of formal models and algorithms for decision-making and for optimization in complex environments (uncertainty and risk, multicriteria decision-making, collective decision-making, context) as well as the development of decision-aiding systems. Our research deals with on the one hand the elaboration or the analysis of sophisticated models to take into account complex decision behaviors and on the other hand the conception of optimization algorithms allowing the preferred solutions to be determined on discrete or continuous domains. The potential applications are decision-aiding systems (rational preparation of important decisions, recommending systems on the web), automatic decision-making (autonomous decision agents) and optimization in large systems (telecommunication, transport, energy).

The main research themes of the Decision team:

• Preference and belief modeling under uncertainty and risk
• Multicriteria aggregation, preference aggregation for collective decision-making
• Heuristic search in state graphs for decision-making
• Stochastic optimization and robust optimization models
• Decomposition methods for optimization in large systems
• Algebraic models for decision-aiding
• Graphical models for reasoning and decision-making (Bayesian nets and GAI nets)
• Context-based decision-making and explanation

Positioning:

1. The competences of the team cover the whole range of decision problems from the development of theoretical models and their mathematical justification, to real applications, through problem modeling and the development of algorithms and decision systems.
2. Our works, due to the formal tools they use and combine but also due to the addressed problems, deal with Operations research as well Artificial Intelligence and the team publishes in both communities.