وحـدة : DECISION - Translate here
Axes : AID (👥👥), TMC (👥👥).
Patrice Perny Campus Pierre et Marie Curie 26-00/411
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
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.
(the webpages from this link are under the responsibility of the head of the team)
Decision-making under uncertainty and risk, multicriteria decision-making, group decision-making, preference modeling, optimization, graphs and algorithms, heuristic search, Bayesian nets, GAI nets.
- Ch. Gonzales, P. Perny, J.‑Ph. Dubus : “Decision Making with Multiple Objectives using GAI networks”, Artificial Intelligence, vol. 175 (7-8), pp. 1153-1179, (Elsevier) [Gonzales 2011a]
- G. Jeantet, O. Spanjaard : “Computing rank dependent utility in graphical models for sequential decision problems”, Artificial Intelligence, vol. 175 (7-8), pp. 1366-1389, (Elsevier) [Jeantet 2011]
- M. Minoux, H. Ouzia : “DRL*: A Hierarchy of Strong Block-Decomposable Linear Relaxations for 0-1 MIPs”, Discrete Applied Mathematics, vol. 158 (18), pp. 2031-2048, (Elsevier) [Minoux 2010b]
- J. Lesca, M. Minoux, P. Perny : “Compact versus Noncompact LP Formulations for minimizing Convex Choquet Integrals”, Discrete Applied Mathematics, vol. 161 (1-2), pp. 184-199, (Elsevier) [Lesca 2013]
- L. Galand, P. Perny, O. Spanjaard : “Choquet-based optimisation in multiobjective shortest path and spanning tree problems”, European Journal of Operational Research, vol. 204 (2), pp. 303-315, (Elsevier) [Galand 2010]
- J.‑F. Maurras, Th. Nguyen, V. Nguyen : “On the linear description of the Huffman trees polytope”, Discrete Applied Mathematics, vol. 164 (1), pp. 225-236, (Elsevier) [Maurras 2014]
- P.‑H. Wuillemin, L. Torti : “Structured Probabilistic Inference”, International Journal of Approximate Reasoning, vol. 53 (7), pp. 946-968, (Elsevier) [Wuillemin 2012]
- M. Borges, P. Brézillon, J. Pino, J.‑Ch. Pomerol : “Dealing with the effects of context mismatch in group work”, Decision Support Systems, vol. 43 (4), pp. 1692-1706, (Elsevier) [Borges 2007]
- I. Alvarez, S. Bernard, G. Deffuant : “Keep the Decision Tree and Estimate the Class Probabilities using its Decision Boundary”, Proceedings of the 20th International Joint Conference on Artificial Intelligence 2007, Hyderabad, India, pp. 654-659 [Alvarez 2007]
- P. Weng : “Markov Decision Processes with Ordinal Rewards: Reference Point-Based Preferences”, International Conference on Automated Planning and Scheduling, vol. 21, Freiburg, Germany, pp. 282-289 [Weng 2011]