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Communication Dans Un Congrès Année : 2017

Algorithms for Multi-criteria optimization in Possibilistic Decision Trees

Résumé

This paper raises the question of solving multi-criteria sequential decision problems under uncertainty. It proposes to extend to possibilistic decision trees the decision rules presented in [1] for non sequential problems. It present a series of algorithms for this new framework: Dynamic Programming can be used and provide an optimal strategy for rules that satisfy the property of monotonicity. There is no guarantee of optimality for those that do not - hence the definition of dedicated algorithms. This paper concludes by an empirical comparison of the algorithms.
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Dates et versions

hal-02639577 , version 1 (28-05-2020)

Identifiants

  • HAL Id : hal-02639577 , version 1
  • OATAO : 22073

Citer

Nahla Ben Amor, Fatma Essghaier, Hélène Fargier. Algorithms for Multi-criteria optimization in Possibilistic Decision Trees. Symbolic and Quantitative Approaches to Reasoning with Uncertainty - 14th European Conference, ECSQARU 2017, Jul 2017, Lugano, Switzerland. pp.295-305. ⟨hal-02639577⟩
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