Constrained optimization under uncertainty for decision-making problems: Application to Real-Time Strategy games - Archive ouverte HAL
Conference Papers Year : 2019

Constrained optimization under uncertainty for decision-making problems: Application to Real-Time Strategy games

Abstract

Decision-making problems can be mod-eled as combinatorial optimization problems with Constraint Programming formalisms such as Constrained Optimization Problems. However, few Constraint Programming formalisms can deal with both optimization and uncertainty at the same time, and none of them are convenient to model problems we tackle in this paper. Here, we propose a way to deal with combinatorial optimization problems under uncertainty within the classical Constrained Optimization Problems formalism by injecting the Rank Dependent Utility from decision theory. We also propose a proof of concept of our method to show it is implementable and can solve concrete decision-making problems using a regular constraint solver, and propose a bot that won the partially observable track of the 2018 µRTS AI competition. Our result shows it is possible to handle uncertainty with regular Constraint Programming solvers, without having to define a new formalism neither to develop dedicated solvers. This brings new perspective to tackle uncertainty in Constraint Programming.
Fichier principal
Vignette du fichier
microrts_uncertainty.pdf (514.54 Ko) Télécharger le fichier
Origin Files produced by the author(s)
Loading...

Dates and versions

hal-02108090 , version 1 (24-04-2019)

Identifiers

Cite

Valentin Antuori, Florian Richoux. Constrained optimization under uncertainty for decision-making problems: Application to Real-Time Strategy games. IEEE Congress on Evolutionary Computation, 2019, Wellington, New Zealand. ⟨10.1109/cec.2019.8789922⟩. ⟨hal-02108090⟩
67 View
415 Download

Altmetric

Share

More