Laboratoire de Génie Informatique et d’Automatique de l’Artois

Yu-Lin HUANG

Research engineer
(Left the LGI2A in 2021)
Member of the research themes:

Conférence Internationale avec Comité de Lecture

2021
International conference with review committee
SPSC: an efficient, general-purpose execution policy for stochastic simulations
Winter Simulation Conference 2021, Phoenix, AZ, United States, 12/2021
SPSC: a new execution policy for exploring discrete-time stochastic simulations
Proceedings of the 22nd International Conference on Principles and Practice of Multi-Agent Systems, PRIMA 2019, pp 568-575, Torino, Italy, 10/2019

Conférence Nationale avec Comité de Lecture

Détection d’événements rares dans les simulations multi-agents
Actes des 26èmes Journées Francophones sur les Systèmes Multi-Agents , JFSMA 2018, Métabief, France, Cépaduès, 10/2018

Author of the Ph.D. thesis "A new policy for running stochastic simulations based on partitioning, selection and cloning principles."

2017 - 2021

Stochastic simulations are widely used in application domains where no deterministic laws can be used to predict the futur state of the system.
The results of such simulations being variable by nature, it is then mandatory to replicate the simulation execution in order to retreive exeprimentally the distribution of possible eventes and then obtain their probabilities. In the general case, the Monte-Carlo method is classically used. Nevertheless, if computing resources are limited or targeted events are rare, MC can be inefficient.

In this thesis, we introduce a new simulation execution policy based on the clustering, selection and cloning of replications states, in order to improve the result quality for a fixed computing cost while being as generic as possible. The general idea of this policy is to constraint the evolution of the replications by periodically selecting and cloning those potentially leading to interesting results for the decision maker.

The policy is validated on simple academic agent-based models (prey predator, and virus transmission), then experimented on a more complex traffic simulation. A probabilistic variant of the policy is finally presented.

(*) The ELSAT2020 project is co-financed by the European Union with the European Regional Development Fund, the French state and the Hauts de France Region Council.