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Design of a Participatory Decision Making Agent Architecture Based on Argumentation and Influence Function – Application to a Serious Game about Biodiversity Conservation

Alessandro Sordoni, Jean-Pierre Briot, Isabelle Alvarez, Eurico Vasconcelos, Marta de Azevedo Irving, Gustavo Melo (2010)

RAIRO - Operations Research

This paper addresses an ongoing experience in the design of an artificial agent taking decisions and combining them with the decisions taken by human agents. The context is a serious game research project, aimed at computer-based support for participatory management of protected areas (and more specifically national parks) in order to promote biodiversity conservation and social inclusion. Its objective is to help various stakeholders (e.g., environmentalist, tourism operator) to collectively understand...

Evolving small-board Go players using coevolutionary temporal difference learning with archives

Krzysztof Krawiec, Wojciech Jaśkowski, Marcin Szubert (2011)

International Journal of Applied Mathematics and Computer Science

We apply Coevolutionary Temporal Difference Learning (CTDL) to learn small-board Go strategies represented as weighted piece counters. CTDL is a randomized learning technique which interweaves two search processes that operate in the intra-game and inter-game mode. Intra-game learning is driven by gradient-descent Temporal Difference Learning (TDL), a reinforcement learning method that updates the board evaluation function according to differences observed between its values for consecutively visited...

GTES : une méthode de simulation par jeux et apprentissage pour l'analyse des systèmes d'acteurs

Y. Caseau (2009)

RAIRO - Operations Research

Cet article décrit une approche de la modélisation d'un système d'acteurs, particulièrement adaptée à la modélisation des entreprises, fondée sur la théorie des jeux [11] et sur l'optimisation par apprentissage du comportement de ces acteurs. Cette méthode repose sur la combinaison de trois techniques : la simulation par échantillonnage (Monte-Carlo), la théorie des jeux pour ce qui concerne la recherche d'équilibre entre les stratégies, et les méthodes heuristiques d'optimisation locale,...

On a dual network exterior point simplex type algorithm and its computational behavior

George Geranis, Konstantinos Paparrizos, Angelo Sifaleras (2012)

RAIRO - Operations Research - Recherche Opérationnelle

The minimum cost network flow problem, (MCNFP) constitutes a wide category of network flow problems. Recently a new dual network exterior point simplex algorithm (DNEPSA) for the MCNFP has been developed. This algorithm belongs to a special “exterior point simplex type” category. Similar to the classical dual network simplex algorithm (DNSA), this algorithm starts with a dual feasible tree-solution and after a number of iterations, it produces a solution that is both primal and dual feasible, i.e....

On a dual network exterior point simplex type algorithm and its computational behavior∗

George Geranis, Konstantinos Paparrizos, Angelo Sifaleras (2012)

RAIRO - Operations Research

The minimum cost network flow problem, (MCNFP) constitutes a wide category of network flow problems. Recently a new dual network exterior point simplex algorithm (DNEPSA) for the MCNFP has been developed. This algorithm belongs to a special “exterior point simplex type” category. Similar to the classical dual network simplex algorithm (DNSA), this algorithm starts with a dual feasible tree-solution and after a number of iterations, it produces a...

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