Chiara F. Sironi

General Video Game Playing (GVGP) is a field of Artificial Intelligence where agents play a variety of real-time video games that are unknown in advance. This limits the use of domain-specific heuristics. Monte-Carlo Tree Search (MCTS) is a search technique for game playing that does not rely on domain-specific knowledge. This paper discusses eight...

Monte-Carlo Tree Search (MCTS) has been applied successfully in many domains, including games. However, its performance is not uniform on all domains, and it also depends on how parameters that control the search are set. Parameter values that are optimal for a task might be sub-optimal for another. In a domain that tackles many games with differen...

Playout Policy Adaptation (PPA) is a state-of-the-art strategy that has been proposed to control the playouts in Monte-Carlo Tree Search (MCTS). PPA has been successfully applied to many two-player, sequential-move games. This paper further evaluates this strategy in General Game Playing (GGP) by first reformulating it for simultaneous-move games....

Bien que les systèmes actuels de General Game Playing (GGP) facilitent la recherche en Intelligence Artificielle (IA) autour des jeux, ils sont souvent trop spécialisés et fournissent une capacité de calcul trop faible. Cet article décrit une première version du système ludémique de GGP dénommé LUDII qui apporte un outil efficace à la recherche en...

While current General Game Playing (GGP) systems facilitate useful research in Artificial Intelligence (AI) for game-playing, they are often somewhat specialized and computationally inefficient. In this paper, we describe an initial version of a "ludemic" general game system called Ludii, which has the potential to provide an efficient tool for AI...

Many enhancements for Monte-Carlo Tree Search (MCTS) have been applied successfully in General Game Playing (GGP). MCTS and its enhancements are controlled by multiple parameters that require extensive and time-consuming off-line optimization. Moreover, as the played games are unknown in advance, off-line optimization cannot tune parameters specifi...

Monte-Carlo Tree Search (MCTS) has shown particular success in General Game Playing (GGP) and General Video Game Playing (GVGP) and many enhancements and variants have been developed. Recently, an on-line adaptive parameter tuning mechanism for MCTS agents has been proposed that almost achieves the same performance as off-line tuning in GGP.

Many enhancements have been proposed for Monte-Carlo Tree Search (MCTS). Some of them have been applied successfully in the context of General Game Playing (GGP). MCTS and its enhancements are usually controlled by multiple parameters that require extensive and time-consuming computation to be tuned in advance. Moreover, in GGP optimal parameter va...

Monte-Carlo Tree Search (MCTS) has shown particular success in General Game Playing (GGP) and General Video Game Playing (GVGP) and many enhancements and variants have been developed. Recently, an on-line adap-tive parameter tuning mechanism for MCTS agents has been proposed that almost achieves the same performance as off-line tuning in GGP. In th...

General Game Playing (GGP) programs need a Game Description Language (GDL) reasoner to be able to interpret the game rules and search for the best actions to play in the game. One method for interpreting the game rules consists of translating the GDL game description into an alternative representation that the player can use to reason more efficien...