Optimizing Propositional Networks

Sironi, Chiara F.; Winands, Mark H. M.

doi:10.1007/978-3-319-57969-6_10

Cited by 14 publications

(17 citation statements)

References 6 publications

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“…GDL (Love et al, 2008) is a low-level logic-based game description language, where games are described as logic programs consisting of many low-level propositions. Many GDL-based agents convert such a GDL description into a propositional network (Schkufza et al, 2008;Cox et al, 2009;Sironi and Winands, 2017), which can more efficiently process the games than Prolog-based reasoners or other similar techniques. Such propositional networks can be automatically constructed from GDL descriptions, and the structure of such a network remains constant across all game states of the same game.…”

Section: Deep Learning In General Game Playingmentioning

confidence: 99%

Deep learning for general game playing with Ludii and Polygames

Soemers

Mella²,

Browne

et al. 2022

ICG

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Combinations of Monte-Carlo tree search and Deep Neural Networks, trained through self-play, have produced state-of-the-art results for automated game-playing in many board games. The training and search algorithms are not game-specific, but every individual game that these approaches are applied to still requires domain knowledge for the implementation of the game’s rules, and constructing the neural network’s architecture – in particular the shapes of its input and output tensors. Ludii is a general game system that already contains over 1,000 different games, which can rapidly grow thanks to its powerful and user-friendly game description language. Polygames is a framework with training and search algorithms, which has already produced superhuman players for several board games. This paper describes the implementation of a bridge between Ludii and Polygames, which enables Polygames to train and evaluate models for games that are implemented and run through Ludii. We do not require any game-specific domain knowledge anymore, and instead leverage our domain knowledge of the Ludii system and its abstract state and move representations to write functions that can automatically determine the appropriate shapes for input and output tensors for any game implemented in Ludii. We describe experimental results for short training runs in a wide variety of different board games, and discuss several open problems and avenues for future research.

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Section: Deep Learning In General Game Playingmentioning

confidence: 99%

Deep learning for general game playing with Ludii and Polygames

Soemers

Mella²,

Browne

et al. 2022

ICG

View full text Add to dashboard Cite

show abstract

“…GDL [16] is a low-level logic-based game description language, where games are described as logic programs consisting of many low-level propositions. Many GDL-based agents convert such a GDL description into a propositional network [23,7,28], which can more efficiently process the games than Prolog-based reasoners or other similar techniques. Such propositional networks can be automatically constructed from GDL descriptions, and the structure of such a network remains constant across all game states of the same game.…”

Section: Deep Learning In General Game Playingmentioning

confidence: 99%

Deep Learning for General Game Playing with Ludii and Polygames

Soemers¹,

Mella²,

Browne³

et al. 2021

Preprint

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Combinations of Monte-Carlo tree search and Deep Neural Networks, trained through self-play, have produced state-of-the-art results for automated game-playing in many board games. The training and search algorithms are not game-specific, but every individual game that these approaches are applied to still requires domain knowledge for the implementation of the game's rules, and constructing the neural network's architecture -in particular the shapes of its input and output tensors. Ludii is a general game system that already contains over 500 different games, which can rapidly grow thanks to its powerful and user-friendly game description language. Polygames is a framework with training and search algorithms, which has already produced superhuman players for several board games. This paper describes the implementation of a bridge between Ludii and Polygames, which enables Polygames to train and evaluate models for games that are implemented and run through Ludii. We do not require any game-specific domain knowledge anymore, and instead leverage our domain knowledge of the Ludii system and its abstract state and move representations to write functions that can automatically determine the appropriate shapes for input and output tensors for any game implemented in Ludii. We describe experimental results for short training runs in a wide variety of different board games, and discuss several open problems and avenues for future research.

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“…This is one of crucial limitations of GDL to make it more applicable in the AI workflows. The fastest GDL interpreters are based on Prolog and propositional networks [19] which instantiate all possible variables and lead to massive structures. There are cases (e.g.…”

Section: A Poor Interpretation Performancementioning

confidence: 99%

Introducing LogDL - Log Description Language for Insights from Complex Data

Ślęzak

2020

Proceedings of the 2020 Federated Conference on Computer Science and Information Systems

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We propose a new logic-based language called Log Description Language (LogDL), designed to be a medium for the knowledge discovery workflows over complex data sets. It makes it possible to operate with the original data along with machinelearning-driven insights expressed as facts and rules, regarded as so-called descriptive logs characterizing the observed processes in real or virtual environments. LogDL is inspired by the research at the border of AI and games, precisely by Game Description Language (GDL) that was developed for General Game Playing (GGP). We emphasize that such formal frameworks for analyzing the gameplay data are a good prerequisite for the case of real, "not digital" processes. We also refer to Fogs of War (FoW)our upcoming project related to AI in video games with limited information -whereby LogDL will be used as well.

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Optimizing Propositional Networks

Cited by 14 publications

References 6 publications

Deep learning for general game playing with Ludii and Polygames

Deep learning for general game playing with Ludii and Polygames

Deep Learning for General Game Playing with Ludii and Polygames

Introducing LogDL - Log Description Language for Insights from Complex Data

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