Efficient Reasoning in Regular Boardgames

Kowalski, Jakub; Miernik, Radoslaw; Mika, Maksymilian; Pawlik, Wojciech; Sutowicz, Jakub; Szykuła, Marek; Tkaczyk, Andrzej

doi:10.1109/cog47356.2020.9231668

Cited by 12 publications

(11 citation statements)

References 14 publications

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“…Games are being used in an ever-increasing variety of disciplines in our daily lives, ranging from entertainment to education, creativity to technology, and game-related research has become a hot topic in computer science. For example, gamification is a technique that is often used to engage Internet users in very different activities [18], and data analytics and artificial intelligence have been used for computer-assisted game design [19] or to teach computers to play games [20]. In [21], the authors showed how to automatically determine board game categories and mechanics by means of a short textual description of the game only, and argue that this kind of analysis might be used to discover new games features and make games effective tools in a variety of socially useful domains, e.g., the promotion of Computational Thinking in schooling, or the identification of those games that are the most suited in social distancing situations.…”

Section: Scenario 2: Game Analyticsmentioning

confidence: 99%

About Challenges in Data Analytics and Machine Learning for Social Good

Montangero

2022

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The large number of new services and applications and, in general, all our everyday activities resolve in data mass production: all these data can become a golden source of information that might be used to improve our lives, wellness and working days. (Interpretable) Machine Learning approaches, the use of which is increasingly ubiquitous in various settings, are definitely one of the most effective tools for retrieving and obtaining essential information from data. However, many challenges arise in order to effectively exploit them. In this paper, we analyze key scenarios in which large amounts of data and machine learning techniques can be used for social good: social network analytics for enhancing cultural heritage dissemination; game analytics to foster Computational Thinking in education; medical analytics to improve the quality of life of the elderly and reduce health care expenses; exploration of work datafication potential in improving the management of human resources (HRM). For the first two of the previously mentioned scenarios, we present new results related to previously published research, framing these results in a more general discussion over challenges arising when adopting machine learning techniques for social good.

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Section: Scenario 2: Game Analyticsmentioning

confidence: 99%

About Challenges in Data Analytics and Machine Learning for Social Good

Montangero

2022

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“…The buffer time is not included, i.e., this is the time limit for pure computation; the real limit sent by the manager is 0.6s. In view of computational power, this roughly corresponds to the setting with 10s limit based on GDL reasoning (Kowalski et al 2020), which is often used for comparisons of agents (Sironi and Winands 2016).…”

Section: The Settingmentioning

confidence: 99%

“…The random generator used was the default used in the RBG system: std::mt19937 together with the Lemire's selection method (Lemire 2019;Kowalski et al 2020).…”

Section: Environmentmentioning

confidence: 99%

Split Moves for Monte-Carlo Tree Search

Kowalski¹,

Mika²,

Pawlik³

et al. 2021

Preprint

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In many games, moves consist of several decisions made by the player. These decisions can be viewed as separate moves, which is already a common practice in multi-action games for efficiency reasons. Such division of a player move into a sequence of simpler / lower level moves is called splitting. So far, split moves have been applied only in forementioned straightforward cases, and furthermore, there was almost no study revealing its impact on agents' playing strength. Taking the knowledge-free perspective, we aim to answer how to effectively use split moves within Monte-Carlo Tree Search (MCTS) and what is the practical impact of split design on agents' strength. This paper proposes a generalization of MCTS that works with arbitrarily split moves. We design several variations of the algorithm and try to measure the impact of split moves separately on efficiency, quality of MCTS, simulations, and action-based heuristics. The tests are carried out on a set of board games and performed using the Regular Boardgames General Game Playing formalism, where split strategies of different granularity can be automatically derived based on an abstract description of the game. The results give an overview of the behavior of agents using split design in different ways. We conclude that split design can be greatly beneficial for single-as well as multi-action games.

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“…If such a list of moves were stored in memory in the game state representation, and updated as moves were applied, the optimisation could also be used outside of playouts (e.g., when building search trees). In the Regular Boardgames system [13], such an idea has been implemented more generally as a step of an optimising compiler [12]. However, we remark that this does increase the memory footprint of the game state representation, and it can slow down operations such as the copying of game states, which is often required in aspects of game tree searches outside of playouts.…”

Section: Add-to-empty Playoutsmentioning

confidence: 99%

“…When such operations can be implemented to run more efficiently, they allow for deeper tree searches, which usually leads to stronger agents. For this reason, a significant amount of research has gone towards techniques such as bitboard methods [2], PropNet optimisations [17] for general game playing, hardware accelerators [6,18], optimising compilers for general game description languages [12], etc.…”

Section: Introductionmentioning

confidence: 99%

Optimised Playout Implementations for the Ludii General Game System

Soemers¹,

Piette²,

Stephenson³

et al. 2021

Preprint

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This paper describes three different optimised implementations of playouts, as commonly used by game-playing algorithms such as Monte-Carlo Tree Search. Each of the optimised implementations is applicable only to specific sets of games, based on their rules. The Ludii general game system can automatically infer, based on a game's description in its general game description language, whether any optimised implementations are applicable. An empirical evaluation demonstrates major speedups over a standard implementation, with a median result of running playouts 5.08 times as fast, over 145 different games in Ludii for which one of the optimised implementations is applicable.

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Efficient Reasoning in Regular Boardgames

Cited by 12 publications

References 14 publications

About Challenges in Data Analytics and Machine Learning for Social Good

About Challenges in Data Analytics and Machine Learning for Social Good

Split Moves for Monte-Carlo Tree Search

Optimised Playout Implementations for the Ludii General Game System

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