We propose a new General Game Playing (GGP) language called Regular Boardgames (RBG), which is based on the theory of regular languages. The objective of RBG is to join key properties as expressiveness, efficiency, and naturalness of the description in one GGP formalism, compensating certain drawbacks of the existing languages. This often makes RBG more suitable for various research and practical developments in GGP. While dedicated mostly for describing board games, RBG is universal for the class of all finite deterministic turn-based games with perfect information. We establish foundations of RBG, and analyze it theoretically and experimentally, focusing on the efficiency of reasoning. Regular Boardgames is the first GGP language that allows efficient encoding and playing games with complex rules and with large branching factor (e.g. amazons, arimaa, large chess variants, go, international checkers, paper soccer). * Arimaa's perft was computed starting from a fixed chess-like position to skip the initial setup. † For tic-tac-toe, the whole game tree is computed in about a second, thus the test could not last enough time to provide reliable results.

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We present an experimental comparison of the efficiency of three General Game Playing systems in their current versions: Regular Boardgames (RBG 1.0), Ludii 0.3.0, and a Game Description Language (GDL) propnet. We show that in general, RBG is currently the fastest GGP system. For example, for chess, we demonstrate that RBG is about 37 times faster than Ludii, and Ludii is about 3 times slower than a GDL propnet. Referring to the recent comparison [An Empirical Evaluation of Two General Game Systems: Ludii and RBG, CoG 2019], we show evidences that the benchmark presented there contains a number of significant flaws that lead to wrong conclusions. * This game code was not originally available in RBG 1.0 and was added later.† The rules in Ludii differ from the others.2.1. Games in Ludii 0.3.0. Unfortunately, Ludii is a closed-source system, which limits debug possibilities for game rules. In multiple cases, the game rules provided with Ludii 0.3.0 differ

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.