Ludii -- The Ludemic General Game System

While current General Game Playing (GGP) systems facilitate useful research in Artificial Intelligence (AI) for game-playing, they are often somewhat specialised and computationally inefficient. In this paper, we describe the "ludemic" general game system Ludii, which has the potential to provide an efficient tool for AI researchers as well as game designers, historians, educators and practitioners in related fields. Ludii defines games as structures of ludemes -- high-level, easily understandable game concepts -- which allows for concise and human-understandable game descriptions. We formally describe Ludii and outline its main benefits: generality, extensibility, understandability and efficiency. Experimentally, Ludii outperforms one of the most efficient Game Description Language (GDL) reasoners, based on a propositional network, in all games available in the Tiltyard GGP repository. Moreover, Ludii is also competitive in terms of performance with the more recently proposed Regular Boardgames (RBG) system, and has various advantages in qualitative aspects such as generality.Comment: Accepted at ECAI 202

Σκακιστικές Μηχανές: Επισκόπηση Μεθοδολογιών και Υλοποίηση Προσέγγισης PVS/NNUE

Σε αυτή την πτυχιακή εργασία μελετώνται οι κύριες δομές μια σκακιστικής μηχανής καθώς και οι νεότερες τεχνικές εύρεσης βέλτιστης κίνησης με χρήση νευρωνικών δικτύων. Θα μελετηθούν οι κύριοι τρόποι αναπαράστασης της δομής του ταμπλό με ιδιαίτερη έμφαση σε αυτή των bitboards. Στην συνέχεια θα γίνει μια αναφορά στην μεθοδολογία παραγωγής κινήσεων με χρήση προ-υπολογισμένων πινάκων και τεχνικών τέλειου κατακερματισμού. Θα συζητηθούν οι διάφορες παραλλαγές των αλγορίθμων MCTS και PVS και οι τρόποι με τους οποίους μπορούν να παραλληλοποιηθούν για γρηγορότερη εκτέλεση. Τέλος θα παρουσιαστεί η νεότερη αρχιτεκτονική δικτύων NNUE για την στατική αξιολόγηση θέσεων και οι διαφορές της με πιο σύνθετα μοντέλα όπως αυτό του Alpha zero. Στα πλαίσια αυτής της εργασίας υλοποιήθηκε και μια σκακιστική μηχανή με χρήση του μοντέλου NNUE, του αλγορίθμου PVS με τις αντίστοιχες βελτιστοποιήσεις κλαδέματος και της αναπαράστασης bitboards. Οι λεπτομέρειες υλοποίησης παρέχονται στην τελευταία ενότητα.In this dissertation we study the primary components of chess engines, as well as the latest techniques for finding optimal moves using neural networks. The various board representation will be analysed, with special emphasis on that of bitboards. We will refer to the main methods for producing moves using pre-calculated lookup tables with perfect hashing. The various variants of MCTS and PVS algorithms and the ways in which they can be parallelised for faster execution will be discussed. Finally, the latest NNUE network architecture for static position evaluation and its differences with more complex models such as Alpha zero will be presented. As part of this work, a chess engine was developed using the NNUE model, the PVS algorithm with the corresponding pruning optimizations and the representation of bitboards. Implementation details are provided in the last section

Extracting tactics learned from self-play in general games

Local, spatial state-action features can be used to effectively train linear policies from self-play in a wide variety of board games. Such policies can play games directly, or be used to bias tree search agents. However, the resulting feature sets can be large, with a significant amount of overlap and redundancies between features. This is a problem for two reasons. Firstly, large feature sets can be computationally expensive, which reduces the playing strength of agents based on them. Secondly, redundancies and correlations between features impair the ability for humans to analyse, interpret, or understand tactics learned by the policies. We look towards decision trees for their ability to perform feature selection, and serve as interpretable models. Previous work on distilling policies into decision trees uses states as inputs, and distributions over the complete action space as outputs. In contrast, we propose and evaluate a variety of decision tree types, which take state-action pairs as inputs, and provide various different types of outputs on a per-action basis. An empirical evaluation over 43 different board games is presented, and two of those games are used as case studies where we attempt to interpret the discovered features

African Studies Abstracts Online: number 21, 2008

ASA Online provides a quarterly overview of journal articles and edited works on Africa in the field of the social sciences and the humanities available in the ASC library. Issue 21 (2008). African Studies Centre, Leiden.ASC – Publicaties niet-programma gebonde

Best Play in Fanorona Leads to Draw

Fanorona is the national board game of Madagascar. The game's complexity is approximately the same as that of checkers. In this article, we present a search-based approach for weakly solving this game. It is a well-chosen combination of Proof-Number search and endgame databases. Retrograde analysis is used to generate the endgame databases in which every position with 7 or fewer pieces on the board has been solved. Then, a Proof-Number search variant, PN2, exploits the databases to prove that the game-theoretical value of the initial position is a draw. Future research should develop techniques for strongly solving the game