Γενικοί Παίκτες: Αλγόριθμοι και Τεχνικές

Η παρούσα διπλωματική εργασία έχει σαν αντικείμενο τη μελέτη γενικών παικτών (General Game Players). Οι γενικοί παίκτες είναι υπεύθυνοι για το παίξιμο οποιουδήποτε παιχνιδιού, αρκεί οι κανόνες του να περιγράφονται σε μια συγκεκριμένη μορφή – γλώσσα. Η αρχή για την υλοποίηση γενικών παικτών έγινε τη δεκαετία του 2000, όμως η ιδέα γεννήθηκε πολλά χρόνια πριν, σε πολύ πρώιμη μορφή. Στην εργασία αυτή γίνεται μια καταγραφή της βασικής αρχιτεκτονικής ενός γενικού παίκτη, των εξελίξεων που υπάρχουν στον τομέα τα τελευταία χρόνια, καθώς και των δυσκολιών που υπάρχουν ακόμα. Προκειμένου να γίνει πιο αποτελεσματική η μελέτη, στα πλαίσια της διπλωματικής εργασίας, αναπτύχθηκε ο γενικός παίκτης SweetGGP, ώστε να εφαρμοστούν στην πράξη, όσο περισσότερο γίνεται, οι γνώσεις που αποκτήθηκαν, και να εξαχθούν συμπεράσματα. Τέλος, αναπτύχθηκαν επεκτάσεις στη γλώσσα περιγραφής παιχνιδιών, προκειμένου να γίνει ευκολότερη η περιγραφή ορισμένων ομάδων παιχνιδιών.The object of this master thesis is the study of general game players. General game players are able to play any game, if the game’s rules are represented in a certain format – language. The development of the first general game players began on 2000s, but the idea was born many years earlier, in an early version. In this thesis, the basic architecture of a general game player, the developments in recent years, and the difficulties found are presented. In order for the study to be more accurate, the SweetGGP general game player was developed, in order to use the knowledge gained, as much as possible, and come to conclusions. Finally, extensions of the game description language were developed, so that the description of some groups of games becomes easier

Reasoning about general games described in GDL-II

Recently the general Game Description Language (GDL) has been extended so as to cover arbitrary games with incomplete/imperfect information. Learning - without human intervention - to play such games poses a reasoning challenge for general game-playing systems that is much more intricate than in case of complete information games. Action formalisms like the Situation Calculus have been developed for precisely this purpose. In this paper we present a full embedding of the Game Description Language into the Situation Calculus (with Scherl and Levesque's knowledge fluent). We formally prove that this provides a sound and complete reasoning method for players' knowledge about game states as well as about the knowledge of the other players

Logics for strategic reasoning and collective decision-making

Strategic decision-making is ubiquitous in everyday life. The analysis of game strategies has been a research theme in game theory for several decades since von Neumann and Morgenstern. Sophisticated models and analysis tools have been developed with wide applications in Economics, Management Science, Social Science and Computer Science, especially in the field of Artificial Intelligence. However, \much of game theory is about the question whether strategic equilibria exist", as Johan van Benthem, a world-leading logician and game-theorist, points out, \but there are hardly any explicit languages for defining, comparing, or combining strategies". Without such a facility it is challenging for computer scientists to build intelligent agents that are capable of strategic decision-making. In the last twenty years, logical approaches have been proposed to tackle this problem. Pioneering work includes Game Logics, Coalition Logic and Alternating-time Temporal Logic (ATL). These logics either provide facilities for expressing and combining games or offer mechanisms for reasoning about strategic abilities of players. But none of them can solve the problem. The intrinsic difficulty in establishing such a logic is that reasoning about strategies requires combinations of temporal reasoning, counterfactual reasoning, reasoning about actions, preferences and knowledge, as well as reasoning about multi-agent interactions and coalitional abilities. More recently, a few new logical formalisms have been proposed by extending ATL with strategy variables in order to express strategies explicitly. However, most of these logics tend to have high computational complexity, because ATL introduces quantifications over strategies (functions), which leaves little hope of building any tractable inference system based on such a logic. This thesis takes up the challenge by using a bottom-up approach in order to create a balance between expressive power and computational efficiency. Instead of starting with a highly complicated logic, we propose a set of logical frameworks based on a simple and practical logical language, called Game Description Language (GDL), which has been used as an official language for General Game Playing (GGP) since 2005. To represent game strategies, we extend GDL with two binary prioritized connectives for combining actions in terms of their priorities specified by these connectives, and provide it with a semantics based on the standard state transition model. To reason about the strategic abilities of players, we further extend the framework with coalition operators from ATL for specifying the strategic abilities of players. More importantly, a unified semantics is provided for both GDL- and ATL- formulas, which allows us to verify and reason about game strategies. Interestingly, the framework can be used to formalize the fundamental game-playing principles and formally derive two well-known results on two-player games: Weak Determinacy and Zermelo's Theorem. We also show that the model-checking problem of the logic is not worse than that of ATL*, an extension of ATL. To deal with imperfect information games, we extend GDL with the standard epistemic operators and provide it with a semantics based on the epistemic state transition model. The language allows us to specify an imperfect information game and formalize its epistemic properties. Meanwhile, the framework allows us to reason about players' own as well as other players' knowledge during game playing. Most importantly, the logic has a moderate computational complexity, which makes it significantly different from similar existing frameworks. To investigate the interplay between knowledge shared by a group of players and its coalitional abilities, we provide a variant of semantics for ATL with imperfect information. The relation between knowledge sharing and coalitional abilities is investigated through the interplay of epistemic and coalition modalities. Moreover, this semantics is able to preserve the desirable properties of coalitional abilities. To deal with collective decision-making, we apply the approach of combining actions via their priorities for collective choice. We extend propositional logic with the prioritized connective for modelling reason-based individual and collective choices. Not only individual preferences but also aggregation rules can be expressed within this logic. A model-checking algorithm for this logic is thus developed to automatically generate individual and collective choices. In many real-world situations, a group making collective judgments may assign individual members or subgroups different priorities to determine the collective judgment. We design an aggregation rule based on the priorities of individuals so as to investigate how the judgment from each individual affects group judgment in a hierarchical environment. We also show that this rule satisfies a set of plausible conditions and has a tractable computational complexity

Logics for strategic reasoning and collective decision-making

Cette thèse aborde le problème du raisonnement stratégique. Le raisonnement stratégique est un thème de recherches existant depuis e nombreuses années en théorie des jeux. Toutefois, celui-ci a le plus souvent pour objet de déterminer si des équilibres stratégiques existent sans détailler la définition en elle-même de ces stratégies. La construction d'agents artificiels capable de raisonner stratégiquement implique de se poser la question de la représentation de ces stratégies afin que les agents puissent les construire, combiner, comparer et enfin et surtout exécuter. Cette thèse propose un ensemble de logiques pour le raisonnement stratégique et la prise de décision collective. Elle établit dans un premier temps un cadre unifiée pour la définition de jeux, la représentation de stratégies et le raisonnement sur celles-ci dans le contexte des jeux à information parfaite. Ce cadre est ensuite étendu pour prendre en compte les jeux à information imparfaite. Les relations entre les connaissances de groupe, le pouvoir des coalitions ainsi que le partage d'informations dans une coalition sont ensuite étudiés. Dans un dernier temps, est introduit une logique modale permettant de de raisonner sur les choix collectifs, cette logique permet de généraliser les approches logiques existantes pour l'agrégation de jugements. La complexité de ces différents cadres logiques est aussi étudiée et nous montrons que ces différents cadres offre un équilibre pertinent entre efficacité computationnelle et pouvoir d'expression.This thesis proposes a set of logics for modelling strategic reasoning and collective decision-making. It first establishes a unified logical framework for game specifications, strategy representation and strategic reasoning in perfect information games. Based on that, it proposes an epistemic extension to address imperfect information games. To investigate the interplay of group knowledge and coalitional abilities, it further models knowledge sharing within coalitions. Finally it introduces a modal logic for collective choice and generalizes the logic-based approach to judgment aggregation. The complexity analysis of these logics indicates that these frameworks make a good balance between expressive power and computational efficiency

Automated Theorem Proving for General Game Playing

While automated game playing systems like Deep Blue perform excellent within their domain, handling a different game or even a slight change of rules is impossible without intervention of the programmer. Considered a great challenge for Artificial Intelligence, General Game Playing is concerned with the development of techniques that enable computer programs to play arbitrary, possibly unknown n-player games given nothing but the game rules in a tailor-made description language. A key to success in this endeavour is the ability to reliably extract hidden game-specific features from a given game description automatically. An informed general game player can efficiently play a game by exploiting structural game properties to choose the currently most appropriate algorithm, to construct a suited heuristic, or to apply techniques that reduce the search space. In addition, an automated method for property extraction can provide valuable assistance for the discovery of specification bugs during game design by providing information about the mechanics of the currently specified game description. The recent extension of the description language to games with incomplete information and elements of chance further induces the need for the detection of game properties involving player knowledge in several stages of the game. In this thesis, we develop a formal proof method for the automatic acquisition of rich game-specific invariance properties. To this end, we first introduce a simple yet expressive property description language to address knowledge-free game properties which may involve arbitrary finite sequences of successive game states. We specify a semantic based on state transition systems over the Game Description Language, and develop a provably correct formal theory which allows to show the validity of game properties with respect to their semantic across all reachable game states. Our proof theory does not require to visit every single reachable state. Instead, it applies an induction principle on the game rules based on the generation of answer set programs, allowing to apply any off-the-shelf answer set solver to practically verify invariance properties even in complex games whose state space cannot totally be explored. To account for the recent extension of the description language to games with incomplete information and elements of chance, we correctly extend our induction method to properties involving player knowledge. With an extensive evaluation we show its practical applicability even in complex games

SpoookyJS. Ein multiagentenbasiertes JavaScript-Framework zur flexiblen Implementation digitaler browserbasierter Brettspiele und spielübergreifender künstlicher Intelligenz

Künstliche Intelligenz in digitalen Spielen ist zumeist Anwendungsdomäne komplexer spielspezifischer Softwarelösungen mangelnder Erweiterbarkeit. Die vorliegende Arbeit beschäftigt sich mit der Konzeption und Realisierung des JavaScript-Frameworks SpoookyJS, das die vereinfachte Erstellung browserbasierter digitaler Brettspiele ermöglicht. Entwickelt als Multiagentensystem, bietet SpoookyJS künstliche Gegner in den umgesetzten Spielen und fungiert als Test- und Entwicklungsumgebung für die Forschung um spielübergreifende artifizielle Entscheidungsfindung

Strategic and epistemic reasoning for the Game Description Language GDL-II

The game description language GDL has been developed as a logic-based formalism for representing the rules of arbitrary games in general game playing. A recent language extension called GDL-II allows to describe nondeterministic games with any number of players who may have incomplete, asymmetric information. In this paper, we show how the well-known Alternating-time Temporal Epistemic Logic (ATEL) can be adapted for strategic and epistemic reasoning about general games described in GDL-II. We provide a semantic characterisation of GDL-II descriptions in terms of ATEL models. We also provide a syntactic translation of GDL-II descriptions into ATEL formulas, and we prove that these two characterisations are equivalent. We show that model checking in this setting is decidable by giving an algorithm, and we demonstrate how our results can be used to verify strategic and epistemic properties of games described in GDL-II