General Game Research Articles

GameTable Working Group 1 meeting report on search, planning, learning, and explainability

The inaugural in-person meeting for the “GameTable” COST Action’s Working Group 1 (WG1) on Search, Planning, Learning, and Explainability took place at the Leiden Institute of Advanced Computer Science (LIACS) on January 31st, 2024. The primary aims of this meeting were to facilitate talks and discussions on, and connect researchers interested in, three core research goals: (1) human-like game-playing AI, (2) imperfect-information games within a general game playing context, and (3) explainable search and reinforcement learning in games. This report provides a summary of the discussions and talks that took place during the meeting.

Reach Chess: An accidental chess variant

Reach Chess is a chess variant discovered by accident while testing the Ludii general game system, in which a simple rule change fundamentally subverts the nature of the standard game. This paper describes Reach Chess, how it came to exist, and reveals its character through a brief analysis and puzzle with annotated solution.

Generic Games: The Ending of Longus’ Daphnis and Chloe

Abstract: This article unravels the careful encodings contained in the final sentence, the sphragis , of Longus’ novel Daphnis and Chloe . Building on previous studies on the ending, I delve more deeply into each of the key terms included in the final sentence and argue for the presence of New Comedy as the key complement to Bucolic in the novel’s generic hybridity. I focus on Chloe’s role as focalizer of the final sentence, and through key intratextual and intertextual resonances of the sphragis argue that the novel’s generic games are bound up above all in her experience and educational development.

Studi Visual UI dan UX pada Gim Persona 5 Royal

Abstract: This journal discusses the intricacies of User Interface (UI) and User Experience (UX) design introduced in Persona 5 Royal, the definitive edition of the critically acclaimed role-playing game Persona 5. The journal begins by providing an overview of Persona 5 Royal's UI design, highlighting key design features and aspects that contribute to a better and more immersive gaming experience. It analyzes the integration of new features, such as simplified menus, enhanced navigation tools, and refined visual aesthetics, and evaluates their effectiveness in improving player engagement and accessibility. It also provides insights into the strengths and weaknesses of Persona 5 Royal's UI and UX, offering valuable perspectives for designers, developers, and general game enthusiasts. Ultimately, this research seeks to celebrate and appreciate Persona 5 Royal's UI and UX design achievements while providing critical insights into the ongoing evolution of game design in the modern gaming landscape or industry as a whole.

Using Genshin Impact as an Example, Examining How Local Cultural Embedding Can Enhance the Player's Gaming Experience

This research examines how the Genshin game developers organically incorporate culture into their games and improve player experience. Using quantitative methods, researchers analyzed 3 interviews with Genshin users to explore their attitudes and feelings towards the integration of Chinese cultural characteristics into the games. Through this game, many Chinese players have developed a sense of cultural pride, and the developers are very good at the details of the game. This game also spreads Chinese culture to the world. In many foreign websites, such as YouTube, Twitter can see a lot of foreign players comments and suggestions to these games. Also, the age coverage of this game is very wide too. From middle-aged people to high school students, all show a strong interest in this game. Researchers intend to study what kind of indigenous Chinese culture is embedded in this game and to understand how indigenous Chinese culture is organically embedded in the game by comparing game screenshots with real-life photos. Next, it is also necessary to check what kind of attitude players hold towards the embedding of Chinese indigenous culture appearing in Genshin Impact on social media platforms and select a few players to interview. Based on the former, it is important to find out how the embedding of Chinese indigenous culture in the game attracts players and improves their gaming experience. Finally, it will be generalized to the general game development, how a game can attract more players to play and improve the player experience through the organic integration of indigenous culture.

Fast and Knowledge-Free Deep Learning for General Game Playing (Student Abstract)

We develop a method of adapting the AlphaZero model to General Game Playing (GGP) that focuses on faster model generation and requires less knowledge to be extracted from the game rules. The dataset generation uses MCTS playing instead of self-play; only the value network is used, and attention layers replace the convolutional ones. This allows us to abandon any assumptions about the action space and board topology. We implement the method within the Regular Boardgames GGP system and show that we can build models outperforming the UCT baseline for most games efficiently.

Improving Behavioral-Based Safety Training in Using Verbal Commands Through a Theory-Driven and Feedback-Based Nonimmersive Virtual Reality Game: Development and Usability Study.

The construction, chemical, aviation, medical, and health care industries have used serious games for safety training. To our knowledge, serious games have not been developed focusing on behavioral change to improve safety through the use of verbal commands and instilling players with heightened awareness of their spatial proximity to other people in their surroundings. We aimed to develop a theory-driven serious game for improving safety behavior using verbal commands and validate the implementation of the theoretical frameworks used for game development. The game developed, KitchenSpeak, was a first-person character (FPC) game where users respond to in-game prompts to use loud verbal commands when they are approaching another employee's blind spot. In addition to using the SERES framework in guiding the general game design and development, and the Reflection, Engagement, Choice, Information, Play, Exposition (RECIPE) framework to inform the design of the game mechanics, we also applied gestalt laws of perception for graphic design to guide the design of the game's user interface. We conducted 2 evaluative tests (alpha and beta) to collect end user and stakeholder feedback on the implementation of the theoretical frameworks, as well as to collect relevant information for full-scale implementation and a future validation study. The alpha and beta tests had 8 and 40 participants, respectively. The alpha test results revealed that the theoretical frameworks were adequately applied; however, suggestions were also made to modify and improve the game. The beta test results suggested further improvements for the game design and found no differences in the perception of ease of play between participants with and without previous FPC gaming experience (P=.47; Kruskal-Wallis). Results suggested that the game met its design and theoretical requirements, and it would be easily playable by all players regardless of their previous experience in FPC games. A theory-driven and evidence-based FPC game titled KitchenSpeak was developed to teach the use of kitchen-speak terms in commercial kitchens. Evaluative tests were conducted to validate the implementation of the theoretical frameworks. Our main contributions are creating and validating game-based training to improve behavioral-based safety in the workplace and the incorporation of gestalt laws of perception for graphic design in the game's user interface.

A research of artificial intelligence game agent application

Currently, large language models are on the rise with breakthrough progress in artificial intelligence. Existing reviews of AI game agents have not covered these latest developments, requiring a combing and analysis of the newest research advancements in game AI agents. This paper summarizes the application scenarios of game AI agents in four aspects: combat AI, Non-Player Character (NPC) interaction, automated testing, and Artificial General Intelligence (AGI) testing. In combat AI, there is a progressive developmental trend, with the introduction of Monte Carlo tree search and reinforcement learning enabling AI game agents to fully surpass humans in traditional board games. In NPC interaction, full AI is unnecessary. Game developers only need to incorporate AI for abilities related to player experience to increase appeal, with controllable generation results. In automated testing, game AI agents lack generalizability for testing so far. In AGI testing, academia has helpfully explored general game AI, but capabilities remain limited to certain games. Introducing large language models to game AI agents shows unprecedented capabilities. Finally, this paper provides an outlook on the hot topics and future directions of this research subject.

Modern General Game Playing with Reinforcement Learning

In computational science and robotics, game playing and artificial intelligence (AI) are research areas that have long been studied. RL systems represent a major step towards autonomous systems that comprehend the visual world at an incredibly deep level and are poised to revolutionize the field of artificial intelligence (AI). Game Play is the testbench where environmental variables can be evaluated to generate adaptive, intelligent, or responsive behavior or simulate real-world scenarios. In this survey, we will explore general gameplay (GGP) with reinforcement learning and its various applications. This survey will cover central algorithms in deep RL, including the deep Qnetwork (DQN) and general mathematical and pragmatic approaches taken in the field. The objective of the current review is to examine the history, associations, and recent advances in the field of general game playing with reinforcement learning and its subfields. We conclude by describing several current areas of research within this field.

The 2022 Ludii AI competition

The Ludii AI Competition involves general game playing events focused on developing agents that can play a wide variety of board games. In the 2022 edition, three competition tracks were proposed: Kilothon, General Game Playing, and Learning. All tracks used the Ludii general game system to provide the necessary games and API. This paper reports the motivation, context, and results of the 2022 Ludii AI Competition.

Research on Price Discrimination Behavior Governance of E-Commerce Platforms—A Bayesian Game Model Based on the Right to Data Portability

The behavior of e-commerce platforms using big data algorithms to implement “big data killing” is not only rejected by regular customers, but also creates many difficulties for supervision by relevant government departments. In order to study how to curb the price discrimination behavior of e-commerce platforms in the case of taking regular customers as the leaders, this paper introduces the right to data portability and considers two types of regular customers with high price sensitivity and low price sensitivity. Then, we build a Bayesian game model between e-commerce platforms and regular customers on the basis of the established general game model. Our experiments show that when the reuse value coefficient of personal data is high, the e-commerce platform will choose a uniform pricing strategy, which indicates that the right to data portability can curb the price discrimination behavior of the e-commerce platform to a certain extent. Moreover, when the proportion of high-sensitivity consumers among regular customers increases, e-commerce platforms will be prompted to choose the uniform pricing strategy, which indicates that consumers can curb price discrimination behaviour on e-commerce platforms by increasing their sensitivity for price change.

Nash conditional independence curve

We study the Spohn conditional independence (CI) variety CX of an n-player game X for undirected graphical models on n binary random variables consisting of one edge. For a generic game, we show that CX is a smooth irreducible complete intersection curve (Nash conditional independence curve) in the Segre variety (P1)n−2×P3 and we give an explicit formula for its degree and genus. We prove two universality theorems for CX: The product of any affine real algebraic variety with the real line or any affine real algebraic variety in Rm defined by at most m−1 polynomials is isomorphic to an affine open subset of CX for some game X.

Epistemic foundation of the backward induction paradox

After having observed a deviation from backward induction, a player might deem the opponent prone to deviate from backward induction again, making it worthwhile to deviate themself. Such reaction might make the deviation by the opponent worthwhile in the first place—which is the backward induction paradox. This argument against backward induction cannot be made in games where all players choose only once on each path. While strategic-form perfect equilibrium yields backward induction in games where players choose only once on each path but not necessarily otherwise, no existing non-equilibrium concept captures the backward induction paradox by having these properties. To provide such a concept, we define and epistemically characterize the concept of independently permissible strategies. Since beliefs are modeled by non-Archimedean probabilities, meaning that some opponent choices might be assigned subjective probability zero without being deemed subjectively impossible, special attention is paid to the formalization of stochastically independent beliefs.

Experience, Rationality, Situation and Fallibilism: Establishing a Feminist Pragmatist Epistemology in Game Studies

Since the seventies, the definition of scientific knowledge has undergone major shifts. However, game researchers do not sufficiently reflect upon those epistemological changes. This paper suggests that to make game studies more inclusive—for women especially and diverse voices in general—game researchers need to shift from traditional, objective epistemologies toward pragmatist ones instead. To support such an argument, this paper first focuses on the central concepts of pragmatist feminist epistemology: experience, rationality, situation and fallibilism. Those concepts are then used for a rereading of game studies epistemological stances. I argue that game studies initially adhered to traditional epistemologies, which formed hostile attitudes toward women and minorities in the field. On the contrary, several authors now develop their scholarship congruently with a feminist pragmatist epistemology. Their works are analyzed to observe how pragmatist feminist concepts concretely manifest in research.

Learning Logic Programs by Discovering Where Not to Search

The goal of inductive logic programming (ILP) is to search for a hypothesis that generalises training examples and background knowledge (BK). To improve performance, we introduce an approach that, before searching for a hypothesis, first discovers "where not to search". We use given BK to discover constraints on hypotheses, such as that a number cannot be both even and odd. We use the constraints to bootstrap a constraint-driven ILP system. Our experiments on multiple domains (including program synthesis and inductive general game playing) show that our approach can (i) substantially reduce learning times by up to 97%, and (ii) can scale to domains with millions of facts.

Algorithm structure for real-time general game playing agents

General Game Playing is an examination of AI agents that are designed without any explicit knowledge of a particular game. Instead, they are required to play a variety of games that they have never seen before by merely reading the description of the rules at runtime, without any intervention from a human being. Previous successful agents have concentrated their efforts on turn-based games, employing either generic heuristics or the Monte Carlo/UCT simulation technique. Real-time games have dominated the market in recent years, and as a result, agents that have more reflexibility have emerged as an important area of research. In this paper, the author evaluateswhether or not it is possible to implement the already-existing algorithms into a real-time playing GGP agent, and we present a potential algorithm structure that, on the basis of this evaluation, could be able to solve the challenge.

Automated videogame mechanics generation with XVGDL

The automatic generation of game mechanics is nowadays one of the most complex challenges within procedural content generation (PCG), even being considered by itself as automatic game generation in the literature. Previous works have contributed with research papers related to PCG in general, and, in particular, the generation of game mechanics from a specification written in a game description language (GDL). One primary reference is the Ludii general game system that allows to generate new mechanics for two-player combinatorial (board) games. Ludii manages game specifications written in a GDL that can be directly represented in a tree structure. These structures are then evolved by means of genetic programming (GP), a well-known bio-inspired optimization technique, to produce new game rules. Now, this paper extends the approach centered on 2-player games and proposed in the Ludii general game system to n-player videogames. The paper describes a system to automatically generate videogame mechanics. The starting point is a videogame specification written in the XML-based videogame description language (XVGDL). Similarly to Ludii, this videogame specification can be directly translated to rule tree structures that can be evolved by GP. The viability of this approach is demonstrated by experimentation with practical examples. The experiments show how it is possible to automatically produce and evaluate a number of distinct versions (that differ in their mechanics) of a videogame that was originally specified in XVGDL.

Lower bounds for the query complexity of equilibria in Lipschitz games

Nearly a decade ago, Azrieli and Shmaya introduced the class of λ-Lipschitz games in which every player's payoff function is λ-Lipschitz with respect to the actions of the other players. They showed that such games admit ϵ-approximate pure Nash equilibria for certain settings of ϵ and λ. They left open, however, the question of how hard it is to find such an equilibrium. In this work, we develop a query-efficient reduction from more general games to Lipschitz games. We use this reduction to show a query lower bound for any randomized algorithm finding ϵ-approximate pure Nash equilibria of n-player, binary-action, λ-Lipschitz games that is exponential in nλ/ϵ. In addition, we introduce “Multi-Lipschitz games,” a generalization involving player-specific Lipschitz values, and provide a reduction from finding equilibria of these games to finding equilibria of Lipschitz games, showing that the value of interest is the average of the individual Lipschitz parameters. Finally, we provide an exponential lower bound on the deterministic query complexity of finding ϵ-approximate Nash equilibria of n-player, m-action, λ-Lipschitz games for strong values of ϵ, motivating the consideration of explicitly randomized algorithms in the above results.

La Grange®: A generic game to reveal trade-offs and synergies among stakeholders in livestock farming areas

CONTEXTLivestock farming systems have received particular attention in the news and scientific media. However, do these considerations ever come to a real debate? Nothing is less certain. There is much attention on the negative impacts of livestock farming on the environment, and it remains difficult to have a fruitful dialogue on the desirable developments of livestock farming areas. This is also true at a local scale, where antagonisms among stakeholders can be strong. OBJECTIVEWe present La Grange®, a generic serious game that reveals trade-offs and synergies in livestock farming areas. Its aim is to promote horizontal knowledge and dialogue among stakeholders on locally-adapted transition for livestock farming. METHODSAfter presenting the conceptual model underlying the game with the different game components and phases, we report the outputs of four case studies with groups of farmers or students in agriculture. RESULTS AND CONCLUSIONSWe show that La Grange® fills a gap in the toolbox for understanding livestock farming, making it possible to integrate generic and local knowledge and incorporating its biotechnical, environmental, economic, and sociocultural dimensions. In each case study, La Grange® not only revealed trade-offs and synergies between these dimensions, but also allowed new cooperation and strategic roadmaps to be set up for a livestock farming area. Both farmers and students perceived the game as a less top-down way of learning. When played with citizens, two of them joined the steering committee on an experimental farm after the game session. SIGNIFICANCEAs a low-tech, easy-to-access game, La Grange® can be easily used by extension services and agricultural teachers. Outputs from the four case-studies makes it possible to classify La Grange® as a “Game for Change” due to its impact on supporting sustainability.

Spatial state-action features for general games

In many board games and other abstract games, patterns have been used as features that can guide automated game-playing agents. Such patterns or features often represent particular configurations of pieces, empty positions, etc., which may be relevant for a game's strategies. Their use has been particularly prevalent in the game of Go, but also many other games used as benchmarks for AI research. In this paper, we formulate a design and efficient implementation of spatial state-action features for general games. These are patterns that can be trained to incentivise or disincentivise actions based on whether or not they match variables of the state in a local area around action variables. We provide extensive details on several design and implementation choices, with a primary focus on achieving a high degree of generality to support a wide variety of different games using different board geometries or other graphs. Secondly, we propose an efficient approach for evaluating active features for any given set of features. In this approach, we take inspiration from heuristics used in problems such as SAT to optimise the order in which parts of patterns are matched and prune unnecessary evaluations. This approach is defined for a highly general and abstract description of the problem—phrased as optimising the order in which propositions of formulas in disjunctive normal form are evaluated—and may therefore also be of interest to other types of problems than board games. An empirical evaluation on 33 distinct games in the Ludii general game system demonstrates the efficiency of this approach in comparison to a naive baseline, as well as a baseline based on prefix trees, and demonstrates that the additional efficiency significantly improves the playing strength of agents using the features to guide search.