Domain Of Games Research Articles

Learning How Design Choices Impact Gameplay Behavior

Designers structure a game to provide a desired range of player behaviors: the play space of a game. Any given game is one instance from a design space of alternatives. Navigating a design space to achieve a designer's goals requires knowledge of how design choices shape the play space in a game. We present algorithms to automatically measure play patterns using statistical models that predict how design choices alter player behavior. We present Monte Carlo tree search as a way to sample behaviors from a play space, action metrics to automate play space measurement, and predictive modeling techniques to model design spaces. We demonstrate these techniques in two simplified, perfect information, adversarial game domains based on Scrabble and Hearthstone showing their use for automated design space modeling.

Path monotonicity, consistency and axiomatizations of some weighted solutions

On the domain of cooperative games with transferable utility, we introduce path monotonicity, a property closely related to fairness (van den Brink, in Int J Game Theory 30:309–319, 2001). The principle of fairness states that if a game changes by adding another game in which two players are symmetric, then their payoffs change by the same amount. Under efficiency, path monotonicity is a relaxation of fairness that guarantees that when the worth of the grand coalition varies, the players’ payoffs change according to some monotone path. In this paper, together with the standard properties of projection consistency (Funaki, in Dual axiomatizations of solutions of cooperative games. Mimeo, New York, 1998) and covariance, we show that path monotonicity characterizes the weighted surplus division solutions. Interestingly, replacing projection consistency by either self consistency (Hart and Mas-Colell, in Econometrica 57:589–614, 1989) or max consistency (Davis and Maschler, in Nav Res Logist Q 12:223–259, 1965) we obtain new axiomatic characterizations of the weighted Shapley values and the prenucleolus, respectively. Finally, by the duality approach we provide a new axiomatization of the weighted egalitarian non-separable contribution solutions using complement consistency (Moulin, in J Econ Theory 36:120–148, 1985).

Consistency, Anonymity, and the Core on the Domain of Convex Games

We show that neither Peleg's nor Tadenuma's well-known axiomatizations of the core by non-emptiness, individual rationality, super-additivity, and max consistency or complement consistency, respectively, hold when only convex rather than balanced TU games are considered, even if anonymity is required in addition. Moreover, we show that the core and its relative interior are only two solutions that satisfy Peleg's axioms together with anonymity and converse max consistency on the domain of convex games.

The Procedural Egalitarian Solution and Egalitarian Stable Games

This paper identifies the maximal domain of transferable utility games on which aggregate monotonicity (no player is worse o when the worth of the grand coalition increases) and egalitarian core selection (no other core allocation can be obtained by a transfer from a richer to a poorer player) are compatible. On this domain, which includes the class of large core games, we show that these two axioms characterize a unique solution which even satisfies coalitional monotonicity (no member is worse off when the worth of one coalition increases) and strong egalitarian core selection (no other core allocation can be obtained by transfers from richer to poorer players).

Welfare Egalitarianism in Surplus-Sharing Problems and Convex Games

We show that the constrained egalitarian surplus-sharing rule, which divides the surplus so that the poorer players’ resulting payoffs become equal but not larger than any remaining player’s status quo payoff, is characterized by Pareto optimality, path independence, both well-known, and less first (LF), requiring that a player does not gain if her status quo payoff exceeds that of another player by the surplus. This result is used to show that, on the domain of convex games, Dutta-Ray’s egalitarian solution is characterized by aggregate monotonicity (AM), bounded pairwise fairness, resembling LF, and the bilateral reduced game property (2-RGP) a la Davis and Maschler. We show that 2-RGP can be replaced by individual rationality and bilateral consistency a la Hart and Mas-Colell. We prove that the egalitarian solution is the unique core selection that satisfies AM and bounded richness, requiring that the poorest players cannot be made richer within the core. Replacing “poorest” by “poorer” allows to eliminate AM.

Designing Model of Serious Game for Flood Safety Training

Serious games have the potential to increase motivation among users in the aspect of safety training. Additionally, serious games can also positively impact training outcomes when the knowledge and skills acquired during a serious game training are transferred to a real-world application. The development of a serious game is based on the game elements and theories determined according to the goals or objectives of the game being developed. There are existing serious games that have been used for training but less usage of scenarios and feedback element render the game less effective for training purposes. Besides that, existing serious games for training purposes fail in delivering domain content to achieve the game objectives since they are more focused on entertainment. This is because the games do not involve experts in providing game domain content. The objective of this paper is to design a serious game model for flood safety training. Preliminary study and literature review are used in this study as the research method. The result of this study is a model of a serious game for flood safety training. In conclusion, this study focuses on the design of a serious game model for flood safety training that includes the elements of serious game identified and adapted to psychology readiness based on the flood training module by Malaysian Defense Force (APM). This makes the serious game more attractive and can give intrinsic motivation to players. For future studies, every single element serious game and theory of psychology readiness in the model developed in this study will be validated with the expert game and expert psychology.

Catalonia 2018: Clashing Identities in Public Space

The article analyses the manifestations of underlying identity conflicts in the social spaces of Catalonia, chiefly Barcelona, encountered in the streets, on the sidewalks, motorways, buildings, or beaches. The study focuses on elements that made their appearance after the illegal Catalan independence referendum of October 1st, 2017. Relying on participant observation, the author shows Catalan public space as a domain of semantic and polysemous game, in which struggle for freedom and free speech is the core element.

Parameterized games of perfect information

Considered are perfect information games with a Borel measurable payoff function that is parameterized by points of a Polish space. The existence domain of such a parameterized game is the set of parameters for which the game admits a subgame perfect equilibrium. We show that the existence domain of a parameterized stopping game is a Borel set. In general, however, the existence domain of a parameterized game need not be Borel, or even an analytic or co-analytic set. We show that the family of existence domains coincides with the family of game projections of Borel sets. Consequently, we obtain an upper bound on the set-theoretic complexity of the existence domains, and show that the bound is tight.

Automation of play: Theorizing self-playing games and post-human ludic agents

Abstract This article offers a critical reflection on automation of play and its significance for the theoretical enquiries into digital games and play. Automation has become an ever more noticeable phenomenon in the domain of video games, expressed by self-playing game worlds, self-acting characters, and non-human agents traversing multiplayer spaces. On the following pages, the author explores various instances of automated non-human play and proposes a post-human theoretical lens, which may help to create a new framework for the understanding of video games, renegotiate the current theories of interaction prevalent in game studies, and rethink the relationship between human players and digital games.

Spatial Contextual Cueing, Assessed in a Computerized Task, Is Not a Limiting Factor for Expert Performance in the Domain of Team Sports or Action Video Game Playing

In two reaction time experiments, we investigated if handball and action video game players show improved implicit learning of repeated spatial configurations for efficient search guidance in comparison to a control group without sport or video game proficiency. To this end, we used both a sport-specific pseudo 3-D contextual cueing task and the original contextual cueing paradigm (Chun and Jiang 36, 28-71, 1998). In this visual search paradigm, a target element has to be searched in a distractor-filled display. A typical block of trials consisted of one half of displays that were repeatedly presented in subsequent blocks, while the other half of displays was always randomly generated. In numerous studies with this paradigm, it has been found that search becomes more efficient in repeated displays, even though participants are often unaware of these repetitions (Chun 4, 170-178, 2000). Contextual cueing was present in all groups. Thus, all groups showed incidental learning of repeated displays. Contrary to our hypothesis, handball and action video game players did not differ in the strength of contextual cueing from the control group, although these groups had overall faster search times in the sport-specific displays of experiment 1. To conclude, our data yield no evidence for superior context-learning skills in athletes or action video game players.

Gemini: Bidirectional Generation and Analysis of Games via ASP

Current approaches to game generation don't understand the games they generate. As a result, even the most sophisticated systems in this regard, e.g., Game-o-Matic, betray this problem — generating games with goals that are at odds with their mechanics. We describe Gemini, the first bidirectional game generation and analysis system. Gemini is able to take games as input, perform a proceduralist reading of them, and produce possible interpretations that the games might afford. By utilizing the declarative nature of Answer Set Programming (ASP), this analysis pathway opens up generation of games targeting specific interpretations and makes it possible to ensure the generated games are consistent with the desired reading. For Gemini, we developed a game specification language capable of expressing a larger domain of games than is possible with VGDL, the most widespread representation. We demonstrate the generality of our approach by generating games in a series of domains. These domains are based on prototypes hand-created by a team without knowledge of the constraints and capabilities of Gemini.

Improved WPM encoding for coalition structure generation under MC-nets

The Coalition Structure Generation (CSG) problem plays an important role in the domain of coalition games. Its goal is to create coalitions of agents so that the global welfare is maximized. To date, Weighted Partial MaxSAT (WPM) encoding has shown high efficiency in solving the CSG problem, which encodes a set of constraints into Boolean propositional logic and employs an off-the-shelf WPM solver to find out the optimal solution. However, in existing WPM encodings, a number of redundant encodings are asserted. This results in additional calculations and correspondingly incurs performance penalty. Against this background, this paper presents an Improved Rule Relation-based WPM (I-RWPM) encoding for the CSG problem, which is expressed by a set of weighted rules in a concise representation scheme called Marginal Contribution net (MC-net). In order to effectively reduce the constraints imposed on encodings, we first identify a subset of rules in an MC-net, referred as a set of freelance rules. We prove that solving the problem made up of all freelance rules can be achieved with a straightforward means without any extra encodings. Thus the set of rules requiring to be encoded is downsized. Next, we improve the encoding of transitive relations among rules. To be specific, compared with the existing rule relation-based encoding that generates transitive relations universally among all rules, I-RWPM only considers the transitivity among rules with particular relationship. In this way, the number of constraints to be encoded can be further decreased. Experiments suggest that I-RWPM significantly outperforms other WPM encodings for solving the same set of problem instances.

Automated State of Play

Automation of play has become an ever more noticeable phenomenon in the domain of video games, expressed by self-playing game worlds, self-acting characters, and non-human agents traversing multiplayer spaces. This article proposes to look at AI-driven non-human play and, what follows, rethink digital games, taking into consideration their cybernetic nature, thus departing from the anthropocentric perspectives dominating the field of Game Studies. A decentralised post-humanist reading, as the author argues, not only allows to rethink digital games and play, but is a necessary condition to critically reflect AI, which due to the fictional character of video games, often plays by very different rules than the so-called »true« AI.

Dynamics in matching and coalition formation games with structural constraints

Matching and coalition formation are fundamental aspects in the organization of many multi-agent systems. In large populations, the emergence of coalitions is often restricted by structural constraints under which agents can reorganize, e.g., local visibility or externality constraints among the agents. We study this aspect using a novel framework for dynamics with constraints within the popular domain of hedonic coalition formation games. We analyze the effects of structural constraints on the convergence of matching and coalition formation processes to stable states. Our main result are tight characterizations for the constraint structures based on which dynamic coalition formation can stabilize quickly. We show a variety of convergence results for matching and coalition formation games with different forms of locality and externality constraints. In particular, we propose and analyze a new model of graph-based visibility for coalition formation games and tightly characterize the graph structures that allow polynomial-time convergence – it can be achieved if and only if coalition formation is based on complete or star graphs.

MCTS-Minimax Hybrids with State Evaluations


 
 
 Monte-Carlo Tree Search (MCTS) has been found to show weaker play than minimax-based search in some tactical game domains. This is partly due to its highly selective search and averaging value backups, which make it susceptible to traps. In order to combine the strategic strength of MCTS and the tactical strength of minimax, MCTS-minimax hybrids have been introduced, embedding shallow minimax searches into the MCTS framework. Their results have been promising even without making use of domain knowledge such as heuristic evaluation functions. This article continues this line of research for the case where evaluation functions are available. Three different approaches are considered, employing minimax with an evaluation function in the rollout phase of MCTS, as a replacement for the rollout phase, and as a node prior to bias move selection. The latter two approaches are newly proposed. Furthermore, all three hybrids are enhanced with the help of move ordering and k-best pruning for minimax. Results show that the use of enhanced minimax for computing node priors results in the strongest MCTS-minimax hybrid investigated in the three test domains of Othello, Breakthrough, and Catch the Lion. This hybrid, called MCTS-IP-M-k, also outperforms enhanced minimax as a standalone player in Breakthrough, demonstrating that at least in this domain, MCTS and minimax can be combined to an algorithm stronger than its parts. Using enhanced minimax for computing node priors is therefore a promising new technique for integrating domain knowledge into an MCTS framework.
 
 

Eighteen Months of Meeple Like Us: An Exploration into the State of Board Game Accessibility

The study of game accessibility to date has largely focused on the topic of accessibility within a video game context. Largely underexplored in the academic and professional literature is accessibility in the domain of tabletop games, especially those that are classified as part of the ‘hobbyist’ market. An ongoing series of research annotations, published on the blog Meeple Like Us, has been aimed at addressing this lack of attention. In this paper, the authors report on the work of the Meeple Centred Design project which to date has examined 116 board games for the accessibility issues they manifest and the lessons that can be learned for designers in this space. While the project has not achieved significant coverage of even a fraction of the available library of hobbyist games, currently numbering approximately one hundred thousand, it has discussed the issues with many of the most critically success and popular of these titles. This paper reports on results to date, methodology of the analyses, limitations of the project, and the future plans for work in this interesting game accessibility context.

Automated State Of Play

Automation of play has become an ever more noticeable phenomenon in the domain of video games, expressed by self-playing game worlds, self-acting characters, and non-human agents traversing multiplayer spaces. This article proposes to look at AI-driven non-human play and, what follows, rethink digital games, taking into consideration their cybernetic nature, thus departing from the anthropocentric perspectives dominating the field of Game Studies. A decentralised posthumanist reading, as the author argues, not only allows to rethink digital games and play, but is a necessary condition to critically reflect AI, which due to the fictional character of video games, often plays by very different rules than the so-called “true” AI.

Tackling Area Coverage Problems in a Reconfigurable Floor Cleaning Robot Based on Polyomino Tiling Theory

Whilst Polyomino tiling theory has been extensively studied as a branch of research in mathematics, its application has been largely confined to multimedia, graphics and gaming domains. In this paper, we present a novel application of Tromino tiling theory, a class of Polyomino with three cells in the context of a reconfigurable floor cleaning robot, hTromo. The developed robot platform is able to automatically generate a global tiling set required to cover a defined space while leveraging on the Tromino tiling theory. Specifically, we validated the application of five Tromino tiling theorems with our hTromo robot. Experiments performed clearly demonstrate the efficacy of the proposed approach resulting in very high levels of area coverage performance in all considered experimental cases. This paper also presents the system architecture of our hTromo robot and a detailed description of the five tiling theorems applied in this study.

Reach-Avoid Games With Two Defenders and One Attacker: An Analytical Approach.

This paper considers a reach-avoid game on a rectangular domain with two defenders and one attacker. The attacker aims to reach a specified edge of the game domain boundary, while the defenders strive to prevent that by capturing the attacker. First, we are concerned with the barrier, which is the boundary of the reach-avoid set, splitting the state space into two disjoint parts: 1) defender dominance region (DDR) and 2) attacker dominance region (ADR). For the initial states lying in the DDR, there exists a strategy for the defenders to intercept the attacker regardless of the attacker's best effort, while for the initial states lying in the ADR, the attacker can always find a successful attack strategy. We propose an attack region method to construct the barrier analytically by employing Voronoi diagram and Apollonius circle for two kinds of speed ratios. Then, by taking practical payoff functions into considerations, we present optimal strategies for the players when their initial states lie in their winning regions, and show that the ADR is divided into several parts corresponding to different strategies for the players. Numerical approaches, which suffer from inherent inaccuracy, have already been utilized for multiplayer reach-avoid games, but computational complexity complicates solving such games and consequently hinders efficient on-line applications. However, this method can obtain the exact formulation of the barrier and is applicable for real-time updates.

From Skills to Symbols: Learning Symbolic Representations for Abstract High-Level Planning

We consider the problem of constructing abstract representations for planning in high-dimensional, continuous environments. We assume an agent equipped with a collection of high-level actions, and construct representations provably capable of evaluating plans composed of sequences of those actions. We first consider the deterministic planning case, and show that the relevant computation involves set operations performed over sets of states. We define the specific collection of sets that is necessary and sufficient for planning, and use them to construct a grounded abstract symbolic representation that is provably suitable for deterministic planning. The resulting representation can be expressed in PDDL, a canonical high-level planning domain language; we construct such a representation for the Playroom domain and solve it in milliseconds using an off-the-shelf planner. We then consider probabilistic planning, which we show requires generalizing from sets of states to distributions over states. We identify the specific distributions required for planning, and use them to construct a grounded abstract symbolic representation that correctly estimates the expected reward and probability of success of any plan. In addition, we show that learning the relevant probability distributions corresponds to specific instances of probabilistic density estimation and probabilistic classification. We construct an agent that autonomously learns the correct abstract representation of a computer game domain, and rapidly solves it. Finally, we apply these techniques to create a physical robot system that autonomously learns its own symbolic representation of a mobile manipulation task directly from sensorimotor data---point clouds, map locations, and joint angles---and then plans using that representation. Together, these results establish a principled link between high-level actions and abstract representations, a concrete theoretical foundation for constructing abstract representations with provable properties, and a practical mechanism for autonomously learning abstract high-level representations.