Subset Of Players Research Articles

Generating Game Levels by Defining Player Experiences

This paper proposes a novel pipeline for generating game levels that elicit predefined emotional experiences from players. Our approach uses evolutionary algorithms alongside data-driven persona agents, predictive emotional models, a PCG parametric level generator, and a newly defined language for the clear and computable definition of player emotional experiences: ExpREx (Experience Regular Expressions). Using these components, we evolve game levels to match the player experience goals specified using the ExpREx language, aiming to create levels that evoke specific emotional experiences for different subsets of players. The efficacy of our method was validated through a user study involving 101 participants, whose continuous annotations of emotional experience were collected and analyzed to assess the congruence between the actual emotional responses elicited and those targeted by our pipeline. We found that 93.73% of the ExpREx goals targeted were also reported by the user study subjects.

Quality of Life in Youth Soccer Players After Mild Traumatic Brain Injury.

Pediatric mild traumatic brain injuries (mTBIs) represent an evolving field of interest in youth athletics. Although most players recover within 4 weeks, some have symptoms that last longer. Little is known about youth health-related quality of life (HRQoL) after mTBI. To characterize youth HRQoL after soccer-related mTBI and to identify predictors of individual differences in HRQoL recovery. Prospective cohort study. Youth soccer. Soccer players, aged 8 to 17 years, who sustained an mTBI (n = 23) or orthopaedic injury (OI, n = 24) or remained uninjured (n = 23) during a single season. We assessed HRQoL via the Pediatric Quality of Life Inventory, version 4.0, and postconcussive symptoms via the Health and Behavior Index. Serial assessments occurred at 24 to 48 hours, 7 days, 30 days, and 90 days postinjury via telephone interview. At 7 days postinjury, the mTBI and OI groups had poorer total HRQoL (F2,67 = 11.35, P < .001) than the uninjured control group. At 7 days, the mTBI group had the poorest psychosocial HRQoL, whereas the OI group had the poorest physical HRQoL. Differences between the mTBI and uninjured control groups resolved by 30 days. Within the mTBI group, players with significant postconcussive symptoms at 7 days had poorer total (F1,21 = 23.071, P ≤ .001; F1,21 = 5.798, P = .028), psychosocial (F1,21 = 16.488, P = < .001; F1,21 = 5.050, P = .039), and physical (F1,21 = 21.671, P = < .001; F1,21 = 5.119, P = .038) HRQoL at 7 and 30 days, respectively, than players with minimal symptoms; these differences resolved by 90 days. As a group, youth soccer players who sustained mTBI had transient impairments in HRQoL that resolved by 30 days. A subset of players with significant postconcussive symptoms at 7 days postinjury had poorer HRQoL for at least 30 days postinjury than those whose postconcussive symptoms had resolved within a week of injury. This suggests ongoing recovery in this subset at 30 days and the potential utility of HRQoL as a measure of recovery.

The Basic Core of a Parallel Machines Scheduling Game

Problem definition: We consider the parallel machine scheduling (PMS) under job-splitting game defined by a set of manufacturers where each holds uniform parallel machines and each is committed to produce some jobs submitted to her by her clients while bearing the cost of the sum of completion times of her jobs on her machines. An efficient algorithm for this scheduling problem is well known. We consider the corresponding cooperative game, where the manufacturers are players that want to join forces. We show that collaboration is profitable. Yet, the stability of the cooperation depends on the cost allocation scheme; we focus on the core of the game. Methodology/results: We prove that the PMS game is totally balanced and its core is infinitely large, by developing a sophisticated methodology of linear complexity that finds a line segment in its symmetric core. We call this segment the basic core of the game. Managerial implications: This PMS game has the potential for various applications both in traditional industry and in distributed computing systems in the hi-tech industry. The formation of a partnership among entrepreneurs, companies, or manufacturers necessitates not only a plan for joining forces toward the achievement of the ultimate goals, but also an acceptable agreement regarding the cost allocation among the partners. Core allocations guarantee the stability of the partnership as no subset of players can gain by defecting from the grand coalition. Funding: This work was supported by the Henry Crown Israeli Institute for Business Research, the Coller Foundation, and the Israel Science Foundation [Grant 1489/19].

Distributed GNE Seeking under Global-Decision and Partial-Decision Information over Douglas-Rachford Splitting Method

This paper develops an algorithm for solving the generalized Nash equilibrium problem (GNEP) in non-cooperative games. The problem involves a set of players, each with a cost function that depends on their own decision as well as the decisions of other players. The goal is to find a decision vector that minimizes the cost for each player. Unlike most of the existing algorithms for GNEP, which require full information exchange among all players, this paper considers a more realistic scenario where players can only communicate with a subset of players through a connectivity graph. The proposed algorithm enables each player to estimate the decisions of other players and update their own and others’ estimates through local communication with their neighbors. By introducing a network Lagrangian function and applying the Douglas-Rachford splitting method (DR), the GNEP is reformulated as a zero-finding problem. It is shown that the DR method can find the generalized Nash equilibrium (GNE) of the original problem under some mild conditions.

Computer Aided Diagnosis of Melanoma Using Deep Neural Networks and Game Theory: Application on Dermoscopic Images of Skin Lesions.

Early detection of melanoma remains a daily challenge due to the increasing number of cases and the lack of dermatologists. Thus, AI-assisted diagnosis is considered as a possible solution for this issue. Despite the great advances brought by deep learning and especially convolutional neural networks (CNNs), computer-aided diagnosis (CAD) systems are still not used in clinical practice. This may be explained by the dermatologist's fear of being misled by a false negative and the assimilation of CNNs to a "black box", making their decision process difficult to understand by a non-expert. Decision theory, especially game theory, is a potential solution as it focuses on identifying the best decision option that maximizes the decision-maker's expected utility. This study presents a new framework for automated melanoma diagnosis. Pursuing the goal of improving the performance of existing systems, our approach also attempts to bring more transparency in the decision process. The proposed framework includes a multi-class CNN and six binary CNNs assimilated to players. The players' strategies is to first cluster the pigmented lesions (melanoma, nevus, and benign keratosis), using the introduced method of evaluating the confidence of the predictions, into confidence level (confident, medium, uncertain). Then, a subset of players has the strategy to refine the diagnosis for difficult lesions with medium and uncertain prediction. We used EfficientNetB5 as the backbone of our networks and evaluated our approach on the public ISIC dataset consisting of 8917 lesions: melanoma (1113), nevi (6705) and benign keratosis (1099). The proposed framework achieved an area under the receiver operating curve (AUROC) of 0.93 for melanoma, 0.96 for nevus and 0.97 for benign keratosis. Furthermore, our approach outperformed existing methods in this task, improving the balanced accuracy (BACC) of the best compared method from 77% to 86%. These results suggest that our framework provides an effective and explainable decision-making strategy. This approach could help dermatologists in their clinical practice for patients with atypical and difficult-to-diagnose pigmented lesions. We also believe that our system could serve as a didactic tool for less experienced dermatologists.

Reward and Punishment Mechanism with weighting enhances cooperation in evolutionary games

The essential of the human cooperative behavior is to find an effective mechanism to promote cooperation. In reality, there is a class of professionals who punish evil and promote good, such as police officers, judges, and volunteers. Their actions and responsibilities can profoundly affect the actions of other individuals by fines and bonuses. Motivated by this phenomenon, we propose a Reward and Punishment Mechanism with weighting (RPMW) for promoting cooperation. The core of RPMW lies in three aspects: (i) A subset of players are tagged as supervisors, who are generated by a random algorithm. If the supervisor chooses to cooperate, she/he acts as the doer of rewards and punishments, otherwise she/he is a general player. (ii) Each supervisor asks their neighbors in turn as well as rewards cooperators and punishes defectors. Bonuses (penalties) are calculated according to a weighting which is the ratio of the number of current evolutionary round cooperators (defectors) to the total of players. (iii) A factor α is added to control the intensity of rewards and punishments, and the β is denoted as the percentage of supervisors in the group. Furthermore, we do a detailed ablation study on the Reward Mechanism with Weighting (RMW) and Punishment Mechanism with Weighting (PMW) in three social dilemmas. The simulation results show that RPMW can effectively promote cooperation in prisoner’s dilemma (PDG), snowdrift game (SDG) and stag-hunt game (SHG). We find that the frequency of cooperation reaches highest when the α is at its maximum and the β is in a positive impact on evolutionary cooperation. The results prove that RPMW is more effective than RMW and PMW on enhancing cooperation in PDG, SDG.

Multiagent off-screen behavior prediction in football

In multiagent worlds, several decision-making individuals interact while adhering to the dynamics constraints imposed by the environment. These interactions, combined with the potential stochasticity of the agents’ dynamic behaviors, make such systems complex and interesting to study from a decision-making perspective. Significant research has been conducted on learning models for forward-direction estimation of agent behaviors, for example, pedestrian predictions used for collision-avoidance in self-driving cars. In many settings, only sporadic observations of agents may be available in a given trajectory sequence. In football, subsets of players may come in and out of view of broadcast video footage, while unobserved players continue to interact off-screen. In this paper, we study the problem of multiagent time-series imputation in the context of human football play, where available past and future observations of subsets of agents are used to estimate missing observations for other agents. Our approach, called the Graph Imputer, uses past and future information in combination with graph networks and variational autoencoders to enable learning of a distribution of imputed trajectories. We demonstrate our approach on multiagent settings involving players that are partially-observable, using the Graph Imputer to predict the behaviors of off-screen players. To quantitatively evaluate the approach, we conduct experiments on football matches with ground truth trajectory data, using a camera module to simulate the off-screen player state estimation setting. We subsequently use our approach for downstream football analytics under partial observability using the well-established framework of pitch control, which traditionally relies on fully observed data. We illustrate that our method outperforms several state-of-the-art approaches, including those hand-crafted for football, across all considered metrics.

Predictors of selection into an elite level youth football academy: A longitudinal study

ABSTRACT Talent identification in football is complex. Research regarding talent indicators that predict selection into professional academies is increasingly multi-disciplinary, though is typically cross-sectional, failing to consider whether the rate of change of those indicators influences selection. The aim of this study was to determine if longitudinal changes in talent indicators are associated with selection into a professional football academy. A total of 110 Dutch male football players (aged 8–12) playing in a youth development programme of a professional club were included in the study. Players were assessed regularly on their anthropometry, physical fitness, gross motor coordination, technical ability, and psychosocial capacities over four years. A subset of players was then selected into the academy. Cross-sectional and longitudinal analyses revealed the indicators that best predicted selection. The best indicator was the 30 m sprint speed, with prediction not improving when including other predictors or their rate of change. The individuals that the club ultimately selected at age 12 could have been predicted well above chance levels using the sprint speeds at age nine or ten. The relative consistency of the rate of improvement in indicators across participants meant that the rate at which they developed played little role in selections.

Dynamic Programming for Computing Power Indices for Weighted Voting Games with Precoalitions

We study the efficient computation of power indices for weighted voting games with precoalitions amongst subsets of players (reflecting, e.g., ideological proximity) using the paradigm of dynamic programming. Starting from the state-of-the-art algorithms for computing the Banzhaf and Shapley–Shubik indices for weighted voting games, we present a framework for fast algorithms for the three most common power indices with precoalitions, i.e., the Owen index, the Banzhaf–Owen index and the symmetric coalitional Banzhaf index, and point out why our new algorithms are applicable for large numbers of players. We discuss implementations of our algorithms for the three power indices with precoalitions in C++ and review computing times, as well as storage requirements.

Weighted voting procedure having a unique blocker

The Goods and Services Tax (GST) Council of India has a non-conventional weighted voting procedure having a primary player who is a blocker and a set of secondary players. The voting weights are not fixed and are determined based on the subset of players which participate in the voting. We introduce the notion of voting schema to formally model such a voting procedure. Individual voting games arise from a voting schema depending on the subset of secondary players who participate in the voting. We make a detailed formal study of the trade-off between the minimal sizes of winning and blocking coalitions in the voting games that can arise from a voting schema. Finally, the GST voting procedure is assessed using the theoretical results leading to suggestions for improvement.

Disturbance Decoupling for Gradient-Based Multi-Agent Learning With Quadratic Costs

Motivated by applications of multi-agent learning in noisy environments, this paper studies the robustness of gradient-based learning dynamics with respect to disturbances. While disturbances injected along a coordinate corresponding to any individual player's actions can always affect the overall learning dynamics, a subset of players can be disturbance decoupled---i.e., such players' actions are completely unaffected by the injected disturbance. We provide necessary and sufficient conditions to guarantee this property for games with quadratic cost functions, which encompass quadratic one-shot continuous games, finite-horizon linear quadratic (LQ) dynamic games, and bilinear games. Specifically, disturbance decoupling is characterized by both algebraic and graph-theoretic conditions on the learning dynamics, the latter is obtained by constructing a game graph based on gradients of players' costs. For LQ games, we show that disturbance decoupling imposes constraints on the controllable and unobservable subspaces of players. For two player bilinear games, we show that disturbance decoupling within a player's action coordinates imposes constraints on the payoff matrices. Illustrative numerical examples are provided.

Metabolic Remodeling as a Way of Adapting to Tumor Microenvironment (TME), a Job of Several Holders.

The microenvironment depends and generates dependence on all the cells and structures that share the same niche, the biotope. The contemporaneous view of the tumor microenvironment (TME) agrees with this idea. The cells that make up the tumor, whether malignant or not, behave similarly to classes of elements within a living community. These elements inhabit, modify and benefit from all the facilities the microenvironment has to offer and that will contribute to the survival and growth of the tumor and the progression of the disease.The metabolic adaptation to microenvironment is a crucial process conducting to an established tumor able to grow locally, invade and metastasized. The metastatic cancer cells are reasonable more plastic than non-metastatic cancer cells, because the previous ones must survive in the microenvironment where the primary tumor develops and in addition, they must prosper in the microenvironment in the metastasized organ.The metabolic remodeling requires not only the adjustment of metabolic pathways per se but also the readjustment of signaling pathways that will receive and obey to the extracellular instructions, commanding the metabolic adaptation. Many diverse players are pivotal in cancer metabolic fitness from the initial signaling stimuli, going through the activation or repression of genes, until the phenotype display. The new phenotype will permit the import and consumption of organic compounds, useful for energy and biomass production, and the export of metabolic products that are useless or must be secreted for a further recycling or controlled uptake. In the metabolic network, three subsets of players are pivotal: (1) the organic compounds; (2) the transmembrane transporters, and (3) the enzymes.This chapter will present the "Pharaonic" intent of diagraming the interplay between these three elements in an attempt of simplifying and, at the same time, of showing the complex sight of cancer metabolism, addressing the orchestrating role of microenvironment and highlighting the influence of non-cancerous cells.

On Synchronous, Asynchronous, and Randomized Best-Response Schemes for Stochastic Nash Games

In this paper, we consider a stochastic Nash game in which each player minimizes a parameterized expectation-valued convex objective function. In deterministic regimes, proximal best-response (BR) schemes have been shown to be convergent under a suitable spectral property associated with the proximal BR map. However, a direct application of this scheme to stochastic settings requires obtaining exact solutions to stochastic optimization problems at each iteration. Instead, we propose an inexact generalization of this scheme in which an inexact solution to the BR problem is computed in an expected-value sense via a stochastic approximation (SA) scheme. On the basis of this framework, we present three inexact BR schemes: (i) First, we propose a synchronous inexact BR scheme where all players simultaneously update their strategies. (ii) Second, we extend this to a randomized setting where a subset of players is randomly chosen to update their strategies while the other players keep their strategies invariant. (iii) Third, we propose an asynchronous scheme, where each player chooses its update frequency while using outdated rival-specific data in updating its strategy. Under a suitable contractive property on the proximal BR map, we proceed to derive almost sure convergence of the iterates to the Nash equilibrium (NE) for (i) and (ii) and mean convergence for (i)–(iii). In addition, we show that for (i)–(iii), the generated iterates converge to the unique equilibrium in mean at a linear rate with a prescribed constant rather than a sublinear rate. Finally, we establish the overall iteration complexity of the scheme in terms of projected stochastic gradient (SG) steps for computing an [Formula: see text]-NE2 (or [Formula: see text]-NE∞) and note that in all settings, the iteration complexity is [Formula: see text], where [Formula: see text] in the context of (i), and c &gt; 0 represents the positive cost of randomization in (ii) and asynchronicity and delay in (iii). Notably, in the synchronous regime, we achieve a near-optimal rate from the standpoint of solving stochastic convex optimization problems by SA schemes. The schemes are further extended to settings where players solve two-stage stochastic Nash games with linear and quadratic recourse. Finally, preliminary numerics developed on a multiportfolio investment problem and a two-stage capacity expansion game support the rate and complexity statements.

A network reliability approach to the analysis of combinatorial repairable threshold schemes

A repairable threshold scheme (which we abbreviate to RTS) is a \begin{document}$ (\tau,n) $\end{document} -threshold scheme in which a subset of players can another player's share in the event that their share has been lost or corrupted. This will take place without the participation of the dealer who set up the scheme. The repairing protocol should not compromise the (unconditional) security of the threshold scheme. Combinatorial repairable threshold schemes (or combinatorial RTS) were recently introduced by Stinson and Wei [ 8 ]. In these schemes, multiple shares are distributed to each player, as defined by a suitable combinatorial design called the distribution design. In this paper, we study the reliability of these combinatorial repairable threshold schemes in a setting where players may not be available to take part in a repair of a given player's share. Using techniques from network reliability theory, we consider the probability of existence of an available repair set, as well as the expected number of available repair sets, for various types of distribution designs.

Decentralized Bayesian Learning in Dynamic Games: A Framework to Study Informational Cascades

We study the problem of Bayesian learning in a dynamical system involving strategic agents with asymmetric information. In a series of seminal papers in the literature, this problem has been investigated under a simplifying model where myopically selfish players appear sequentially and act once in the game, based on private noisy observations of the system state and public observation of past players’ actions. It has been shown that there exist information cascades where users discard their private information and mimic the action of their predecessor. In this paper, we provide a framework for studying Bayesian learning dynamics in a more general setting than the one described above. In particular, our model incorporates cases where players are non-myopic and strategically participate for the whole duration of the game, and cases where an endogenous process selects which subset of players will act at each time instance. The proposed framework hinges on a sequential decomposition methodology for finding structured perfect Bayesian equilibria (PBE) of a general class of dynamic games with asymmetric information, where user-specific states evolve as conditionally independent Markov processes and users make independent noisy observations of their states. Using this methodology, we study a specific dynamic learning model where players make decisions about public investment based on their estimates of everyone’s types. We characterize a set of informational cascades for this problem where learning stops for the team as a whole. We show that in such cascades, all players’ estimates of other players’ types freeze even though each individual player asymptotically learns its own true type.

The Impact of Cash Mobs in the Vote with the Wallet Game: Experimental Results

We simulate in a randomised lab experiment the effect of Cash Mobs on consumers’ behaviour in an original variant of the multiplayer Prisoner’s dilemma called Vote-with-the-wallet Game (VWG). The effect is modelled in a sequential game with/without an environmental frame in which a subset of players (cash-mobbers) is given the opportunity to reveal publicly (in aggregate without disclosing individual identities) their cooperation decision. We find that the treatment has a positive gross effect, that is, the share of cooperators is significantly higher in treated sessions and this is mainly due to the higher share of cooperators among cash-mobbers. Our results suggest that cash mobs-like mechanisms can help to solve social dilemmas with entirely private solutions (not based on punishment but on positive action) without costs for government budgets.

Combinatorial repairability for threshold schemes

In this paper, we consider methods whereby a subset of players in a $(k,n)$-threshold scheme can "repair" another player's share in the event that their share has been lost or corrupted. This will take place without the participation of the dealer who set up the scheme. The repairing protocol should not compromise the (unconditional) security of the threshold scheme, and it should be efficient, where efficiency is measured in terms of the amount of information exchanged during the repairing process. We study two approaches to repairing. The first method is based on the "enrollment protocol" from \cite{NSG} which was originally developed to add a new player to a threshold scheme (without the participation of the dealer) after the scheme was set up. The second method distributes "multiple shares" to each player, as defined by a suitable combinatorial design. This method results in larger shares, but lower communication complexity, as compared to the first method.

On the Information Ratio of Non-perfect Secret Sharing Schemes

A secret sharing scheme is non-perfect if some subsets of players that cannot recover the secret value have partial information about it. The information ratio of a secret sharing scheme is the ratio between the maximum length of the shares and the length of the secret. This work is dedicated to the search of bounds on the information ratio of non-perfect secret sharing schemes and the construction of efficient linear non-perfect secret sharing schemes. To this end, we extend the known connections between matroids, polymatroids and perfect secret sharing schemes to the non-perfect case. In order to study non-perfect secret sharing schemes in all generality, we describe their structure through their access function, a real function that measures the amount of information on the secret value that is obtained by each subset of players. We prove that there exists a secret sharing scheme for every access function. Uniform access functions, that is, access functions whose values depend only on the number of players, generalize the threshold access structures. The optimal information ratio of the uniform access functions with rational values has been determined by Yoshida, Fujiwara and Fossorier. By using the tools that are described in our work, we provide a much simpler proof of that result and we extend it to access functions with real values.

Targeted Information Release in Social Networks

As various firms initially make information and access to their products/services scarce within a social network, identifying influential players emerges as a pivotal step for their success. In this paper, we tackle this problem using a stylized model that features payoff externalities and local network effects. The network designer is allowed to release information to only a subset of players (leaders); these targeted players make their contributions first and the rest (followers) move subsequently after observing the leaders’ decisions. In the presence of incomplete information, the signaling incentive drives the optimal selection of leaders and can lead to a first-order materialistic effect on equilibrium contributions. We propose a novel index for the key leader selection with incomplete information that can be substantially different from the key player index in Ballester et al. (2006) [Ballester C, Calvó-Armengol A, Zenou Y (2006) Who’s who in networks. wanted: The key player. Econometrica 74(5):1403–1417] and the key leader index with complete information proposed in Zhou and Chen (2015) [Zhou J, Chen Y-J (2015) Key leaders in social networks. J. Econom. Theory 157:212–235]. We also show that in undirected graphs, the optimal leader group identified in Zhou and Chen (2015) is exactly the optimal follower group when signaling is present. In particular, if the graphs are complete, the network designer ranks the players by the ascending order of their intrinsic valuations, and the leaders are those with lower intrinsic valuations. In the out-tree hierarchical structure, the key leader turns out to be the one that stays in the middle, and it is not necessarily exactly the central player in the network.

Cooperation among agents with a proximity relation

A cooperative game consists of a set of players and a characteristic function determining the maximal gain or minimal cost that every subset of players can achieve when they decide to cooperate, regardless of the actions that the other players take. The relationships of closeness among the players should modify the bargaining among them and therefore their payoffs. The first models that have studied this closeness used a priori unions or undirected graphs. In the a priori union model a partition of the big coalition is supposed. Each element of the partition represents a group of players with the same interests. The groups negotiate among them to form the grand coalition and later, inside each one, players bargain among them. Now we propose to use proximity relations to represent leveled closeness of the interests among the players and extending the a priori unions model.