Allowing two moves in succession increases the game's bias: A theorem

Chess is probably the best known example of a game which is “biased”—in the sense that whoever starts the game has an advantage. From the commonsense viewpoint, the resulting bias should be the same whether we allow the players to play as usual: 1st, 2nd, 1st, 2nd, or whether we allow each player to make two moves at the same time. However, in practice, if we allow each player to make two moves in succession, the bias increases. In this paper, we provide a theoretical explanation for this empirical phenomenon. © 2001 John Wiley & Sons, Inc.     

Digital Game Dynamics Preferences and Player Types

In this study, we examine digital game preferences by identifying game dynamics, i.e. player–game interaction modes, of 700 contemporary digital games, and players' (N = 1717) desire to play games with specific types of dynamics. Based on statistical analysis of the data, 5 game dynamics preference categories (“assault,” “manage,” “journey,” “care,” and “coordinate”) and 7 player types were revealed. The results show that identifying player types requires including both preferred and undesired game dynamics categories in the analysis. The findings unveil digital gaming as a more multifaceted phenomenon than common stereotypes suggest. The original game preferences model we present in this study can be conceptualized as a complementary approach for motivations to play and player behavior studies.     

Road Pricing Design Based on Game Theory and Multi-agent Consensus

Consensus theory and noncooperative game theory respectively deal with cooperative and noncooperative interactions among multiple players/agents. They provide a natural framework for road pricing design, since each motorist may myopically optimize his or her own utility as a function of road price and collectively communicate with his or her friends and neighbors on traffic situation at the same time. This paper considers the road pricing design by using game theory and consensus theory. For the case where a system supervisor broadcasts information on the overall system to each agent, we present a variant of standard fictitious play called average strategy fictitious play (ASFP) for large-scale repeated congestion games. Only a weighted running average of all other players' actions is assumed to be available to each player. The ASFP reduces the burden of both information gathering and information processing for each player. Compared to the joint strategy fictitious play (JSFP) studied in the literature, the updating process of utility functions for each player is avoided. We prove that there exists at least one pure strategy Nash equilibrium for the congestion game under investigation, and the players' actions generated by the ASFP with inertia (players' reluctance to change their previous actions) converge to a Nash equilibrium almost surely. For the case without broadcasting, a consensus protocol is introduced for individual agents to estimate the percentage of players choosing each resource, and the convergence property of players' action profile is still ensured. The results are applied to road pricing design to achieve socially local optimal trip timing. Simulation results are provided based on the real traffic data for the Singapore case study.      

Evolving Nash-optimal poker strategies using evolutionary computation

This paper focuses on the development of a competitive computer player for the one versus one Texas Hold’em poker using evolutionary algorithms (EA). A Texas Hold’em game engine is first constructed where an efficient odds calculator is programmed to allow for the abstraction of a player’s cards, which yield important but complex information. Effort is directed to realize an optimal player that will play close to the Nash equilibrium (NE) by proposing a new fitness criterion. Preliminary studies on a simplified version of poker highlighted the intransitivity nature of poker. The evolved player displays strategies that are logical but reveals insights that are hard to comprehend e.g., bluffing. The player is then benchmarked against Poki and PSOpti, which is the best heads-up Texas Hold’em artificial intelligence to date and plays closest to the optimal Nash equilibrium. Despite the much constrained chromosomal strategy representation, simulated results verified that evolutionary algorithms are effective in creating strategies that are comparable to Poki and PSOpti in the absence of expert knowledge.     

Player target tracking and detection in football game video using edge computing and deep learning

The purpose is to explore the player detection and motion tracking in football game video based on edge computing and deep learning (DL), thus improving the detection effect of player trajectory in different scenes. First, the basic technology of player target tracking and detection task is analyzed based on the Histograms of Oriented Gradients feature. Then, the neural network structure in DL is combined with the target tracking method to improve the miss detection problem of the Faster R-CNN (FRCN) algorithm in detecting small targets. Edge computing places massive computing nodes close to the terminal devices to meet the high computing and low latency requirements of DL on edge devices. After the occlusion problem in the football game is analyzed, the optimized algorithm is applied to the public dataset OTB2013 and the football game dataset containing 80 motion trajectories. After testing, the target tracking accuracy of the improved FRCN is 89.1%, the target tracking success rate is 64.5%, and the running frame rate is still about 25 fps. The high confidence of FRCN algorithm also avoids template pollution. In the ordinary scene, the FRCN algorithm basically does not lose the target. The area under curve value of the proposed FRCN algorithm decreases slightly in the scene where the target is occluded. The FRCN algorithm based on DL technology can achieve the target tracking of players in football game video and has strong robustness to the situation of players occlusion. The designed target detection algorithm is applied to the football game video, which can better analyze the technical characteristics of players, promote the development of football technology, bring different viewing experiences to the audience, drive the development of economic products derived from football games, and promote the dissemination and promotion of football.

     

Behavlets: a method for practical player modelling using psychology-based player traits and domain specific features

As player demographics broaden it has become important to understand variation in player types. Improved player models can help game designers create games that accommodate a range of playing styles, and may also facilitate the design of systems that detect the currently-expressed player type and adapt dynamically in real-time. Existing approaches can model players, but most focus on tracking and classifying behaviour based on simple functional metrics such as deaths, specific choices, player avatar attributes, and completion times. We describe a novel approach which seeks to leverage expert domain knowledge using a theoretical framework linking behaviour and game design patterns. The aim is to derive features of play from sequences of actions which are intrinsically informative about behaviour—which, because they are directly interpretable with respect to psychological theory of behaviour, we name ‘Behavlets’. We present the theoretical underpinning of this approach from research areas including psychology, temperament theory, player modelling, and game composition. The Behavlet creation process is described in detail; illustrated using a clone of the well-known game Pac-Man, with data gathered from 100 participants. A workshop-based evaluation study is also presented, where nine game design expert participants were briefed on the Behavlet concepts and requisite models, and then attempted to apply the method to games of the well-known first/third-person shooter genres, exemplified by ‘Gears of War’, (Microsoft). The participants found 139 Behavlet concepts mapping from behavioural preferences of the temperament types, to design patterns of the shooter genre games. We conclude that the Behavlet approach has significant promise, is complementary to existing methods and can improve theoretical validity of player models.     

Modeling outcomes of violent video game play: Applying mental models and model matching to explain the relationship between user differences,game characteristics,enjoyment, and aggressive intentions

While much research on video games has focused on aggressive outcomes, the extant research has not come to consensus on the role of other factors, notably enjoyment, frustration and individual differences amongst players, in mediating the link between violent game play and aggression. This experimental design uses a mental models approach to examine game features (e.g., controller naturalness), player characteristics (e.g., first person shooter experience, sex, degree of competitiveness) and the game play experience (e.g., perceived game realism, immersion, game failure, frustration and enjoyment) to explain aggressive outcomes. Results from a revised path model suggest perceived realism, immersion, and experience with other first person shooters all positively predicted enjoyment while frustration negatively predicted game enjoyment. Frustration and enjoyment both predicted state aggression. The findings of this study support the application of mental models as a theoretical approach to unify thinking about violent games, contextual features of violent games, individual difference variables and differences in player perceptions and game experiences with regards to outcome aggression. Results are discussed in terms of the model matching hypothesis and implications for the comprehensive study of violent game play, including the importance of enjoyment and frustration, are also discussed.     

基于增强现实技术的AR赛车游戏

AR赛车是一款融入了增强现实技术的3D赛车游戏。在赛车游戏中运用增强现实技术．使得游戏有了全新的控制方式和场景效果。在游戏控制方式上,玩家不再需要用键盘来控制赛车,而是通过在摄像头前操纵有特定标识图案的纸片来控制赛车;在场景效果上,AR赛车不再像传统游戏那样始终处于虚拟场景中,而是采用真实场景与虚拟物体相结合。这些特点将会给玩家带来全新的游戏体验。     

Improving player experience in Computer Games by using players' behavior analysis and linguistic descriptions

Understanding player behavior has an interest to computer games researchers and developers since it allows them to improve the design and implementation of computer games and also to ensure that players have the expected experiences. Currently this knowledge is not usually reported to players as feedback, although sometimes it is already used as an analysis tool. This paper presents a novel technology for automatically generating linguistic reports and immediate feedback from actions performed by players during play sessions. These reports allow developers to provide players with a more complete and personalized feedback about their behaviors, abilities, attitudes, skills or movements. In order to show and explore the possibilities of this new technology, we have incorporated it in the core of a computer game. We have evaluated positively that the incorporation of this kind of feedback into the core of YADY computer game allows us to improve the overall player experience.     

Billiard Combat Modeling and Simulation Based on Optimal Cue Placement Control and Strategic Planning

The selection of a best sequential shots for a given start cue position is a major challenging task in a billiard game. A new algorithm is proposed as a strategy to apply maximum tolerance angle search sequentially. The strategy considers combinations among all pockets and target object balls during both the pre and post collision shots selection processes. A simulation program is developed to test the strategy in a competition scenario by players with different proficiencies. The level of proficiency of players in the competition is controlled by a threshold value as a criterion to evaluate capability to conduct consecutive shots and when to give out right of play. The winning score of each game (win rate) is used as a performance comparison index among different gaming situations and to verify the effectiveness of the algorithm. The initial results of several simulation games using our strategy show that higher proficiency player can out beat lower proficiency player easily. This is consistent with the gaming situation in the real world, showing the consistency of our simulation program. The simulation also verifies that the play order does decide the final competition outcomes, when the players?? proficiencies are close to each other. This work is the first to investigate the effects of consecutive shots and order of play on the billiard gaming results. A low cost training system is proposed to verify the efficiency of the repositioning algorithm in real world settings. The system adapts an augmented reality technology to instruct users for reliable aiming assistance. It makes use of a vision system for cue ball, object ball locations and cue stick velocity tracking. In all, the simulation program can provide an initial proof of the effectiveness of the reposition algorithm in the competition situation. Experiments results of maximum tolerance angle all pocket search strategy using our training facility as tested by users with different skill levels all out performed the results without guidance for the set of users with the same proficiency.     

On frame rate and player performance in first person shooter games

The rate at which frames are rendered in a computer game directly impacts player performance, influencing both the game playability and enjoyability. However, despite the importance of frame rate and the wide-spread popularity of computer games, to the best of our knowledge, there is little quantitative understanding of the effects of frame rate on player performance in computer games. This paper provides a unique classification of actions in First Person Shooter (FPS) games based on interaction requirements that allow qualitative assessment of the impact of frame rates on player performance. This qualitative assessment is supported by quantitative analysis from two large user studies that measure the effects of frame rate on the fundamental player actions in a FPS game. Nearly 100 users participated in the two user study experiments, providing performance and perception data over a range of frame rates commonly studied for video streaming and inclusive of frame rates found in many computer game platforms. In general, the analysis shows that actions that require precise, rapid response, such as shooting, are greatly impacted by degradations in frame rates, while actions with lower precision and response requirements, such as moving, are more tolerant of low frame rates. These insights into the effects of frame rates on player performance can guide players in their choice for game settings and new hardware purchases, and inform system designers in their development of new hardware.     

The multivoicedness of game play: Exploring the unfolding of a student’s learning trajectory in a gaming context at school

The purpose of this article is to gain knowledge about how interactions in a gaming context become constituted as effective resources for a student’s learning trajectory. In addition, this detailed study of a learning trajectory documents how a computer game becomes a learning resource for working on a specific topic in school. The article reports on a qualitative study of students at an upper secondary school who have played the computer game Global Conflicts: Palestine to learn about the complexity of the Israeli-Palestinian conflict. A sociocultural and dialogic approach to learning and meaning-making is employed as an analytical framework. Analyzing different interactional episodes, in which important orientations and reorientations are located, documents how the student’s learning trajectory developed and changed during the project. When engaged in game play in educational settings, experiences with playing computer games outside of school can relevantly be invoked and become part of the collaborative project of finding out how to play the game. However, these ways of engaging with a computer game might not necessarily facilitate a subtle understanding of the specific topic that is addressed in the game. The findings suggest that the constitution of a computer game as a learning resource is a collaborative project, in which multiple resources for meaning-making are in play, and for which the teacher has an important role in facilitating student’s adoption of a multiperspective on the conflict. Furthermore, the findings shed light on what characterizes student-teacher interactions that contribute to a subtle understanding, and offer a framework for important issues upon which to reflect in game-based learning (GBL).     

The Trojan Player Typology: A cross-genre,cross-cultural,behaviorally validated scale of video game play motivations

While many video game researchers have built scales to tackle the motivations that people have for playing video games, these scales are often limited by their focus on specific game genres or player cultures as well as their lack of behavioral validation. The present research offers a new scale for player motivations and then examines its validity across two distinct gaming genres and cultures, drawing from server-side data combined with survey data of 18,627 players of the Multiplayer Online Battle Arena League of Legends and 18,819 players of the Chinese Massively Multiplayer Online Game Chevalier’s Romance Online 3. Six types of player motivations were found: socializer, completionist, competitor, escapist, story-driven, and smarty-pants. Consistent with previous research on player motivations, this typology offers new insights into why people play video games and how player motivations can be used to infer players’ in-game behaviors.     

Soft-computing-based emotional expression mechanism for game of computer Go

The game of Go is considered one of the most complicated games in the world. One Go game is divided into three stages: the opening, the middle, and the ending stages. Millions of people regularly play Go in countries around the world. The game is played by two players. One is White and another is Black. The players alternate placing one of their stones on an empty intersection of a square grid-patterned game board. The player with more territory wins the game. This paper proposes a soft-computing-based emotional expression mechanism and applies it to the game of computer Go to make Go beginners enjoy watching Go game and keep their tension on the game. First, the knowledge base and rule base of the proposed mechanism are defined by following the standards of the fuzzy markup language. The soft-computing mechanism for Go regional alarm level is responsible for showing the inferred regional alarm level to Go beginners. Based on the inferred board situation, the fuzzy inference mechanisms for emotional pleasure and arousal are responsible for inferring the pleasure degree and arousal degree, respectively. An emotional expression mapping mechanism maps the inferred degree of pleasure and degree of arousal into the emotional expression of the eye robot. The protocol transmission mechanism finally sends the pre-defined protocol to the eye robot via universal serial bus interface to make the eye robot express its emotional motion. From the experimental results, it shows that the eye robot can support Go beginners to have fun and retain their tension while watching or playing a game of Go.     

Scheduling search procedures: The wheel of fortune

Suppose that a player can make progress on n jobs, and her goal is to complete a target job among them, as soon as possible. Unfortunately she does not know what the target job is, perhaps not even if the target exists. This is a typical situation in searching and testing. Depending on the player’s prior knowledge and optimization goals, this gives rise to various optimization problems in the framework of game theory and, sometimes, competitive analysis. Continuing earlier work on this topic, we study another two versions. In the first game, the player knows only the job lengths and wants to minimize the completion time. A simple strategy that we call wheel-of-fortune (WOF) is optimal for this objective. A slight and natural modification, however makes this game considerably more difficult: If the player can be sure that the target is present, WOF fails. However, we can still construct in polynomial time an optimal strategy based on WOF. We also prove that the tight absolute bounds on the expected search time. In the final part, we study two competitive-ratio minimization problems where either the job lengths or the target probabilities are known. We show their equivalence, describe the structure of optimal strategies, and give a heuristic solution.     

Graphical Congestion Games

We consider congestion games with linear latency functions in which each player is aware only of a subset of all the other players. This is modeled by means of a social knowledge graph G in which nodes represent players and there is an edge from i to j if i knows j. Under the assumption that the payoff of each player is affected only by the strategies of the adjacent ones, we first give a complete characterization of the games possessing pure Nash equilibria. Namely, if the social graph G is undirected, the game is an exact potential game and thus isomorphic to a classical congestion game. As a consequence, it always converges and possesses Nash equilibria. On the other hand, if G is directed an equilibrium is not guaranteed to exist, but the game is always convergent and an equilibrium can be found in polynomial time if G is acyclic, even if finding the best equilibrium remains an intractable problem.     

Interactive strategy sets in multiple payoff games

In game theory, it is usually assumed that each player has only one payoff function and the strategy set of the game is composed of the topological product of individual players’ strategy sets. In real business and system design or control problems, however, players’ strategy sets may be interactive and each player may have more than one payoff function. This paper, investigates the more general situation of multiple payoff and multiple person games in a normal form. In this paper, each player has several payoff functions which are dominated by certain convex cones, and the feasible strategy set of each player may be interactive with those of the other players. This new model is applied to a classical example without requiring variational and quasi-variational inequalities, or point-to-set mappings.     

Existence of a feedback equilibrium for two-stage Stackelberg games

In this paper we consider a two-stage feedback game in which two players solve a Stackelberg problem at each stage and each player knows the state of the game at every level of play. In this kind of game, the leader does not have the ability to announce his strategy at all levels of play prior to the start of the game. Without assuming that at each stage the Stackelberg problem has a unique solution, we define a concept of “feedback Stackelberg solution” and give sufficient conditions to get existence of such a solution     

Game-theoretic axioms for local rationality and bounded knowledge

We present an axiomatic approach for a class of finite, extensive form games of perfect information that makes use of notions like rationality at a node and knowledge at a node. We distinguish between the game theorist's and the players' own theory of the game. The latter is a theory that is sufficient for each player to infer a certain sequence of moves, whereas the former is intended as a justification of such a sequence of moves. While in general the game theorist's theory of the game is not and need not be axiomatized, the players' theory must be an axiomatic one, since we model players as analogous to automatic theorem provers that play the game by inferring (or computing) a sequence of moves. We provide the players with an axiomatic theory sufficient to infer a solution for the game (in our case, the backwards induction equilibrium), and prove its consistency. We then inquire what happens when the theory of the game is augmented with information that a move outside the inferred solution has occurred. We show that a theory that is sufficient for the players to infer a solution and still remains consistent in the face of deviations must be modular. By this we mean that players have distributed knowledge of it. Finally, we show that whenever the theory of the game is group-knowledge (or common knowledge) among the players (i.e., it is the same at each node), a deviation from the solution gives rise to inconsistencies and therefore forces a revision of the theory at later nodes. On the contrary, whenever a theory of the game is modular, a deviation from equilibrium play does not induce a revision of the theory.A former version of this paper was presented at the Center for Rationality and Interactive Decision Theory at the Hebrew University of Jerusalem. A subsequent version has been presented at the Nobel Symposium on Game Theory held in Björkborn, Sweden, in June 1993. We would like to thank Martin Dufwenberg, Itzhak Gilboa, Sergiu Hart, Bart Lipman, Dov Samet, Shmuel Zamir and especially Robert Aumann for many useful comments.     

Development of a computer game-based framework for cognitive behaviour identification by using Bayesian inference methods

In this work a novel technique for cognitive behavioural data acquisition via computer/console games is introduced by which the user feels more relax than s/he is in a formal environment (e.g., labs and clinics) and has less disruption as s/he provides cognitive data sequence by playing a game. The method can be adapted into any game and is based on the assumption that in this way more efficient analysis of mind can be made to unveil the cognitive or mental characteristics of an individual. In experiments of the proposed work a commercial console game was utilised by different users to complete the tasks in which each game player followed his/her own optional scenarios of the game for a certain period of time. The attributes were then extracted from the behavioural video data sequence by visual inspection where each one corresponds to user’s behavioural characteristics spotted throughout the game and then analysed by the Bayesian network utility. At the end of all the experiments, two types of results were obtained: semantic representation of behavioural attributes and classification of personal behavioural characteristics. The approach is proved to be a unique way and helped identify general and specific behavioural characteristics of the individuals and is likely to open new areas of applications.