Evaluating Singleplayer and Multiplayer in Human Computation Games

Human computation games (HCGs) can provide novel solutions to intractable computational problems, help enable scientific breakthroughs, and provide datasets for artificial intelligence. However, our knowledge about how to design and deploy HCGs that appeal to players and solve problems effectively is incomplete. We present an investigatory HCG based on Super Mario Bros. We used this game in a human subjects study to investigate how different social conditions---singleplayer and multiplayer---and scoring mechanics---collaborative and competitive---affect players' subjective experiences, accuracy at the task, and the completion rate. In doing so, we demonstrate a novel design approach for HCGs, and discuss the benefits and tradeoffs of these mechanics in HCG design.Comment: 10 pages, 4 figures, 2 table

A Framework for Exploring and Evaluating Mechanics in Human Computation Games

Human computation games (HCGs) are a crowdsourcing approach to solving computationally-intractable tasks using games. In this paper, we describe the need for generalizable HCG design knowledge that accommodates the needs of both players and tasks. We propose a formal representation of the mechanics in HCGs, providing a structural breakdown to visualize, compare, and explore the space of HCG mechanics. We present a methodology based on small-scale design experiments using fixed tasks while varying game elements to observe effects on both the player experience and the human computation task completion. Finally we discuss applications of our framework using comparisons of prior HCGs and recent design experiments. Ultimately, we wish to enable easier exploration and development of HCGs, helping these games provide meaningful player experiences while solving difficult problems.Comment: 11 pages, 5 figure

Design and Evaluation of Intelligent Reward Structures in Human Computation Games

Despite the ubiquity of artificial intelligence, some problems and procedures— such as building commonsense knowledge understanding or generating creative works— have no or few effective algorithmic solutions, yet are considered straightforward for humans to solve. Human computation games (HCGs) are playful, game-based interfaces for tackling these problems through crowdsourcing. HCGs have been used to solve tasks that were and still are considered complex for computational algorithms such as image tagging, protein synthesis, 3D structure reconstruction, and creative artifact generation. However, despite these successes, HCGs have not seen broad adoption compared to other types of serious digital games. Among the many reasons for this lack of adoption is the reality that these games are typically not seen as engaging or compelling to play, as well as the fact that creating HCGs comes at a high development cost to task providers who are typically not game development experts. This thesis is a step towards building and establishing a more formalized design understanding of how to create HCGs that both provide a compelling player experience and complete the underlying task effectively. In this thesis, I explore reward mechanics in HCGs. Reward mechanics are integral to HCGs due their associations with player motivation, compensation, and task validation. I first propose a framework for understanding HCG mechanics and advocate for an experimental methodology evaluating both player experience and task completion metrics to understand variations in HCG mechanics. I then use these tools to frame and design three experiments that explore small-scale variations of reward systems in HCGs: reward functions, reward distribution, and reward personalization. These studies demonstrate that even small variations in rewards (i.e., offering players the ability to choose the type of reward) may have significant positive effects on both player experience and task completion metrics. I also show that some variations (i.e., co-located, competitive reward scoring) may have both positive and negative tradeoffs across these metrics. Moreover, this work observes that existing, anecdotal design wisdom for HCGs may not always hold (i.e., allowing players to verbally collude actually predicts higher task solution accuracy). Altogether, this thesis demonstrates that certain aspects of reward systems in HCGs can be varied to improve the player experience without compromising task completion metrics, and builds more empirically-tested design knowledge for creating more engaging, effective HCGs.Ph.D

Design of networked multiplayer snake and ladder educational game based on hash map and vector data structure

Computer games have been used as educational media or popularly named as educational games. However, most educational computer games that have been created can be played by one player. This study aims to build a multiplayer ladder and snake educational game focused on the server program as a moderator that handles players, player groups, and data traffic during the game is running. The game is built on Java socket programming and local area network (LAN) as a data communication medium between players. Whereas to handle the username and socket address of the whole player, the hash map data structure is used. A vector data structure is also used to manage data package sending for each player group. The experiment shows that the system works properly where the computer server’s performance is influenced by the specifications, especially the processor and random-access memory (RAM)

Validating generic metrics of fairness in game-based resource allocation scenarios with crowdsourced annotations

Being able to effectively measure the notion of fairness is of vital importance as it can provide insight into the formation and evolution of complex patterns and phenomena, such as social preferences, collaboration, group structures and social conflicts. This paper presents a comparative study for quantitatively modelling the notion of fairness in one-to-many resource allocation scenarios - i.e. one provider agent has to allocate resources to multiple receiver agents. For this purpose, we investigate the efficacy of six metrics and cross-validate them on crowdsourced human ranks of fairness annotated through a computer game implementation of the one-to-many resource allocation scenario. Four of the fairness metrics examined are well-established metrics of data dispersion, namely standard deviation, normalised entropy, the Gini coefficient and the fairness index. The fifth metric, proposed by the authors, is an ad-hoc context-based measure which is based on key aspects of distribution strategies. The sixth metric, finally, is machine learned via ranking support vector machines (SVMs) on the crowdsourced human perceptions of fairness. Results suggest that all ad-hoc designed metrics correlate well with the human notion of fairness, and the context-based metrics we propose appear to have a predictability advantage over the other ad-hoc metrics. On the other hand, the normalised entropy and fairness index metrics appear to be the most expressive and generic for measuring fairness for the scenario adopted in this study and beyond. The SVM model can automatically model fairness more accurately than any ad-hoc metric examined (with an accuracy of 81.86%) but it is limited by its expressivity and generalisability.Being able to effectively measure the notion of fairness is of vital importance as it can provide insight into the formation and evolution of complex patterns and phenomena, such as social preferences, collaboration, group structures and social conflicts. This paper presents a comparative study for quantitatively modelling the notion of fairness in one-to-many resource allocation scenarios - i.e. one provider agent has to allocate resources to multiple receiver agents. For this purpose, we investigate the efficacy of six metrics and cross-validate them on crowdsourced human ranks of fairness annotated through a computer game implementation of the one-to-many resource allocation scenario. Four of the fairness metrics examined are well-established metrics of data dispersion, namely standard deviation, normalised entropy, the Gini coefficient and the fairness index. The fifth metric, proposed by the authors, is an ad-hoc context-based measure which is based on key aspects of distribution strategies. The sixth metric, finally, is machine learned via ranking support vector machines (SVMs) on the crowdsourced human perceptions of fairness. Results suggest that all ad-hoc designed metrics correlate well with the human notion of fairness, and the context-based metrics we propose appear to have a predictability advantage over the other ad-hoc metrics. On the other hand, the normalised entropy and fairness index metrics appear to be the most expressive and generic for measuring fairness for the scenario adopted in this study and beyond. The SVM model can automatically model fairness more accurately than any ad-hoc metric examined (with an accuracy of 81.86%) but it is limited by its expressivity and generalisability.peer-reviewe

Making CNNs for Video Parsing Accessible

The ability to extract sequences of game events for high-resolution e-sport games has traditionally required access to the game's engine. This serves as a barrier to groups who don't possess this access. It is possible to apply deep learning to derive these logs from gameplay video, but it requires computational power that serves as an additional barrier. These groups would benefit from access to these logs, such as small e-sport tournament organizers who could better visualize gameplay to inform both audience and commentators. In this paper we present a combined solution to reduce the required computational resources and time to apply a convolutional neural network (CNN) to extract events from e-sport gameplay videos. This solution consists of techniques to train a CNN faster and methods to execute predictions more quickly. This expands the types of machines capable of training and running these models, which in turn extends access to extracting game logs with this approach. We evaluate the approaches in the domain of DOTA2, one of the most popular e-sports. Our results demonstrate our approach outperforms standard backpropagation baselines.Comment: 11 pages, 6 figures, Foundations of Digital Games 201

The Role of Mindfulness, Mind Wandering, Attentional Control, and Maladaptive Personality Traits in Problematic Gaming Behavior

Objectives Problematic gaming has become a phenomenon of growing clinical relevance due to its negative impact on life and mental health outcomes. Much research has been carried out on its complex aetiology, and some studies have suggested that dispositional mindfulness, mind wandering, attentional control, and maladaptive personality traits may play some role, but they have never been included in the same prediction model. This study used Gaussian graphical models and Bayesian networks to investigate the pattern of association of these constructs and of background and gaming-related variables with problematic gaming in a sample of adult gamers. Method Participants (n=506) were administered an online survey comprising a questionnaire on background and gaming-related variables and the Gaming Disorder Test, the Five Facet Mindfulness Questionnaire-15, the Mind WanderingSpontaneous and Deliberate scales, the Attention Control-Distraction and Shifting scales, and the Personality Inventory for DSM-5-Brief Form. Results Gaussian graphical models showed that problematic gaming was directly associated with Acting with Awareness, Disinhibition, Psychoticism, playing more than 30 hr a week, ability level, and playing strategy games. Bayesian networks indicated that the occurrence of high levels of problematic gaming directly depended on the presence of low scores on Acting with Awareness. Conclusions The results suggest that one key feature of problematic gamers can be a high level of spontaneous thinking, either in the form of mind wandering or in the lack of Acting with Awareness, while maladaptive personality traits and attentional control seem to play a less central role

Robottiautojen tutkimukseen tarkoitetun virtuaalisen koneoppimisympäristön suorituskyvyn evaluointi

While automotive manufacturers are already implementing Autonomous Driving (AD) features in their latest commercial vehicles, fully automated vehicles are still not a reality. In addition to AD, recent developments in mobile networks enables the possibility of Vehicle-to-Infrastructure (V2I) and Vehicle-to-Vehicle (V2V) communication. Vehicle-to-Everything (V2X) communication, or vehicular Internet of Things (IoT), can provide solutions that improve the safety and efficiency of traffic. Both AD and vehicular IoT need improvements to the surrounding infrastructure and vehicular hardware and software. The upcoming 5G network not only reduces latency, but improves availability and massively increases the amount of supported simultaneous connections, making vehicular IoT a possibility. Developing software for AD and vehicular IoT is difficult, especially because testing the software with real vehicles can be hazardous and expensive. The use of virtual environments makes it possible to safely test the behavior of autonomous vehicles. These virtual 3D environments include physics simulation and photorealistic graphics. Real vehicular hardware can be combined with these simulators. The vehicle driving software can control the virtual vehicle and observe the environment through virtual sensors, such as cameras and radars. In this thesis we investigate the performance of such simulators. The issue with existing open-source simulators is their insufficient performance for real-time simulation of multiple vehicles. When the simulation is combined with real vehicular hardware and edge computing services, it is important that the simulated environment resembles reality as closely as possible. As driving in traffic is very latency sensitive, the simulator should always be running in real-time. We select the most suitable traffic simulator for testing these multi-vehicle driving scenarios. We plan and implement a system for distributing the computational load over multiple computers, in order to improve the performance and scalability. Our results show that our implementation allows scaling the simulation by increasing the amount of computing nodes, and therefore increasing the number of simultaneously simulated autonomous vehicles. For future work, we suggest researching how the distributed computing solution affects latency in comparison to a real-world testing environment. We also suggest the implementation of an automated load-balancing system for automatically scaling the simulation to multiple computation nodes based on demand.Vaikka uusimmista automalleista löytyy jo itsestään ajavien autojen ominaisuuksia, robottiautot vaativat vielä runsaasti kehitystä ennen kuin ne kykenevät ajamaan liikenteessä täysin itsenäisesti. Robottiautojen ohella ajoneuvojen ja infrastruktuurin välinen (V2X) kommunikaatio ja tuleva 5G mobiiliverkkoteknologia sekä mobiiliverkkojen tukiasemien yhteyteen sijoitettavat laskentapilvet mahdollistavat liikenteen turvallisuuden ja sujuvuuden parantamisen. Tätä V2X kommunikaatiota voidaan esimerkiksi hyödyntää varoittamalla ajoneuvoja nurkan takaa tulevista pyöräilijöistä, jalankulkijoista ja huonoista tieolosuhteista. Robottiautojen ja V2X kommunikaation hyödyntämistä on hankala tutkia oikeassa liikenteessä. Fyysisten autojen ja tieverkostoa ympäröivän infrastruktuurin rakentaminen on kallista, lisäksi virhetilanteista johtuvat onnettomuudet voivat aiheuttaa henkilö- ja tavaravahinkoja. Yksi ratkaisu on virtuaalisten testausympäristöjen käyttö. Tällaiset simulaattorit kykenevät mallintamaan ajoneuvojen käyttäytymistä reaaliaikaisen fysiikkamoottorin avulla ja tuottamaan valokuvamaista grafiikkaa simulaatioympäristöstä. Robottiauton ohjelmisto voi hallita simuloidun auton käyttäytymistä ja havainnoida simuloitua ympäristöä virtuaalisten kameroiden ja tutkien avulla. Tässä diplomityössä tutkitaan liikennesimulaattorien suorituskykyä. Avoimen lähdekoodin simulaattorien ongelmana on niiden huono skaalautuvuus, eikä niiden suorituskyky riitä simuloimaan useita autoja reaaliajassa. Tässä diplomityössä tehdään lyhyt katsaus olemassa oleviin simulaattoreihin, joiden joukosta valitaan parhaiten yllämainittujen ongelmien tutkimiseen soveltuva simulaattori. Simulaattorin suorituskyvyn ja skaalautuvuuden parantamiseksi suunnitellaan järjestelmä, joka hajauttaa simulaattorin työkuorman useammalle laskentapisteelle. Kyseinen järjestelmä toteutetaan ja sen toimivuutta testataan mittaamalla. Mittaustulokset osoittavat, että hajautettu laskenta parantaa simulaattorin suorituskykyä ja että reaaliaikaisesti simuloitujen autojen lukumäärää voidaan kasvattaa lisäämällä laskentapisteiden lukumäärää. Jatkotutkimukseksi ehdotetaan tutkimaan simulaation hajauttamisen vaikutusta viiveisiin, ja kuinka simulaattorin aiheuttamat ylimääräiset viiveet suhtautuvat tosielämän viiveisiin. Lisäksi suositellaan automaattisen kuormituksentasaajan toteuttamista, jonka avulla simulaatiota voidaan automaattisesti hajauttaa useille laskentapisteille tarvittavan laskentakapasiteetin mukaisesti