Recommender systems for players of online video games

The content in this project is the approach, exploration, analysis and use of recommender systems to integrate an implementation of one system that learns the players’ behavior and recommends them to other players, to show recommender systems as a way of enhancing the player experience

Interpretable Contextual Team-aware Item Recommendation: Application in Multiplayer Online Battle Arena Games

The video game industry has adopted recommendation systems to boost users interest with a focus on game sales. Other exciting applications within video games are those that help the player make decisions that would maximize their playing experience, which is a desirable feature in real-time strategy video games such as Multiplayer Online Battle Arena (MOBA) like as DotA and LoL. Among these tasks, the recommendation of items is challenging, given both the contextual nature of the game and how it exposes the dependence on the formation of each team. Existing works on this topic do not take advantage of all the available contextual match data and dismiss potentially valuable information. To address this problem we develop TTIR, a contextual recommender model derived from the Transformer neural architecture that suggests a set of items to every team member, based on the contexts of teams and roles that describe the match. TTIR outperforms several approaches and provides interpretable recommendations through visualization of attention weights. Our evaluation indicates that both the Transformer architecture and the contextual information are essential to get the best results for this item recommendation task. Furthermore, a preliminary user survey indicates the usefulness of attention weights for explaining recommendations as well as ideas for future work. The code and dataset are available at: https://github.com/ojedaf/IC-TIR-Lol

An Ecosystem Framework for the Meta in Esport Games

This paper examines the evolving landscape of modern digital games, emphasizing their nature as live services that continually evolve and adapt. In addition to engaging with the core gameplay, players and other stakeholders actively participate in various game-related experiences, such as tournaments and streaming. This interplay forms a vibrant and intricate ecosystem, facilitating the construction and dissemination of knowledge about the game. Such knowledge flow, accompanied by resulting behavioral changes, gives rise to the concept of a video game meta. Within the competitive gaming context, the meta represents the strategic and tactical knowledge that goes beyond the fundamental mechanics of the game, enabling players to gain a competitive advantage. We present a review of the state-of-the-art of knowledge for game metas and propose a novel model for the meta knowledge structure and propagation that accounts for this ecosystem, based on a review of the academic literature and practical examples. By exploring the dynamics of knowledge exchange and its influence on gameplay, the review presented here sheds light on the intricate relationship between game evolution, player engagement, and the associated emergence of game meta

Human-Machine Collaborative Optimization via Apprenticeship Scheduling

Coordinating agents to complete a set of tasks with intercoupled temporal and resource constraints is computationally challenging, yet human domain experts can solve these difficult scheduling problems using paradigms learned through years of apprenticeship. A process for manually codifying this domain knowledge within a computational framework is necessary to scale beyond the ``single-expert, single-trainee" apprenticeship model. However, human domain experts often have difficulty describing their decision-making processes, causing the codification of this knowledge to become laborious. We propose a new approach for capturing domain-expert heuristics through a pairwise ranking formulation. Our approach is model-free and does not require enumerating or iterating through a large state space. We empirically demonstrate that this approach accurately learns multifaceted heuristics on a synthetic data set incorporating job-shop scheduling and vehicle routing problems, as well as on two real-world data sets consisting of demonstrations of experts solving a weapon-to-target assignment problem and a hospital resource allocation problem. We also demonstrate that policies learned from human scheduling demonstration via apprenticeship learning can substantially improve the efficiency of a branch-and-bound search for an optimal schedule. We employ this human-machine collaborative optimization technique on a variant of the weapon-to-target assignment problem. We demonstrate that this technique generates solutions substantially superior to those produced by human domain experts at a rate up to 9.5 times faster than an optimization approach and can be applied to optimally solve problems twice as complex as those solved by a human demonstrator.Comment: Portions of this paper were published in the Proceedings of the International Joint Conference on Artificial Intelligence (IJCAI) in 2016 and in the Proceedings of Robotics: Science and Systems (RSS) in 2016. The paper consists of 50 pages with 11 figures and 4 table

Social software for music

Tese de mestrado integrado. Engenharia Informática e Computação. Faculdade de Engenharia. Universidade do Porto. 200

Dynamic Personalization of Gameful Interactive Systems

Gameful design, the process of creating a system with affordances for gameful experiences, can be used to increase user engagement and enjoyment of digital interactive systems. It can also be used to create applications for behaviour change in areas such as health, wellness, education, customer loyalty, and employee management. However, existing research suggests that the qualities of users, such as their personality traits, preferences, or identification with a task, can influence gamification outcomes. It is important to understand how to personalize gameful systems, given how user qualities shape the gameful experience. Current evidence suggests that personalized gameful systems can lead to increased user engagement and be more effective in helping users achieve their goals than generic ones. However, to create these kinds of systems, designers need a specific method to guide them in personalizing the gameful experience to their target audience. To address this need, this thesis proposes a novel method for personalized gameful design divided into three steps: (1) classification of user preferences, (2) classification and selection of gameful design elements, and (3) heuristic evaluation of the design. Regarding the classification of user preferences, this thesis evaluates and validates the Hexad Gamification User Types Scale, which scores a person in six user types: philanthropist, socialiser, free spirit, achiever, player, and disruptor. Results show that the scale’s structural validity is acceptable for gamification studies through reliability analysis and factor analysis. For classification and selection of gameful design elements, this thesis presents a conceptual framework based on participants’ self-reported preferences, which classifies elements in eight groups organized into three categories: individual motivations (immersion and progression), external motivations (risk/reward, customization, and incentives), and social motivations (socialization, altruism, and assistance). And to evaluate the design of gameful applications, this thesis introduces a set of 28 gameful design heuristics, which are based on motivational theories and gameful design methods and enable user experience professionals to conduct a heuristic evaluation of a gameful application. Furthermore, this thesis describes the design, implementation, and pilot evaluation of a software platform for the study of personalized gameful design. It integrates nine gameful design elements built around a main instrumental task, enabling researchers to observe and study the gameful experience of participants. The platform is flexible so the instrumental task can be changed, game elements can be added or removed, and the level and type of personalization or customization can be controlled. This allows researchers to generate different experimental conditions to study a broad range of research questions. Our personalized gameful design method provides practical tools and clear guidelines to help designers effectively build personalized gameful systems

Adaptive games for learner and systems (bidirectional) learning

Thesis (PhD)--Stellenbosch University, 2022.ENGLISH ABSTRACT:Traditional learning environments are ineffective and inefficient and are failing to adequately equip students and employees with the knowledge and skills required in today’s jobs, let alone prepare them for the jobs of tomorrow. Given the rapidly changing landscapes of technologies and business models, organisations need to be flexible and adaptable to respond to, and even pre-empt future demands. One of the primary shortcomings of existing learning environments is their inflexibility and the ‘one size fits all’ approach followed. Serious games and game-based learning are widely recognised for their potential in providing more effective learning environments, especially when designed in a personalised, adaptive manner, and are explored in this dissertation. In addition to adapting to the individual traits and preferences of users, games are also highly context dependent. Whilst there is a great deal of literature and documented case studies of game-based learning, most focus only on the implementation of one particular game in a specific context. Whilst many existing game design models and approaches focus on achieving improved learning outcomes of learners, there is an opportunity to consider the impact of gameplay on other stakeholders and drive the active development of meta-skills in various stakeholders. Bidirectional learning, where learning simultaneously takes place in a two-way direction [295], has great potential and has, to date, not been incorporated in serious game design. By integrating different perspectives and variable scenarios, the dynamic personalisation of learning trajectories may be possible. Serious games offer a potential platform to aggregate learner behaviours and results, and use these to dynamically configure, adjust and tailor the game to individuals and contexts, ultimately providing a learning environment of improved quality, effectiveness and efficiency. In this dissertation, adaptive, bidirectional games are explored as a means to provide more effective and efficient learning environments for multiple stakeholders. Moreover, an architecture is presented to support the creation of such games for specific scenarios in a faster, more effective and more efficient manner. Following a research-by-design approach, the architecture is iteratively developed and simultaneously applied in four case studies. Experiences and learnings from each case study are infused into subsequent design iterations of the architecture. The architecture allows users to explore and exploit the solution space more deliberately and better understand the various functions and the interrelations between them. The flexible and modular structure of the architecture allows users to prioritise functionalities as required in the given scenario. Furthermore, the directional relations between functions can be interpreted and prioritised as needed given the specific context and requirements. The architecture incorporates various stakeholders in the design process, leading to greater transparency and better understanding throughout the process. More importantly, it emphasises bidirectional learning whereby different stakeholders can learn from gameplay and the aggregated results and behaviours of players.AFRIKAANS OPSOMMING: Tradisionele leeromgewings is oneffektief en ondoeltreffend en slaag nie daarin om studente en werknemers voldoende toe te rus met die kennis en vaardighede wat in die huidige werk benodig is nie, en nog minder vir toekomstige werk. Gegewe die vinnig veranderende landskappe van tegnologie¨e en sakemodelle, moet organisasies buigsaam en aanpasbaar wees om te reageer op, en selfs toekomstige behoeftes te voorkom. Een van die belangrikste tekortkominge van bestaande leeromgewings is die onbuigsaamheid daarvan asook die ‘een grootte pas almal’ benadering wat gevolg word. Ernstige speletjies en spelgebaseerde leer word oor die algemeen erken vir hul potensiaal om meer effektiewe leeromgewings te skep, veral as dit op ’n persoonlike, aanpasbare manier ontwerp is, en word in hierdie proefskrif ondersoek. Benewens die aanpassing by die individuele eienskappe en voorkeure van gebruikers, is speletjies ook baie kontekstafhanklik. Alhoewel daar baie literatuur en gedokumenteerde gevallestudies oor spelgebaseerde leer is, fokus die meeste daarvan slegs op die implementering van een spesifieke spel in ’n spesifieke konteks. Alhoewel baie bestaande spelontwerpmodelle en -benaderings op die verbeterde leeruitkomste van leerders focus, is daar ’n geleentheid om die impak van spel op ander belanghebbendes te oorweeg en die aktiewe ontwikkeling van metavaardighede by verskeie belanghebbendes te dryf. Tweerigtingleer, waar leer gelyktydig in twee rigtinge plaasvind [295], het ’n groot potensiaal en is huidig nog nie in ernstige spelontwerp opgeneem nie. Deur die integrasie van verskillende perspektiewe en veranderlike scenario’s, word die dinamiese personalisering van leertrajekte moontlik. Ernstige speletjies bied ’n moontlike platform om leerdergedrag en -resultate saam te voeg, en dit te gebruik om die spel dinamies te konfigureer en aan te pas by individue en kontekste, wat ’n leeromgewing van verbeterde kwaliteit, effektiwiteit en doeltreffendheid bied. In hierdie proefskrif word aanpasbare, tweerigting speletjies ondersoek as ’n manier om meer effektiewe en doeltreffende leeromgewings vir verskeie belanghebbendes te bied. Boonop word ’n argitektuur aangebied om die skep van sulke speletjies vir spesifieke scenario’s vinniger, meer effektief en doeltreffender te ondersteun. Na aanleiding van ’n navorsing-deur-ontwerp benadering word die argitektuur iteratief ontwikkel en gelyktydig toegepas in vier gevallestudies. Ervarings en leerstellings uit elke gevallestudie word ingesluit in die daaropvolgende ontwerp iterasies van die argitektuur. Met die argitektuur kan gebruikers die oplossingsruimte doelbewus ondersoek en benut, en die verskillende funksies en onderlinge verwantskappe tussen hulle beter verstaan. Die buigsame en modulˆere struktuur van die argitektuur stel gebruikers in staat om funksionaliteite te prioritiseer soos vereis in die gegewe scenario. Verder kan die rigtingverhoudinge tussen funksies ge¨ınterpreteer en geprioritiseer word soos benodig, gegewe die spesifieke konteks en vereistes. Die argitektuur bevat verskillende belanghebbendes in die ontwerpproses, wat lei tot verbeterde deursigtigheid en begrip gedurende die proses. Belangriker nog, dit beklemtoon tweerigtingleer waardeur verskillende belanghebbendes kan leer deur die spel en die saamgestelde resultate en gedrag van spelers.Doctora