The Personalization Paradox: the Conflict between Accurate User Models and Personalized Adaptive Systems

Personalized adaptation technology has been adopted in a wide range of digital applications such as health, training and education, e-commerce and entertainment. Personalization systems typically build a user model, aiming to characterize the user at hand, and then use this model to personalize the interaction. Personalization and user modeling, however, are often intrinsically at odds with each other (a fact some times referred to as the personalization paradox). In this paper, we take a closer look at this personalization paradox, and identify two ways in which it might manifest: feedback loops and moving targets. To illustrate these issues, we report results in the domain of personalized exergames (videogames for physical exercise), and describe our early steps to address some of the issues arisen by the personalization paradox.Comment: arXiv admin note: substantial text overlap with arXiv:2101.1002

DDA PADA MUSUH BERBASIS SKOR MENGGUNAKAN LOGIKA FUZZY

Pada saat ini, banyak orang telah bermain game. Terdapat beberapa faktor dalam permainan yang membuat orang dapat menikmati permainan seperti graphics interface, latar belakang cerita, perangkat input permainan, dan game balancing. Dalam sebuah permainan dengan perhitungan skor yang bersifat statik atau berdasarkan rule-rule yang bersifat tegas mengakibatkan tingkat kesulitan dari sebuah permainan monoton atau tidak imbang antara pemain. Dimana tingkat kesulitan permainan untuk pemain pemula dan berpengalaman sama. Hal ini menyebabkan pemain pemula akan merasa frustasi karena permainan terlalu susah, sedangkan pemain berpengalaman akan merasa bosan karena permainan tersebut terlalu mudah untuk dimainkan. Pada penelitian ini dihasilkan suatu sistem penilaian skor yang dinamik menggunakan logika fuzzy untuk menentukan tingkat kesulitan pada musuh dalam permainan. Dimana penentuan tingkat kesulitan dalam permainan disesuaikan dengan kemampuan pemain. Kemampuan pemain didapat dari suatu sistem penilaian skor menggunakan logika fuzzy. Sistem penilaian skor permainan ini dibuat berdasarkan beberapa kriteria yaitu seberapa sering pemain terluka, item yang didapatkan pemain, berapa banyak musuh yang dibunuh, berapa kali pemain mengulang permainan dan waktu yang dibutuhkan untuk menyelesaikan satu stage permainan. Dalam penelitian ini dilakukan perbandingan dua metode yaitu metode tsukamoto dan sugeno,dimana hasil penilaian skor menggunakan sugeno lebih baik dari tsukamot

Dynamic Difficulty Adjustment in Procedural Content Generation

The world of game development has gone through many chapters since its birth. Many complex techniques, designed to give every player a personal game experience, have been developed by those who love to create. This project explores modern Procedural Content Generation and Dynamic Difficulty Adjustment techniques. The algorithm developed in the course of this project is designed to increase developer and player quality of life. Through adaptive generation, this project will explore how players engage with games and what developers can do to add to the player experience

Multi-Modal Data Analysis Based Game Player Experience Modeling Using LSTM-DNN

Game player modeling is a paradigm of computational models to exploit players’ behavior and experience using game and player analytics. Player modeling refers to descriptions of players based on frameworks of data derived from the interaction of a player’s behavior within the game as well as the player’s experience with the game. Player behavior focuses on dynamic and static information gathered at the time of gameplay. Player experience concerns the association of the human player during gameplay, which is based on cognitive and affective physiological measurements collected from sensors mounted on the player’s body or in the player’s surroundings. In this paper, player experience modeling is studied based on the board puzzle game “Candy Crush Saga” using cognitive data of players accessed by physiological and peripheral devices. Long Short-Term Memory-based Deep Neural Network (LSTM-DNN) is used to predict players’ effective states in terms of valence, arousal, dominance, and liking by employing the concept of transfer learning. Transfer learning focuses on gaining knowledge while solving one problem and using the same knowledge to solve different but related problems. The homogeneous transfer learning approach has not been implemented in the game domain before, and this novel study opens a new research area for the game industry where the main challenge is predicting the significance of innovative games for entertainment and players’ engagement. Relevant not only from a player’s point of view, it is also a benchmark study for game developers who have been facing problems of “cold start” for innovative games that strengthen the game industrial economy

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 play styles/preferences, and may also facilitate the design of systems that detect 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 different 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 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.Peer reviewe