Personalized Game Content Generation and Recommendation for Gamified Systems

Gamification, that is, the usage of game content in non-game contexts, has been successfully employed in several application domains to foster engagement, as well as to influence the behavior of end users. Although gamification is often effective in inducing behavioral changes in citizens, the difficulty in retaining players and sustaining the acquired behavior over time, shows some limitations of this technology. That is especially unfortunate, because changing players’ demeanor (which have been shaped for a long time), cannot be immediately internalized; rather, the gamification incentive must be reinforced to lead to stabilization. This issue could be sourced from utilizing static game content and a one-size-fits-all strategy in generating the content during the game. This reveals the need for dynamic personalization over the course of the game. Our research hypothesis is that we can overcome these limitations with Procedural Content Generation (PCG) of playable units that appeal to each individual player and make her user experience more varied and compelling. In this thesis, we propose a deep, large and long solution, deployed in two main phases of Design and Integration to tackle these limitations. To support the former phase, we present a “PCG and Recommender system” to automate the generation and recommendation of playable units, named “Challenges”, which are Personalized and Contextualized on the basis of players’ preferences, skills, etc., and the game ulterior objectives. To this end, we develop a multi-layered framework to generate the personalized game content to be assigned and recommended to the players involved in the gamified system. To support the latter phase, we integrate two modules into the system including Machine Learning (ML) and Player Modeling, in order to optimize the challenge selection process and learning players’ behavior to further improve the personalization, by deriving the style of the player, respectively. We have carried out the implementation and evaluation of the proposed framework and its integration in two different contexts. First, we assess our Automatic Procedural Content Generation and Recommendation (APCGR) system within a large-scale and long-running open field experiment promoting sustainable urban mobility that lasted twelve weeks and involved more than 400 active players. Then, we implement the “Player Modeling” module (in the integration phase) in an educational interactive game domain to assess the performance of the proposed play style extraction approach. The contributions of this dissertation are a first step toward the application of machine learning in automating the procedural content generation and recommendation in gamification systems

Generating Personalized Challenges to Enhance the Persuasive Power of Gamification

While gamification is often effective in incentivizing behavioral changes, well-known limitations concern retaining the interest of players over the long term, and sustaining the new behaviors promoted through the game. To make the gamification user experience more varied and compelling, we propose an approach based on the Procedural Content Generation of personalized and contextualized playable units that appeal individually to each player. We have implemented this approach as a system that generates and recommends personalized challenges, based on the player’s preference, history, game state and performance, and we have evaluated it using a smart urban mobility game that proposed weekly challenges to hundreds of citizens/players

Personalized Game Content Generation and Recommendation for Gamified Systems

Gamification, that is, the usage of game content in non-game contexts, has been successfully employed in several application domains to foster engagement, as well as to influence the behavior of end users. Although gamification is often effective in inducing behavioral changes in citizens, the difficulty in retaining players and sustaining the acquired behavior over time, shows some limitations of this technology. That is especially unfortunate, because changing players’ demeanor (which have been shaped for a long time), cannot be immediately internalized; rather, the gamification incentive must be reinforced to lead to stabilization. This issue could be sourced from utilizing static game content and a one-size-fits-all strategy in generating the content during the game. This reveals the need for dynamic personalization over the course of the game. Our research hypothesis is that we can overcome these limitations with Procedural Content Generation (PCG) of playable units that appeal to each individual player and make her user experience more varied and compelling. In this thesis, we propose a deep, large and long solution, deployed in two main phases of Design and Integration to tackle these limitations. To support the former phase, we present a “PCG and Recommender system” to automate the generation and recommendation of playable units, named “Challenges”, which are Personalized and Contextualized on the basis of players’ preferences, skills, etc., and the game ulterior objectives. To this end, we develop a multi-layered framework to generate the personalized game content to be assigned and recommended to the players involved in the gamified system. To support the latter phase, we integrate two modules into the system including Machine Learning (ML) and Player Modeling, in order to optimize the challenge selection process and learning players’ behavior to further improve the personalization, by deriving the style of the player, respectively. We have carried out the implementation and evaluation of the proposed framework and its integration in two different contexts. First, we assess our Automatic Procedural Content Generation and Recommendation (APCGR) system within a large-scale and long-running open field experiment promoting sustainable urban mobility that lasted twelve weeks and involved more than 400 active players. Then, we implement the “Player Modeling” module (in the integration phase) in an educational interactive game domain to assess the performance of the proposed play style extraction approach. The contributions of this dissertation are a first step toward the application of machine learning in automating the procedural content generation and recommendation in gamification systems

Machine Learning for Personalized Challenges in a Gamified Sustainable Mobility Scenario

Gamification has been successfully applied in several application domains to persuade people to reach to a certain goal. However, such persuasive technologies have concerns to keep players engaged and to sustain newly acquired behaviors for a long term. One option to tackle this issue is personalizing the gamified experience for each individual player. To this end, we developed a framework that can personalize the game content by proposing to players challenges that are tailored to their profiles and game histories. In this paper we present a Machine Learning module that optimizes the challenge selection process and the evaluation results within an on-the-field game promoting sustainable mobility habits

Hand drawn optical circuitrRecognition

Electrical diagram is foundation of studies in electrical science. A circuit diagram convey many information about the system. Behind any device there are plenty of electrical ingredients which perform their specific tasks, today all the electrical software tools failed to effectively convert the information automatically from a circuit image diagram to digital form. Hence electrical engineers should manually enter all information into computers, and this process takes time and bring errors with high probability. Moreover, when the diagram is hand drawn, the problem is more complicated for any electrical analysis. Thus, in this paper we propose a new method using Artificial Neural Network (ANN) to make a machine that can directly read the electrical symbols from a hand drawn circuit image. The recognition process involves two steps: first step is feature extraction using shape based features, and the second one is a classification procedure using ANN through a back propagation algorithm. The ANN was trained and tested with different hand drawn electrical images. The results show that our proposal is viable and brings good performances

Hand drawn optical circuitrRecognition

Electrical diagram is foundation of studies in electrical science. A circuit diagram convey many information about the system. Behind any device there are plenty of electrical ingredients which perform their specific tasks, today all the electrical software tools failed to effectively convert the information automatically from a circuit image diagram to digital form. Hence electrical engineers should manually enter all information into computers, and this process takes time and bring errors with high probability. Moreover, when the diagram is hand drawn, the problem is more complicated for any electrical analysis. Thus, in this paper we propose a new method using Artificial Neural Network (ANN) to make a machine that can directly read the electrical symbols from a hand drawn circuit image. The recognition process involves two steps: first step is feature extraction using shape based features, and the second one is a classification procedure using ANN through a back propagation algorithm. The ANN was trained and tested with different hand drawn electrical images. The results show that our proposal is viable and brings good performances

A Snapshot-Stacked Ensemble and Optimization Approach for Vehicle Breakdown Prediction

Predicting breakdowns is becoming one of the main goals for vehicle manufacturers so as to better allocate resources, and to reduce costs and safety issues. At the core of the utilization of vehicle sensors is the fact that early detection of anomalies facilitates the prediction of potential breakdown issues, which, if otherwise undetected, could lead to breakdowns and warranty claims. However, the making of such predictions is too complex a challenge to solve using simple predictive models. The strength of heuristic optimization techniques in solving np-hard problems, and the recent success of ensemble approaches to various modeling problems, motivated us to investigate a hybrid optimization- and ensemble-based approach to tackle the complex task. In this study, we propose a snapshot-stacked ensemble deep neural network (SSED) approach to predict vehicle claims (in this study, we refer to a claim as being a breakdown or a fault) by considering vehicle operational life records. The approach includes three main modules: Data pre-processing, Dimensionality Reduction, and Ensemble Learning. The first module is developed to run a set of practices to integrate various sources of data, extract hidden information and segment the data into different time windows. In the second module, the most informative measurements to represent vehicle usage are selected through an adapted heuristic optimization approach. Finally, in the last module, the ensemble machine learning approach utilizes the selected measurements to map the vehicle usage to the breakdowns for the prediction. The proposed approach integrates, and uses, the following two sources of data, collected from thousands of heavy-duty trucks: Logged Vehicle Data (LVD) and Warranty Claim Data (WCD). The experimental results confirm the proposed system’s effectiveness in predicting vehicle breakdowns. By adapting the optimization and snapshot-stacked ensemble deep networks, we demonstrate how sensor data, in the form of vehicle usage history, contributes to claim predictions. The experimental evaluation of the system on other application domains also indicated the generality of the proposed approach

Predicting Vehicle Behavior Using Multi-task Ensemble Learning

Vehicle utilization analysis is an essential tool for manufacturers to understand customer needs, improve equipment uptime, and to collect information for future vehicle and service development. Typically today, this behavioral modeling is done on high-resolution time-resolved data with features such as GPS position and fuel consumption. However, high-resolution data is costly to transfer and sensitive from a privacy perspective. Therefore, such data is typically only collected when the customer pays for extra services relying on that data. This motivated us to develop a multi-task ensemble approach to transfer knowledge from the high-resolution data and enable vehicle behavior prediction from low-resolution but high dimensional data that is aggregated over time in the vehicles. This study proposes a multi-task snapshot-stacked ensemble (MTSSE) deep neural network for vehicle behavior prediction by considering vehicles’ low-resolution operational life records. The multi-task ensemble approach utilizes the measurements to map the low-frequency vehicle usage to the vehicle behaviors defined from the high-resolution time-resolved data. Two data sources are integrated and used: high-resolution data called Dynafleet, and low-resolution so-called Logged Vehicle Data (LVD). The experimental results demonstrate the proposed approach’s effectiveness in predicting the vehicle behavior from low frequency data. With the suggested multi-task snapshot-stacked ensemble deep network, it is shown how low-resolution sensor data can highly contribute to predicting multiple vehicle behaviors simultaneously while using only one single training process