Truong-Huy D. Nguyen

Kathiravan Natarajan, Truong-Huy D. Nguyen, Mutlu Mete

Drones are conventionally controlled using joysticks, remote controllers, mobile applications, and embedded computers. A few significant issues with these approaches are that drone control is limited by the range of electromagnetic radiation and susceptible to interference noise. In this study we propose the use of hand gestures as a method to control drones. We investigate the use of computer vision methods to develop an intuitive way of agent-less communication between a drone and its operator. Computer vision-based methods rely on the ability of a drone's camera to capture surrounding images and use pattern recognition to translate images to meaningful and/or actionable information. The proposed framework involves a few key parts toward an ultimate action to be taken. They are: image segregation from the video streams of front camera, creating a robust and reliable image recognition based on segregated images, and finally conversion of classified gestures into actionable drone movement, such as takeoff, landing, hovering and so forth. A set of five gestures are studied in this work. Haar feature-based AdaBoost classifier is employed for gesture recognition. We also envisage safety of the operator and drone's action calculating the distance based on computer vision for this task. A series of experiments are conducted to measure gesture recognition accuracies considering the major scene variabilities, illumination, background, and distance. Classification accuracies show that well-lit, clear background, and within 3 ft gestures are recognized correctly over 90%. Limitations of current framework and feasible solutions for better gesture recognition are discussed, too. The software library we developed, and hand gesture data sets are open-sourced at project website.

Truong-Huy Dinh Nguyen, Magy Seif El-Nasr, Alessandro Canossa

Understanding player strategies is a key question when analyzing player behavior both for academic researchers and industry practitioners. For game designers and game user researchers, it is important to gauge the distance between intended strategies and emergent strategies; this comparison allows identification of glitches or undesirable behaviors. For academic researchers using games for serious purposes such as education, the strategies adopted by players are indicative of their cognitive progress in relation to serious goals, such as learning process. Current techniques and systems created to address these needs present a few drawbacks. Qualitative methods are difficult to scale upwards to include large number of players and are prone to subjective biases. Other approaches such as visualization and analytical tools are either designed to provide an aggregated overview of the data, losing the nuances of individual player behaviors, or, in the attempt of accounting for individual behavior, are not specifically designed to reduce the visual cognitive load. In this work, we propose a novel visualization technique that specifically addresses the tasks of comparing behavior sequences in order to capture an overview of the strategies enacted by players and at the same time examine individual player behaviors to identify differences and outliers. This approach allows users to form hypotheses about player strategies and verify them. We demonstrate the effectiveness of the technique through a case study: utilizing a prototype system to investigate data collected from a commercial educational puzzle game. While the prototype's usability can be improved, initial testing results show that core features of the system proved useful to our potential users for understanding player strategies.

Kasper Grispino, Damian Lyons, Truong-Huy Nguyen

Human-to-human communications are enriched with affects and emotions, conveyed, and perceived through both verbal and nonverbal communication. It is our thesis that drone swarms can be used to communicate information enriched with effects via nonverbal channels: guiding, generally interacting with, or warning a human audience via their pattern of motions or behavior. And furthermore that this approach has unique advantages such as flexibility and mobility over other forms of user interface. In this paper, we present a user study to understand how human participants perceived and interpreted swarm behaviors of micro-drone Crazyflie quadcopters flying three different flight formations to bridge the psychological gap between front-end technologies (drones) and the human observers' emotional perceptions. We ask the question whether a human observer would in fact consider a swarm of drones in their immediate vicinity to be nonthreatening enough to be a vehicle for communication, and whether a human would intuit some communication from the swarm behavior, despite the lack of verbal or written language. Our results show that there is statistically significant support for the thesis that a human participant is open to interpreting the motion of drones as having intent and to potentially interpret their motion as communication. This supports the potential use of drone swarms as a communication resource, emergency guidance situations, policing of public events, tour guidance, etc.

Sabbir Ahmad, Andy Bryant, Erica Kleinman et al.

Modeling players' behaviors in games has gained increased momentum in the past few years. This area of research has wide applications, including modeling learners and understanding player strategies, to mention a few. In this paper, we present a new methodology, called Interactive Behavior Analytics (IBA), comprised of two visualization systems, a labeling mechanism, and abstraction algorithms that use Dynamic Time Warping and clustering algorithms. The methodology is packaged in a seamless interface to facilitate knowledge discovery from game data. We demonstrate the use of this methodology with data from two multiplayer team-based games: BoomTown, a game developed by Gallup, and DotA 2. The results of this work show the effectiveness of this method in modeling, and developing human-interpretable models of team and individual behavior.

Erica Kleinman, Sabbir Ahmad, Zhaoqing Teng et al.

Many successful games rely heavily on data analytics to understand players and inform design. Popular methodologies focus on machine learning and statistical analysis of aggregated data. While effective in extracting information regarding player action, much of the context regarding when and how those actions occurred is lost. Qualitative methods allow researchers to examine context and derive meaningful explanations about the goals and motivations behind player behavior, but are difficult to scale. In this paper, we build on previous work by combining two existing methodologies: Interactive Behavior Analytics (IBA) and sequence analysis (SA), in order to create a novel, mixed methods, human-in-the-loop data analysis methodology that uses behavioral labels and visualizations to allow analysts to examine player behavior in a way that is context sensitive, scalable, and generalizable. We present the methodology along with a case study demonstrating how it can be used to analyze behavioral patterns of teamwork in the popular multiplayer game Defense of the Ancients 2 (DotA 2).

Zhengxing Chen, Truong-Huy D Nguyen, Yuyu Xu et al.

Multiplayer Online Battle Arena (MOBA) games have received increasing popularity recently. In a match of such games, players compete in two teams of five, each controlling an in-game avatars, known as heroes, selected from a roster of more than 100. The selection of heroes, also known as pick or draft, takes place before the match starts and alternates between the two teams until each player has selected one hero. Heroes are designed with different strengths and weaknesses to promote team cooperation in a game. Intuitively, heroes in a strong team should complement each other's strengths and suppressing those of opponents. Hero drafting is therefore a challenging problem due to the complex hero-to-hero relationships to consider. In this paper, we propose a novel hero recommendation system that suggests heroes to add to an existing team while maximizing the team's prospect for victory. To that end, we model the drafting between two teams as a combinatorial game and use Monte Carlo Tree Search (MCTS) for estimating the values of hero combinations. Our empirical evaluation shows that hero teams drafted by our recommendation algorithm have significantly higher win rate against teams constructed by other baseline and state-of-the-art strategies.

Zhengxing Chen, Chris Amato, Truong-Huy Nguyen et al.

Deck building is a crucial component in playing Collectible Card Games (CCGs). The goal of deck building is to choose a fixed-sized subset of cards from a large card pool, so that they work well together in-game against specific opponents. Existing methods either lack flexibility to adapt to different opponents or require large computational resources, still making them unsuitable for any real-time or large-scale application. We propose a new deck recommendation system, named Q-DeckRec, which learns a deck search policy during a training phase and uses it to solve deck building problem instances. Our experimental results demonstrate Q-DeckRec requires less computational resources to build winning-effective decks after a training phase compared to several baseline methods.

Truong-Huy D. Nguyen, Kasper Grispino, Damian Lyons

Drone swarms are teams of autonomous un-manned aerial vehicles that act as a collective entity. We are interested in humanizing drone swarms, equipping them with the ability to emotionally affect human users through their non-verbal motions. Inspired by recent findings in how observers are emotionally touched by watching dance moves, we investigate the questions of whether and how coordinated drone swarms' motions can achieve emotive impacts on general audience. Our preliminary study on Amazon Mechanical Turk led to a number of interesting findings, including both promising results and challenges.

Zhengxing Chen, Yuyu Xu, Truong-Huy D. Nguyen et al.

Multiplayer Online Battle Arena (MOBA) games have received increasing worldwide popularity recently. In such games, players compete in teams against each other by controlling selected game avatars, each of which is designed with different strengths and weaknesses. Intuitively, putting together game avatars that complement each other (synergy) and suppress those of opponents (opposition) would result in a stronger team. In-depth understanding of synergy and opposition relationships among game avatars benefits player in making decisions in game avatar drafting and gaining better prediction of match events. However, due to intricate design and complex interactions between game avatars, thorough understanding of their relationships is not a trivial task. In this paper, we propose a latent variable model, namely Game Avatar Embedding (GAE), to learn avatars' numerical representations which encode synergy and opposition relationships between pairs of avatars. The merits of our model are twofold: (1) the captured synergy and opposition relationships are sensible to experienced human players' perception; (2) the learned numerical representations of game avatars allow many important downstream tasks, such as similar avatar search, match outcome prediction, and avatar pick recommender. To our best knowledge, no previous model is able to simultaneously support both features. Our quantitative and qualitative evaluations on real match data from three commercial MOBA games illustrate the benefits of our model.

Truong-Huy Dinh Nguyen, Wee-Sun Lee, Tze-Yun Leong

We consider the problem of using a heuristic policy to improve the value approximation by the Upper Confidence Bound applied in Trees (UCT) algorithm in non-adversarial settings such as planning with large-state space Markov Decision Processes. Current improvements to UCT focus on either changing the action selection formula at the internal nodes or the rollout policy at the leaf nodes of the search tree. In this work, we propose to add an auxiliary arm to each of the internal nodes, and always use the heuristic policy to roll out simulations at the auxiliary arms. The method aims to get fast convergence to optimal values at states where the heuristic policy is optimal, while retaining similar approximation as the original UCT in other states. We show that bootstrapping with the proposed method in the new algorithm, UCT-Aux, performs better compared to the original UCT algorithm and its variants in two benchmark experiment settings. We also examine conditions under which UCT-Aux works well.

Truong-Huy Dinh Nguyen, David Hsu, Wee-Sun Lee et al.

We apply decision theoretic techniques to construct non-player characters that are able to assist a human player in collaborative games. The method is based on solving Markov decision processes, which can be difficult when the game state is described by many variables. To scale to more complex games, the method allows decomposition of a game task into subtasks, each of which can be modelled by a Markov decision process. Intention recognition is used to infer the subtask that the human is currently performing, allowing the helper to assist the human in performing the correct task. Experiments show that the method can be effective, giving near-human level performance in helping a human in a collaborative game.