Pong Game Research Articles

Handrithum Gaming Hub

The "Handrithum Gaming Hub" Project Is an Exciting Blend of Computer Vision and Gaming Tech. Our Main Goal Is to Make Gaming More Fun by Creating a Pong Game That You Control Using Your Hands, Thanks To OpenCV-Based Hand Tracking. Imagine playing Pong without the need for a keyboard or joystick simply move your hands in the air to control the game. Through the use of smart image processing techniques, we capture and interpret hand movements in real-time, allowing for effortless control of the Pong paddles. This seamless interaction between gestures and game commands transforms the gaming experience, making it as natural as possible. Delving into the realm of computer vision, we harness the power of OpenCV to meticulously track every movement of the player's hands. The result is a swift and fluid translation of hand actions into precise game commands, elevating the gaming experience to a new level. Players will feel a physical connection to the game as they effortlessly guide the paddles on the screen with their hands.

Lamarckian Platform: Pushing the Boundaries of Evolutionary Reinforcement Learning Toward Asynchronous Commercial Games

Despite the emerging progress of integrating evolutionary computation into reinforcement learning, the absence of a high-performance platform endowing composability and massive parallelism causes non-trivial difficulties for research and applications related to asynchronous commercial games. Here we introduce Lamarckian <xref ref-type="fn" rid="fn1" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">¹</xref> <fn id="fn1" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><label>¹</label> The code and demonstrational setup of Lamarckian are publicly available at <uri>https://github. com/lamarckian/lamarckian</uri>. </fn> – an open-source platform featuring support for evolutionary reinforcement learning scalable to distributed computing resources. To improve the training speed and data efficiency, Lamarckian adopts optimized communication methods and an asynchronous evolutionary reinforcement learning workflow. To meet the demand for an asynchronous interface by commercial games and various methods, Lamarckian tailors an asynchronous Markov Decision Process interface and designs an object-oriented software architecture with decoupled modules. In comparison with the state-of-the-art RLlib, we empirically demonstrate the unique advantages of Lamarckian on benchmark tests with up to 6000 CPU cores: i) both the sampling efficiency and training speed are doubled when running PPO on Google football game; ii) the training speed is 13 times faster when running PBT+PPO on Pong game. Moreover, we also present two use cases: i) how Lamarckian is applied to generating behavior-diverse game AI; ii) how Lamarckian is applied to game balancing tests for an asynchronous commercial game.

Design and Implementation of Multi Player Pong Game using Altera DE2 Board

Pong game is a simple but entertaining game of logic control. This research paper presents the design and implementation of an FPGA-based Pong game that runs on an Altera DE2 board using Verilog HDL. This article explains the VGA controller, object creation and animation, and text subsystem and of course how to link them all together to build a functioning circuit. There is an interesting multi-player mode and single-player mode feature in this design scheme. This game's multiplayer mode features both real-time and automatic players to create a competitive atmosphere. This design method followed less complicated, fastest processing, and utilized memory requirements and logic elements. The single-player mode uses 1.3% of total logic elements, the two-player mode uses 1.32%, and automatic player vs. real player uses 1.456% of total logic elements which is very small compared to the other gaming schemes and it reduces the processing time that is cost-effective for universal use. All the modules are designed by using Verilog HDL. The synthesis is done with the help of Altera DE2 FPGA. Functional simulation and synthesis prove that the design is universally usable and combines different modules in one module that presents sound entertainment and extends the electronics application-based work in the future.

Application of rapid invisible frequency tagging for brain computer interfaces

BackgroundBrain-computer interfaces (BCI) based on steady-state visual evoked potentials (SSVEPs/SSVEFs) are among the most commonly used BCI systems. They require participants to covertly attend to visual objects flickering at specified frequencies. The attended location is decoded online by analysing the power of neuronal responses at the flicker frequency. New methodWe implemented a novel rapid invisible frequency-tagging technique, utilizing a state-of-the-art projector with refresh rates of up to 1440 Hz. We flickered the luminance of visual objects at 56 and 60 Hz, which was invisible to participants but produced strong neuronal responses measurable with magnetoencephalography (MEG). The direction of covert attention, decoded from frequency-tagging responses, was used to control an online BCI PONG game. ResultsOur results show that seven out of eight participants were able to play the pong game controlled by the frequency-tagging signal, with average accuracies exceeding 60 %. Importantly, participants were able to modulate the power of the frequency-tagging response within a 1-second interval, while only seven occipital sensors were required to reliably decode the neuronal response. Comparison with existing methodsIn contrast to existing SSVEP-based BCI systems, rapid frequency-tagging does not produce a visible flicker. This extends the time-period participants can use it without fatigue, by avoiding distracting visual input. Furthermore, higher frequencies increase the temporal resolution of decoding, resulting in higher communication rates. ConclusionUsing rapid invisible frequency-tagging opens new avenues for fundamental research and practical applications. In combination with novel optically pumped magnetometers (OPMs), it could facilitate the development of high-speed and mobile next-generation BCI systems.

Efficient Application of Competitive Multiagent Learning by Neuroevolution

Multiagent frameworks give an ideal climate to the assessment and examination of true issues utilizing support learning calculations. Most customary ways to deal with multiagent learning are impacted by lengthy preparation periods as well as high computational intricacy. Flawless (NeuroEvolution of Augmenting Topologies) is a well-known developmental methodology used to get the best performing brain network design frequently used to handle streamlining issues in the field of man-made reasoning. This paper uses the NEAT calculation to accomplish serious multiagent learning on a changed pong game climate in a productive way. The contending specialists keep various guidelines while having comparable perception space boundaries. The proposed calculation uses this property of the climate to characterize a particular neuro transformative system that gets the ideal strategy for every one of the specialists. The ordered outcomes show that the proposed execution accomplishes ideal conduct in an exceptionally short preparation period when contrasted with existing multiagent support learning models.

Gamification and Control of Nitinol Based Ankle Rehabilitation Robot.

Conventional ankle rehabilitation exercises can be monotonous and repetitive. The use of robots and games can complement the existing practices, provide an engaging environment for the patient and alleviate the physiotherapist’s workload. This paper presents an ankle rehabilitation robot that uses two nitinol wire actuators and a Pong game to provide foot plantarflexion and dorsiflexion exercises. Nitinol is a type of smart material that has high volumetric mechanical energy density and can produce translational motion. A two-state discrete antagonistic control is proposed to manipulate the actuators. The system was tested on healthy participants and stroke patients. The results showed that the robot was safe and compliant. The robot did not forcefully plantarflex or dorsiflex the foot when the participant exerted opposing force. The actuators worked antagonistically to flex to the foot as intended, in sync with the up and down motions of the player’s bat in the game. These behaviors demonstrated the feasibility of a nitinol-based ankle rehabilitation robot and a simple and yet intuitive game in providing interactive rehabilitation exercise. The robot is expected to enhance the patient’s experience, participation and compliance to the rehabilitation routine and to quantitatively monitor the patient’s recovery progress.

An Efficient Application of Neuroevolution for Competitive Multiagent Learning

Multiagent systems provide an ideal environment for the evaluation and analysis of real-world problems using reinforcement learning algorithms. Most traditional approaches to multiagent learning are affected by long training periods as well as high computational complexity. NEAT (NeuroEvolution of Augmenting Topologies) is a popular evolutionary strategy used to obtain the best performing neural network architecture often used to tackle optimization problems in the field of artificial intelligence. This paper utilizes the NEAT algorithm to achieve competitive multiagent learning on a modified pong game environment in an efficient manner. The competing agents abide by different rules while having similar observation space parameters. The proposed algorithm utilizes this property of the environment to define a singular neuroevolutionary procedure that obtains the optimal policy for all the agents. The compiled results indicate that the proposed implementation achieves ideal behaviour in a very short training period when compared to existing multiagent reinforcement learning models.

An Augmented Reality Implementation of the Pong Game

This article presents the development of an interactive, augmented reality, and collaborative Pong game. In our approach, an overhead projector displays the game on the floor, showing the game's current state as it develops. In the meantime, a computer vision system sensing throughout an overhead depth camera detects the players and their movements. We tested our system in a roofed but wall-less environment. Our assessment of the gaming experience includes natural language processing on interviews with children and qualitative analysis of their verbal responses.

게임형식을 차용한 데이터 예술 콘텐츠 디자인에 관한 연구 - 프로토타입 웨더퐁을 중심으로

This study focuses on the design of data art content using various data generated by the interactions among nature, humans, and artifacts, which empathizes with the current eco-environmental crisis and contemplates anthropocentrism. Sympathetic to the research topic of translife(extended life), the author specified research keywords and conceptualized this research. Through this conceptualization, the author hypothesized that interactive art content borrowing the Pong game format and using various real-time data could be appropriate as a form of participant experience(PX) to represent translife, proposing a prototype entitled Weather Pong. A virtual weather environment of the content was devised by creating emotion, weather, and environment bubbles made from the weather and environment data of Auckland and Seoul, along with human emotion data about weather mined by artificial intelligence. Participants had opportunities to reflect on the existence of humans and on the meaning of our lives, affecting the life of the tree in the center of the screen by popping bubbles by controlling the paddle and the ball. As a research methodology here, the author used design thinking. Based on this method, the author designed a PX consisting of familiarization and defamiliarization and implemented a stable interactive system through a co-design strategy using audio-visual mapping. Afterwards, the author exhibited Weather Pong at five exhibitions, including Translife: The International Triennial of New Media Art, thus confirming the significance of this study through evaluations of the PX. This study did not greatly succeed in providing an intuitive PX because there were differences between participant evaluations and expert evaluations. However, it is important to note that art has been shown to be an activist instrument that criticizes our society and demands its change by providing a fresh PX through the borrowing of game formats and by providing an alternative approach to data.

Effects of visuomotor delays on the control of movement and on perceptual localization in the presence and absence of visual targets.

The sensory system constantly deals with delayed feedback. Recent studies showed that playing a virtual game of pong with delayed feedback caused hypermetric reaching movements. We investigated whether this effect is associated with a perceptual bias. In addition, we examined the importance of the target in causing hypermetric movements. In a first experiment, participants played a delayed pong game and blindly reached to presented targets. Following each reaching movement, they assessed the position of the invisible cursor. We found that participants performed hypermetric movements but reported that the invisible cursor reached the target, suggesting that they were unaware of the hypermetria and that their perception was biased toward the target rather than toward their hand position. In a second experiment, we removed the visual target, and strikingly, the hypermetria vanished. Moreover, participants reported that the invisible cursor was located with their hand. Taking these results together, we conclude that the adaptation to the visuomotor delay during the pong game selectively affected the execution of goal directed movements, resulting in hypermetria and perceptual bias when movements are directed toward visual targets but not when such targets are absent.NEW & NOTEWORTHY Recent studies showed that adaptation to visuomotor delays causes hypermetric movements in the absence of visual feedback, suggesting that visuomotor delay is represented using current state information. We report that this adaptation also affects perception. Importantly, both the motor and perceptual effects are selective to the representations that are used in the execution of goal-directed movements toward visual targets.

Behavior fusion for deep reinforcement learning

For deep reinforcement learning (DRL) system, it is difficult to design a reward function for complex tasks, so this paper proposes a framework of behavior fusion for the actor–critic architecture, which learns the policy based on an advantage function that consists of two value functions. Firstly, the proposed method decomposes a complex task into several sub-tasks, and merges the trained policies for those sub-tasks into a unified policy for the complex task, instead of designing a new reward function and training for the policy. Each sub-task is trained individually by an actor–critic algorithm using a simple reward function. These pre-trained sub-tasks are building blocks that are used to rapidly assemble a rapid prototype of a complicated task. Secondly, the proposed method integrates modules in the calculation of the policy gradient by calculating the accumulated returns to reduce variation. Thirdly, two alternative methods to acquire integrated returns for the complicated task are also proposed. The Atari 2600 pong game and a wafer probe task are used to validate the performance of the proposed methods by comparison with the method using a gate network.

IMOVE: A Motion Tracking and Projection Framework for Social Interaction Applications

In public places such as malls, train stations, and airports, there is a constant flow of people either waiting or commuting. Even though people at these locations are surrounded by many other individuals, mostly there is little social interaction, which generally creates a gloomy atmosphere. Any applications promoting social interactions are a welcome addition. We present IMOVE, an interactive framework aimed at facilitating the development of such applications. It offers a combination of motion tracking and projection methods which makes it easier to create interactive experiences and games, tailored to motivate people to move around, explore, and, most importantly, interact with each other in a fun way. People moving around trigger events and effects, interacting with the applications using their body movements or even collaboratively working towards an outcome. IMOVE was validated by means of a variety of applications in a real scenario, the entrance hall of a busy public building: the classic Pong game, a collaborative and accessible casual game (Save the Turtles!), and a procedural visual art generator based on game mechanics (Light Trails). All applications have been successfully running for the past year. The IMOVE framework is freely available online and it has been shown to be particularly suited and accessible to novice game and interactive application developers for large public spaces.

Python animation for a pong game

This abstract describes an assignment used by the authors in a CS1/CS2 course. The assignment requires the students to develop a simple pong game as shown in the following diagram.

State-Based Delay Representation and Its Transfer from a Game of Pong to Reaching and Tracking

To accurately estimate the state of the body, the nervous system needs to account for delays between signals from different sensory modalities. To investigate how such delays may be represented in the sensorimotor system, we asked human participants to play a virtual pong game in which the movement of the virtual paddle was delayed with respect to their hand movement. We tested the representation of this new mapping between the hand and the delayed paddle by examining transfer of adaptation to blind reaching and blind tracking tasks. These blind tasks enabled to capture the representation in feedforward mechanisms of movement control. A Time Representation of the delay is an estimation of the actual time lag between hand and paddle movements. A State Representation is a representation of delay using current state variables: the distance between the paddle and the ball originating from the delay may be considered as a spatial shift; the low sensitivity in the response of the paddle may be interpreted as a minifying gain; and the lag may be attributed to a mechanical resistance that influences paddle’s movement. We found that the effects of prolonged exposure to the delayed feedback transferred to blind reaching and tracking tasks and caused participants to exhibit hypermetric movements. These results, together with simulations of our representation models, suggest that delay is not represented based on time, but rather as a spatial gain change in visuomotor mapping.

The Development of Holography-based Ping Pong Game System using Hand Tracking Interaction

The Development of Holography-based Ping Pong Game System using Hand Tracking Interaction

Developing interactive and simple electromyogram PONG game for foot dorsiflexion and plantarflexion rehabilitation exercise.

Gaming systems have been proven to be able to improve physical and cognitive skills. Several studies have integrated games to be part of rehabilitation program to improve functional ability of human limbs in performing activities of daily living. This paper presents the development of an interactive electromyogram (EMG) based PONG game for foot dorsifexion and plantarflexion exercise. PONG game is selected because it is a classical game that can be easily comprehended and can be played by young and old people. Two surface EMG sensors were used in this work to acquire muscle activity of tibialis anterior muscle and gastrocnemius muscle. An experimental study was carried out to examine the viability of this sensor as input to play the game. The results were satisfactory. This study further strengthens the viability of using muscle activity as an input for gaming system and as a mean to monitor patient rehabilitation progress.

Are you a human or a humanoid: Predictive user modelling through behavioural analysis of online gameplay data

Intelligent agents are widely used in robotics, gaming and simulation. A key issue is modelling human behaviours so that intelligent agents can use a human’s behavioural model to imitate them and predict their next moves. In this article, we use Internet-based multiplayer online gaming (MOG) as a case study to present our approach to predictive user modelling through behavioural analysis of online gameplay data. As latency is an inherited bottleneck of the Internet and is likely to remain so into a foreseeable future, a lot of efforts have been made to address the resulting issues. Most of the existing latency handling techniques are based on the assumption that latency is within an acceptable threshold so that they can alleviate or even completely hide its negative impact on players’ quality of experience (QoE) that directly determines consumers’ satisfaction of the provided MOG services. While this assumption is mostly valid, it is worth noting that a player’s Internet connection latency always fluctuates (known as jitter), possibly to the extent of exceeding a MOG’s designated threshold in which case none of the techniques can handle properly but disconnecting the player from the gameplay session. Forcing a player to quit prematurely simply due to a spike of unusual high latency has a significant negative impact both on the gameplay’s fairness and on the player’s QoE. To improve customer satisfaction of a MOG service, we propose a more tolerant approach by temporarily substituting a player with a humanoid bot in the event of latency hikes so that the player always remains in the gameplay session. The challenge in this approach is to create a personalised humanoid bot that can imitate the playing pattern of the individual human player being substituted. Our solution is to first extract key variables that have impact on the human player’s decision-makings through behavioural analysis of the player’s historical gameplay data, then model the relationships among these variables, and finally creates the player’s humanoid bot with the model. In this paper, we use a multiplayer online pong game as a case study to explain behavioural variables, modelling techniques, processes, outcomes, and performance studies.

Target size matters: target errors contribute to the generalization of implicit visuomotor learning

The process of sensorimotor adaptation is considered to be driven by errors. While sensory prediction errors, defined as the difference between the planned and the actual movement of the cursor, drive implicit learning processes, target errors (e.g., the distance of the cursor from the target) are thought to drive explicit learning mechanisms. This distinction was mainly studied in the context of arm reaching tasks where the position and the size of the target were constant. We hypothesize that in a dynamic reaching environment, where subjects have to hit moving targets and the targets' dynamic characteristics affect task success, implicit processes will benefit from target errors as well. We examine the effect of target errors on learning of an unnoticed perturbation during unconstrained reaching movements. Subjects played a Pong game, in which they had to hit a moving ball by moving a paddle controlled by their hand. During the game, the movement of the paddle was gradually rotated with respect to the hand, reaching a final rotation of 25°. Subjects were assigned to one of two groups: The high-target error group played the Pong with a small ball, and the low-target error group played with a big ball. Before and after the Pong game, subjects performed open-loop reaching movements toward static targets with no visual feedback. While both groups adapted to the rotation, the postrotation reaching movements were directionally biased only in the small-ball group. This result provides evidence that implicit adaptation is sensitive to target errors.

Bacterial transfer to beverages during drinking games: 'beer pong'

The study’s first phase identified bacteria recovered from ping pong balls used in games played by the general public. Ping pong balls were collected from uncontrolled environments and analysed according to playing location. Results showed that the highest levels of contamination were observed in games played outside, with a mean of 5.3 log CFU/ball, while the lowest levels were observed for games played over carpeted surfaces, with a mean of 2.8 log CFU/ball. In terms of identification, four distinct bacterial types were selected for presumptive identification based on their appearance on agar plates. The second part of the study examined bacterial transfer from inoculated ping pong balls to beer. Using a non-pathogenic E. coli strain as the inoculum, the mean level of contamination was 4.9 log CFU/ball. Overall, the results for both parts of this study demonstrate that the game of beer pong is a source of bacterial transfer.

Air

Air Brian Phillip Whalen (bio) I’d lost the electric inflater, so I sat opposite my father on the hard-wood floor while we took turns blowing air into the rubber nipple. The more the mattress amplified, the faster air escaped. To offset the pressure lost, we inhaled deeper and blew harder and before we knew it we were feeling pretty high. “In Nam,” my father said, hugging the mattress with his tree-trunk arms, “They dropped me from twelve-thousand feet with nothing but a bed sheet.” He huffed and puffed and slid the mattress to me. I said, “I hope they let you keep the sheet.” “Get a job,” he said, “and maybe you could buy a spare set for when your old man visits.” I tried not to think about my father’s slobber while I blew into the nipple. “Hey,” my father asked, “Are you feeling light-headed?” “Do I look old enough to be wearing shorts designed by Kareem Abdul-Jabbar?” The mattress was a game of pong between us, a rubber lung receiving and releasing air with every pass. “True style never fades,” my father answered, adjusting his elastic waist and cotton headband. “Only sanity,” I said. My father blew and handed me the mattress, wet with arm-sweat. It was 110 degrees outside. “When I caught touchdowns back in college,” he said, “I did back flips in the end zone.” I bit the nipple with my teeth but air slipped out. I was in fact a little dizzy. I couldn’t catch my breath. “No, Dad, that was Uncle Skip.” And it was Skip, my father’s older brother, who went to Vietnam. My father was, it’s true, in the airborne division, but he jumped from helicopters, not planes, and never from more than a few hundred feet. He was too young for the draft, and the war was over by the time he’d joined the infantry. But Uncle Skip enlisted in his junior year, and no amount of back flips could land him back in Jersey. He died over enemy lines. “I’m high as a kite!” my father cried. His laughter came in bursts, his tired lungs intaking air before he had a chance to blow it out again. Crying, I let go of the mattress and it flew like a balloon around the room, and we waved our arms as if we could touch it. [End Page 12] Brian Phillip Whalen Brian Phillip Whalen is a doctoral candidate in Creative Writing at the University at Albany, NY. He received his MFA in Creative Writing from Iowa State University. His work most recently appears or is forthcoming in Blueline, The Chattahoochee Review, Chautauqua, Cutbank, the Newer York, PANK, and Rhino. Copyright © 2014 University of Wisconsin Board of Regents