Serious game self-regulation using human-like agents to visualize students engagement base on crowd

Nowadays, the emergence of artificial intelligent (AI) technology for games has been advancely developed. A serious game is a technology employing AI to create a virtual environment in a serious gamification strategy. This research describes AI based virtual classrooms to adopt proper strategies and focusing on maintaining and increasing student engagement by encouraging self-regulation behavior at the learning process. The self-regulation behavior describes student's ability to direct their own learning to achieve learning targets on a path full of obstacles. By employing a human-like agent to visualize student engagement, this visualization aims to provide human-like experiences for users to comprehend student behavior. A reciprocal velocity obstacles (RVO)-based crowd behavior is employed to visualize student engagement. RVO is an autonomous navigation approach for directing the achievement of agents target. The human-like agents behave in various ways to reach the goal points depending on the performances and the obstacles before them. We employ our method in an investigation of students' learning activities in a pedagogically-centered learning environment at Universitas Islam Negeri (UIN) Walisongo, Semarang, Indonesia. The results demonstrate the best scenario changes along with the performances and obstacles faced to reach the goal points as well as the learning target

Modelling vehicle-pedestrian interactions at unsignalised locations employing game-theoretic models

There are some aspects of driver-pedestrian interactions at unsignalised locations that remain poorly understood. Understanding these aspects is vital for promoting road traffic safety in general which involves the interaction of human road users. Recent developments in vehicle automation have called for investigating human-robot interactions before the deployment of highly automated vehicles (HAVs) on roads so that they can communicate effectively with pedestrians making them trustworthy and reliable road users. To understand such interactions, one can simulate interactive scenarios studying various factors affecting road user decision-making processes through lab and naturalistic studies. To quantify such scenarios, mathematical models of human behaviour can be useful. One of these mathematical models that is capable of capturing interactions is game theory (GT). GT can provide valuable insights and strategies to help resolve road user interactions by analysing the behaviour of different participants in traffic situations and suggesting optimal decisions for each party. Thus, the current doctoral thesis aimed to investigate vehicle-pedestrian interactions at unsignalised crossings using GT models, applied to both lab-based and naturalistic data. One of the main aims of the current thesis was to understand how two or more human road users can communicate in a safe and controlled manner demonstrating behaviours of a game-theoretic nature. Thus, an experimental paradigm was created in the form of a distributed simulator study (DSS), by connecting a motion-based driving simulator to a CAVE-based pedestrian simulator to achieve this goal. It was found that the DSS could generate scenarios where participants interact actively showing similar communication patterns to those observed in real traffic. Another prominent finding was the stronger role of vehicle kinematics than personality traits for determining interaction outcomes at unmarked crossings, i.e. whether the pedestrian or driver passed first. To quantify the observations made from the DSS, five computational models namely four GT and one logit model were developed, tested and compared using this dataset. The GT models were obtained from both conventional and behavioural GT literature (CGT and BGT, respectively). This was done to bridge a gap in the previous research, specifically the lack of a comparison between these two modelling approaches in the context of vehicle-pedestrian interactions. Overall, the findings showed that: 1) DSS is a reliable source for the testing and development of GT models; 2) there is a high behaviour variability among road users highlighting the value of studying individualised data in such studies; 3) the BGT models showed promising results in predicting interaction outcomes and simulating the whole interaction process, when compared to the conventional models. These findings suggest that future studies should proceed to adopt, test, and develop BGT approaches for future HAV-human road user interaction studies. To validate the findings of the first two studies, a naturalistic study was conducted in the city of Leeds using state-of-the-art sensors. The sensors gathered road user data including their trajectory and speed over time. The findings from observations revealed similar communication patterns between drivers and pedestrians as in the DSS, suggesting a high degree of relative validity of the experimental paradigm. The results for the computational models were similar but the differences among the models were less noticeable compared to when the models tested against the controlled dataset. Overall, this thesis illustrates that the experimental paradigm and BGT models developed as part of the PhD programme have potential applications for HAV decision-making and motion planning algorithms, as well as traffic safety in general

Inverse Dynamic Game Methods for Identification of Cooperative System Behavior

Die dynamische Spieltheorie hat sich als ein effektiver Ansatz zur Modellierung und Analyse der Interaktion zwischen mehreren Akteuren oder Spielern in dynamischen Prozessen erwiesen. Um diese Theorie in realen Anwendungen umzusetzen, ist jedoch die Möglichkeit einer schnellen Identifikation der Ziele jedes Spielers entscheidend. Dieses Identifikationsproblem wird als inverses dynamisches Spiel bezeichnet. Hierfür präsentiert diese Dissertation Lösungen, die auf Beobachtungen der Spieleraktionen und der resultierenden Zustandstrajektorie basieren, welche die Entwicklung des Spiels über die Zeit beschreibt. Es werden zwei Arten von Methoden zur Lösung von inversen dynamischen Spielen entwickelt. Die erste besteht in der Anwendung von regelungstechnischen Methoden. Für die weitverbreitete Klasse der linear-quadratischen dynamischen Spiele werden zusätzlich explizite Mengen formuliert, die alle möglichen Lösungen des inversen Problems beschreiben. Der zweiten Methode liegen Verfahren des Inverse Reinforcement Learnings aus der Informatik zugrunde. Für beide Arten von Methoden werden mathematische Bedingungen formuliert, unter denen eine erfolgreiche Schätzung der Ziele aller Spieler garantiert ist. Ein simulativer Vergleich mit einem Verfahren aus dem Stand der Technik zeigt die höhere Effizienz der vorgestellten neuen Ansätze. Darüber hinaus werden die Methoden für die Identifikation von kooperativem menschlichen Verhalten in einem Lenkmanöver angewendet. Die entwickelten Ansätze für inverse dynamische Spiele ermöglichen die effiziente Identifikation von Spielerzielen und können in zahlreichen Anwendungsfeldern wie beispielsweise der Mensch-Maschine-Interaktion und der Verhaltensbeschreibung biologischer Systeme eingesetzt werden

Human-Robot Collaborations in Industrial Automation

Technology is changing the manufacturing world. For example, sensors are being used to track inventories from the manufacturing floor up to a retail shelf or a customer’s door. These types of interconnected systems have been called the fourth industrial revolution, also known as Industry 4.0, and are projected to lower manufacturing costs. As industry moves toward these integrated technologies and lower costs, engineers will need to connect these systems via the Internet of Things (IoT). These engineers will also need to design how these connected systems interact with humans. The focus of this Special Issue is the smart sensors used in these human–robot collaborations