Evolutionary Game Theory (EGT) and Artificial Intelligence (AI) are two fields that, at first glance, might seem distinct, but they have notable connections and intersections. The former focuses on the evolution of behaviors (or strategies) in a population, where individuals interact with others and update their strategies based on imitation (or social learning). The more successful a strategy is, the more prevalent it becomes over time. The latter, meanwhile, is centered on machine learning algorithms and (deep) neural networks. It is often from a single-agent perspective but increasingly involves multi-agent environments, in which intelligent agents adjust their strategies based on feedback and experience, somewhat akin to the evolutionary process yet distinct in their self-learning capacities. In light of the key components necessary to address real-world problems, including (i) learning and adaptation, (ii) cooperation and competition, (iii) robustness and stability, and altogether (iv) population dynamics of individual agents whose strategies evolve, the cross-fertilization of ideas between both fields will contribute to the advancement of mathematics of multi-agent learning systems, in particular, to the nascent domain of ``collective cooperative intelligence'' bridging evolutionary dynamics and multi-agent reinforcement learning.
In past years, we have been dedicated to automating user acceptance testing (UAT) process of WeChat Pay, one of the most influential mobile payment applications in China. A system titled XUAT has been developed for this purpose. However, there is still a human-labor-intensive stage, i.e, test scripts generation, in the current system. Therefore, in this paper, we concentrate on methods of boosting the automation level of the current system, particularly the stage of test scripts generation. With recent notable successes, large language models (LLMs) demonstrate significant potential in attaining human-like intelligence and there has been a growing research area that employs LLMs as autonomous agents to obtain human-like decision-making capabilities. Inspired by these works, we propose an LLM-powered multi-agent collaborative system, named XUAT-Copilot, for automated UAT. The proposed system mainly consists of three LLM-based agents responsible for action planning, state checking and parameter selecting, respectively, and two additional modules for state sensing and case rewriting. The agents interact with testing device, make human-like decision and generate action command in a collaborative way. The proposed multi-agent system achieves a close effectiveness to human testers in our experimental studies and gains a significant improvement of Pass@1 accuracy compared with single-agent architecture. More importantly, the proposed system has launched in the formal testing environment of WeChat Pay mobile app, which saves a considerable amount of manpower in the daily development work.
The compact muon solenoid (CMS) experiment is a general-purpose detector for high-energy collision at the large hadron collider (LHC) at CERN. It employs an online data quality monitoring (DQM) system to promptly spot and diagnose particle data acquisition problems to avoid data quality loss. In this study, we present semi-supervised spatio-temporal anomaly detection (AD) monitoring for the physics particle reading channels of the hadronic calorimeter (HCAL) of the CMS using three-dimensional digi-occupancy map data of the DQM. We propose the GraphSTAD system, which employs convolutional and graph neural networks to learn local spatial characteristics induced by particles traversing the detector, and global behavior owing to shared backend circuit connections and housing boxes of the channels, respectively. Recurrent neural networks capture the temporal evolution of the extracted spatial features. We have validated the accuracy of the proposed AD system in capturing diverse channel fault types using the LHC Run-2 collision data sets. The GraphSTAD system has achieved production-level accuracy and is being integrated into the CMS core production system--for real-time monitoring of the HCAL. We have also provided a quantitative performance comparison with alternative benchmark models to demonstrate the promising leverage of the presented system.
Language models have been foundations in various scenarios of NLP applications, but it has not been well applied in language variety studies, even for the most popular language like English. This paper represents one of the few initial efforts to utilize the NLP technology in the paradigm of World Englishes, specifically in creating a multi-variety corpus for studying Asian Englishes. We present an overview of the CCAE -- Corpus of Chinese-based Asian English, a suite of corpora comprising six Chinese-based Asian English varieties. It is based on 340 million tokens in 448 thousand web documents from six regions. The ontology of data would make the corpus a helpful resource with enormous research potential for Asian Englishes (especially for Chinese Englishes for which there has not been a publicly accessible corpus yet so far) and an ideal source for variety-specific language modeling and downstream tasks, thus setting the stage for NLP-based World Englishes studies. And preliminary experiments on this corpus reveal the practical value of CCAE. Finally, we make CCAE available at \href{https://huggingface.co/datasets/CCAE/CCAE-Corpus}{this https URL}.
Despite the remarkable advances in image matching and pose estimation, image-based localization of a camera in a temporally-varying outdoor environment is still a challenging problem due to huge appearance disparity between query and reference images caused by illumination, seasonal and structural changes. In this work, we propose to leverage additional sensors on a mobile phone, mainly GPS, compass, and gravity sensor, to solve this challenging problem. We show that these mobile sensors provide decent initial poses and effective constraints to reduce the searching space in image matching and final pose estimation. With the initial pose, we are also able to devise a direct 2D-3D matching network to efficiently establish 2D-3D correspondences instead of tedious 2D-2D matching in existing systems. As no public dataset exists for the studied problem, we collect a new dataset that provides a variety of mobile sensor data and significant scene appearance variations, and develop a system to acquire ground-truth poses for query images. We benchmark our method as well as several state-of-the-art baselines and demonstrate the effectiveness of the proposed approach. The code and dataset will be released publicly.
Underwater manipulation with free-floating autonomous underwater vehicles (AUVs) is an under-explored research area that this paper addresses. The open-source mechanical, electrical, and software designs of an AUV and continuum manipulator system are provided as a platform for performing this research. The underwater robot system has high degrees of freedom including the vehicle body motion and the manipulator joints. Therefore, when performing a manipulation task, the robot has many different potential trajectories which satisfy the task constraints, and this kinematic redundancy needs to be resolved. This paper provides a method for solving the redundancy problem. The relevant kinematic models are derived in order to build an algorithm to calculate desired joint velocities in real time. Different methods to optimize the algorithm for specific tasks are proposed, including a basic weighting method and a gradient projection method to optimize a user-defined objective function. Both simulation and experimental results are analyzed to assess the performance of this algorithm.
In recent years, there is an increasing interest in high school robotics extracurriculars such as robotics clubs and robotics competitions. The growing demand is a result of more ubiquitous open-source software and affordable off-the-shelf hardware kits, which significantly help lower the barrier for entry-level robotics hobbyists. In this project, we present an open-source, low-cost, and lightweight robotic manipulator designed and developed by a high school researcher under the guidance of a university faculty and a Ph.D. student. We believe the presented project is suitable for high school robotics research and educational activities. Our open-source package consists of mechanical design models, mechatronics specifications, and software program source codes. The mechanical design models include CAD (Computer Aided Design) files that are ready for prototyping (3D printing technology) and serve as an assembly guide accommodated with a complete bill of materials. Electrical wiring diagrams and low-level controllers are documented in detail as part of the open-source software package. The educational objective of this project is to enable high school student teams to replicate and build a robotic manipulator. The engineering experience that high school students acquire in the proposed project is full-stack, including mechanical design, mechatronics, and programming. The project significantly enriches their hands-on engineering experience in a project-based environment. Throughout this project, we discovered that the high school researcher was able to apply multidisciplinary knowledge from K-12 STEM courses to build the robotic manipulator. The researcher was able to go through a system engineering design and development process and obtain skills to use professional engineering tools including SolidWorks and Arduino microcontrollers.
Most aerial manipulators use serial rigid-link designs, which results in large forces when initiating contacts during manipulation and could cause flight stability difficulty. This limitation could potentially be improved by the compliance of continuum manipulators. To achieve this goal, we present the novel design of a compact, lightweight, and modular cable-driven continuum manipulator for aerial drones. We then derive a complete modeling framework for its kinematics, statics, and stiffness (compliance). The modeling framework can guide the control and design problems to integrate the manipulator to aerial drones. In addition, thanks to the derived stiffness (compliance) matrix, and using a low-cost IMU sensor to capture deformation angles, we present a simple method to estimate manipulation force at the tip of the manipulator. We report preliminary experimental validations of the hardware prototype, providing insights on its manipulation feasibility. We also report preliminary results of the IMU-based force estimation method.
The equilibrium shape of a continuum robot is resulted from both its internal actuation and the external physical interaction with a surrounding environment. A fast and accurate shape estimation method (i) can be used as a feedback to compensate for more accurate motion; and (ii) can reveal rich information about physical interactions (e.g. instrument-anatomy contacts / forces during a surgery). From a prior work that demonstrated an offline calibration of continuum robots, we adopt its shape modal representation and error propagation models that include identification Jacobians. In this work, we present an iterative observer approach to enable online shape estimation. We develop a dual Extended Kalman Filter (EKF) to estimate both the robot state and the shape modal parameters. The dual EKF provides robust estimation on (i) the configuration space variables that are controllable and driven by internal actuation; and (ii) the modal coefficients representing homotopies of shape families that are governed by the physical interactions with the environment. We report results from simulation studies in this work, and plan to investigate methods in the future to use the proposed approach for predicting physical interactions.
Most aerial manipulators use serial rigid-link designs, which results in large forces when initiating contacts during manipulation and could cause flight stability difficulty. This limitation could potentially be improved by the compliance of continuum manipulators. To achieve this goal, we present the novel design of a compact, lightweight, and modular cable-driven continuum manipulator for aerial drones. We then derive a complete modeling framework for its kinematics, statics, and stiffness (compliance). The framework is essential for integrating the manipulator to aerial drones. Finally, we report preliminary experimental validations of the hardware prototype, providing insights on its manipulation feasibility. Future work includes the integration and test of the proposed continuum manipulator with aerial drones.