This paper presents a novel approach to graph neural network (GNN) learning, leveraging an adaptive graph neural network (AGNN) to dynamically learn the graph topology and subsequently utilize this topology for data modeling. Traditional GNNs typically rely on static graph structures, limiting their ability to effectively capture complex relationships within the data. This work introduces an AGNN that adapts its network architecture and weights to the specific graph structure, enhancing the model's capacity to represent and extract meaningful information. We demonstrate the effectiveness of this method through several experiments, showcasing improved data modeling and analysis efficiency compared to existing approaches.
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
Graph Neural Networks (GNNs) have achieved remarkable success in various domains, including social network analysis, recommendation systems, and knowledge graph reasoning. However, a significant limitation of traditional GNNs is their inability to effectively handle evolving graph structures and temporal dependencies. This paper proposes a novel Temporal Graph Neural Network (TGNN) architecture, incorporating a Contextual Drift Model, designed to address this challenge. The TGNN learns and predicts the rate of change in node and edge attributes over time, explicitly modeling the concept of "contextual drift." This information is then integrated into the GNN's message-passing process, allowing the model to adapt dynamically to changing graph dynamics. The core mechanism utilizes a recurrent neural network (RNN) layer to capture the temporal evolution of attributes. The resulting TGNN demonstrates improved performance in scenarios where graph structures and node/edge attributes change over time, offering a more robust and adaptable solution compared to static GNNs. The primary contribution lies in the integration of temporal dynamics and contextual information within a graph neural network framework, paving the way for more sophisticated and reliable graph representations in dynamic environments.
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
This paper introduces Geometric Topology Optimization Algorithms (GTOA) – a novel approach that integrates deep learning with topology optimization to accelerate complex structural simulations and design. We propose a novel "拓扑感知" algorithm utilizing Generative Adversarial Networks (GANs) to learn the geometric structure of target shapes and combine it with reinforcement learning to automatically generate optimization paths. The core mechanism lies in automating the exploration process, overcoming the limitations of traditional methods. The paper details the algorithm's architecture, training process, and validation results, demonstrating its potential for efficient and intelligent geometric structure design. The new algorithm promises to significantly reduce design time and improve the accuracy of optimized structures.
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
This paper presents a novel approach to robot motion planning based on adaptive learning. Traditional robot motion planning methods often struggle to cope with dynamic and uncertain environments. This research leverages reinforcement learning (RL) to develop a system capable of autonomously learning optimal motion strategies and adapting to real-time changes in the environment and task requirements. The core of the system is a dynamically adjusted trajectory generation process, allowing the robot to navigate complex scenarios with greater flexibility and efficiency. The system is trained using an RL algorithm, and the learned policy is then utilized for real-time trajectory optimization. This approach demonstrates the potential for significantly enhancing the adaptability and intelligence of robots in various applications. The system's ability to learn and adapt provides a robust solution for dynamic environments, addressing limitations inherent in traditional planning techniques. ---
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
This paper explores a novel approach to reinforcement learning (RL) that integrates causal inference to enhance both the reliability and interpretability of decision-making processes. Traditional RL algorithms often operate as "black boxes," lacking transparency into the reasoning behind their actions. This research addresses this limitation by leveraging causal relationships within an environment to guide the learning process. We propose a framework where causal graphs are utilized to understand the underlying dynamics of the environment, informing the selection of optimal policies in an RL agent. Furthermore, we employ explainable AI (XAI) techniques to provide insights into the agent's decision-making rationale. The core claim is that by combining causal reasoning with RL and XAI, we can significantly improve the robustness and trustworthiness of the learned policies, while simultaneously increasing their interpretability. The proposed method aims to provide a more reliable and understandable system than standard RL approaches, particularly in complex and uncertain environments.
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
This paper introduces a Dynamic Semantic Network Generator (DSNG), a novel system designed to automatically construct and maintain semantic networks representing complex, dynamic systems. The core of the DSNG is a reinforcement learning (RL) framework that adapts the network's structure and connections in response to changes in system states and external events. Unlike traditional static semantic networks, the DSNG allows for real-time semantic understanding and evolution, offering a more accurate and flexible representation of complex systems. The system utilizes a state representation, action space, and reward function to learn optimal network configurations. The key innovation lies in the dynamic adjustment mechanism, leveraging RL to continuously refine the network's topology and weights, mirroring the evolving relationships within the system under observation. This approach addresses the limitations of static models, providing a robust solution for analyzing and understanding systems where relationships are not fixed but constantly shifting. The system's performance is evaluated through simulations, demonstrating its ability to capture and adapt to complex temporal dependencies.
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
This paper introduces Dynamic Context-Aware Neural Architecture Search (DCA-NAS), a novel approach to neural architecture search that addresses the limitations of traditional NAS methods by incorporating dynamic contextual information during the search process. The core claim of DCA-NAS is that real-time analysis of intermediate representations generated during training—such as activation values and gradient information—can dynamically adjust the exploration strategy of the NAS algorithm, thereby accelerating the discovery of optimal architectures. DCA-NAS combines generative NAS techniques (e.g., evolutionary algorithms, reinforcement learning) with search-based NAS (e.g., differentiable architecture search) through an integrated "context module." This module learns to identify the contextual factors most influential on architecture selection, considering not only the statistical properties of the searched architecture but also the statistical properties of intermediate representations, training loss gradients, and learning rates. Based on this contextual understanding, the module dynamically adjusts the search strategy of the generative component or the search space of the search-based component. Furthermore, DCA-NAS divides the search process into multiple "sub-search" stages, each optimized for a specific context. Experimental results demonstrate DCA-NAS's ability to achieve superior architecture quality and search efficiency compared to static NAS methods, particularly in scenarios with diverse tasks and datasets.
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
This paper investigates the potential of dynamically self-organizing neural networks inspired by biological systems. Traditional static neural networks often struggle with adaptability and efficiency in complex environments. We propose a novel approach that leverages reinforcement learning to dynamically adjust connection strengths and incorporates probabilistic mechanisms for synaptic generation and removal, mirroring the plasticity observed in biological neural networks. The network topology itself evolves over time, driven by the reinforcement learning process, leading to the formation of more effective connection patterns. Our simulations demonstrate that this dynamic topology self-organization significantly enhances learning and inference capabilities compared to static networks. The core claim is that by simulating dynamic connections and synaptic changes, we can achieve more efficient and adaptive learning and reasoning. The central mechanism utilizes reinforcement learning to adjust connection strengths based on network output success rates and introduces probabilistic synaptic generation and deletion to mimic biological synaptic plasticity. The network topology evolves over time, creating optimized connection patterns.
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
This paper investigates the application of Multi-Agent Reinforcement Learning (MARL) to the complex system scheduling problem. Traditional scheduling methods often struggle to adapt to dynamic and intricate system environments, leading to suboptimal performance. This research proposes a novel framework leveraging MARL to address these challenges. The core idea involves deploying multiple agents, each responsible for scheduling a specific portion of the complex system. These agents operate independently, learning optimal scheduling policies through interaction with the environment and a carefully designed reward function. The system's overall efficiency and performance are enhanced through the coordinated learning and adaptation of these individual agents. The key contribution lies in the intelligent coordination of agents within a reinforcement learning framework, resulting in improved scheduling outcomes. This approach offers a scalable solution for managing complex systems with high degrees of variability and dynamism.
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
This paper explores the application of Deep Reinforcement Learning (DRL) to automate software testing. Traditional software testing methodologies heavily rely on predefined rules and templates, often proving inadequate against the complexity and variability of modern software systems. This research proposes a novel approach leveraging DRL to intelligently generate test cases and execute test procedures automatically. The core mechanism involves training a deep reinforcement learning agent to learn optimal testing strategies and methods. The agent learns through trial and error, maximizing reward based on test execution outcomes. We present a framework for implementing this approach, focusing on the challenges and potential benefits of automating the testing process with a learning agent. The results suggest that DRL can significantly improve the efficiency and effectiveness of software testing, particularly in scenarios with high complexity and evolving requirements. This work contributes to the growing field of intelligent software testing and offers a promising avenue for reducing testing costs and improving software quality.
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
