This paper presents a novel approach to simulating subconscious decision-making processes by leveraging multi-modal data fusion. The core idea is to construct a computational model capable of mirroring the complexities of human subconscious decision-making, moving beyond traditional behavioral analysis. We employ a graph neural network (GNN) architecture for robust multi-modal data integration, transforming diverse data streams – including visual, auditory, and tactile information – into a unified representation. This representation is then utilized within a reinforcement learning framework to simulate the subconscious decision-making process, explicitly modeling the interactive effects between different modalities. The resulting model provides a deeper understanding of how individuals make decisions without conscious awareness, offering potential applications in fields such as robotics, human-computer interaction, and cognitive modeling. The key innovation lies in the comprehensive incorporation of multi-modal interactions, providing a more accurate representation of the human subconscious than existing approaches. We define the following key equations to represent the core processes within the model: Let *xi* represent the input vector for modality *i*, where *i* ∈ {V, A, T}, representing Visual, Auditory, and Tactile modalities, respectively. The dimensionality of each *xi* is denoted as *di*. The multi-modal fusion process can be expressed as: * *xfused* = FusionNetwork(*xV*, *xA*, *xT*) Where *xfused* is the fused representation and FusionNetwork is the graph neural network. The reinforcement learning agent's decision-making process is governed by the following equation: * *ai* = argmaxj [Q( *xfused*, *aj* ) + β * R( *xfused*, *aj*)] Where *ai* is the action taken, *Q* is the Q-function estimating the expected reward, *R* is the reward function, and β is a weighting factor. The model's training objective can be formalized as: Minimize Eτ [ Σt=0T γt *R( *xfused*, *at* )] Where τ is a trajectory, *R* is the reward function, γ is the discount factor, and T is the time horizon.
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
This paper proposes a novel hierarchical reinforcement learning (HRL) framework that leverages intrinsic motivation, meta-learning, and a hierarchical architecture to address the limitations of traditional HRL methods concerning exploration and generalization. The core idea is to integrate these three components to create a more adaptive and efficient learning system. At each level of the hierarchy, intrinsic motivation, specifically novelty seeking, encourages exploration. Simultaneously, meta-learning dynamically adjusts the learning rate and policy updates, enabling rapid adaptation to diverse tasks. We demonstrate the effectiveness of this approach through a theoretical analysis and a conceptual framework, outlining the key components and their interactions. The resulting system offers improved learning speed and robustness compared to standard HRL techniques, particularly when facing complex and varied environments. This work lays the groundwork for future research in developing truly adaptable and intelligent hierarchical agents.
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
This paper presents Adaptive Resource Allocation in Cloud Computing (ARAC), a novel approach to cloud management that leverages reinforcement learning for dynamic resource allocation. Traditional cloud platforms often rely on manual configuration and pre-defined rules, leading to suboptimal resource utilization and potentially degraded user experience. ARAC addresses this limitation by employing a reinforcement learning-based resource scheduling algorithm. This algorithm continuously learns and adapts to changing conditions, optimizing the allocation of virtual machines, storage, and bandwidth based on user requests, resource utilization rates, and system load. The core claim of ARAC is to design a cloud platform capable of automatically adjusting resource allocations in response to evolving demands. The system's mechanism involves a dynamic adjustment of resources, aiming for optimal utilization and a superior user experience. This paper outlines the architecture, the reinforcement learning framework, and the key components of ARAC, demonstrating its potential to significantly improve cloud computing efficiency and responsiveness. ---
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
Meta-learning, the learning to learn, has shown significant promise in tackling complex tasks. However, a prevalent limitation lies in the reliance on static reward functions and environment dynamics, often simplifying the learning process and potentially hindering generalization to real-world scenarios. This paper introduces a novel adaptive meta-learning framework that addresses this limitation by dynamically adjusting the simulated environment's dynamics during the meta-training phase. The core idea is to utilize a learned Markov model to govern the environment's behavior, and to adapt the model parameters based on the agent's performance. This creates a continually evolving training environment, mirroring the inherent dynamism and uncertainty of real-world systems. We demonstrate that this approach leads to improved meta-learning performance compared to traditional static environment meta-learning methods. The algorithm incorporates key elements of reinforcement learning and Bayesian modeling to achieve adaptability and robustness. The primary formula representing the updated Markov model is: (Qt+1 | Qt, At) = f(Qt, At), where Qt+1 represents the state of the Markov model at time t+1, Qt is the state at time t, and At is the agent's action at time t. The function f is a parameterized function that is updated during the meta-training process. We explore the theoretical implications of this dynamic adaptation and discuss potential avenues for future research.
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
This paper introduces a novel method for reconstructing complex topological structures from a set of modular building blocks, utilizing advanced pattern recognition and generative algorithms. The core principle centers on the creation of a "Semantic Topological Bridge" – an AI system that analyzes relationships between modular elements and automatically generates a bridge that connects them, enabling the formation of higher-level topological structures. This approach addresses the challenge of translating abstract topological concepts into concrete, usable representations, offering a significant advancement over traditional topological modeling techniques. The paper details the methodology, demonstrates its efficacy through illustrative examples, and explores the potential applications of this technology across diverse fields.
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
This paper presents an innovative framework for material design utilizing adaptive topology, leveraging generative artificial intelligence to automatically generate and optimize material topologies. Traditional materials design relies heavily on trial-and-error experimentation, often leading to suboptimal material properties and manufacturing challenges. Our approach addresses these limitations by employing a dynamic, iterative process guided by computational simulations, specifically focusing on the interplay between structural integrity and desired material characteristics. We introduce a novel method for generating topology, incorporating feedback loops that continuously refine the resulting structures based on established material science principles and predictive modeling. The framework's core mechanism centers on the synergistic integration of generative AI and established simulation techniques to achieve a significant improvement in material design efficiency and predictive accuracy. This work demonstrates the potential of adaptive topology to unlock new possibilities in material science, pushing the boundaries of material design and facilitating the creation of materials with tailored properties for a diverse range of applications.
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
This paper introduces a novel method for reconstructing complex topological structures from a set of modular building blocks, utilizing advanced pattern recognition and generative algorithms. The core principle centers on the creation of a "Semantic Topological Bridge" – an AI system that analyzes relationships between modular elements and automatically generates a bridge that connects them, enabling the formation of higher-level topological structures. This approach addresses the challenge of translating abstract topological concepts into concrete, usable representations, offering a significant advancement over traditional topological modeling techniques. The paper details the methodology, demonstrates its efficacy through illustrative examples, and explores the potential applications of this technology across diverse fields.
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
