108 papers indexed here
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This paper proposes a novel approach to system management leveraging dynamic topological information for intelligent inference. The core idea is to construct and maintain a semantic network that reflects the evolving relationships between system components, considering factors like network latency, device load, and com...
This paper investigates the application of multi-agent reinforcement learning (MARL) for optimizing complex systems. Traditional system optimization approaches often rely on centralized control or distributed control strategies, which can struggle with the inherent complexity and dynamic nature of many real-world syste...
This paper presents a novel approach to swarm control that leverages collective intelligence to achieve adaptive behavior. Traditional swarm control methods often rely on pre-defined rules and lack the flexibility to respond to dynamic environmental changes or unexpected events. This work addresses this limitation by c...
Deep learning models often exhibit sensitivity to training data, leading to suboptimal performance. Traditional regularization techniques, while effective, can be inflexible and require extensive hyperparameter tuning. This paper introduces an adaptive topology for deep learning model regularization that dynamically ad...
This paper introduces a novel self-adaptive reinforcement learning algorithm based on topological networks. Traditional reinforcement learning approaches often rely on predefined reward functions and static policies, limiting their ability to adapt to dynamic environments. Our algorithm leverages the inherent topologic...
This paper presents a novel approach to neural network optimization that dynamically adapts the network topology based on learned dependencies. The core idea is to leverage reinforcement learning, where two agents collaborate: one adjusts connection weights and the other modifies the network's structure (adding, removi...
This paper proposes a novel cognitive architecture for embodied artificial intelligence (AI) systems, designed to address the limitations of current approaches. The architecture leverages principles of cognitive science, specifically Bayesian inference, hierarchical control, and reinforcement learning, to facilitate ro...
This paper explores the application of adaptive chaos theory, leveraging reinforcement learning to dynamically adjust parameters of chaotic systems. Traditional approaches often rely on fixed, static parameter sets, limiting the system's flexibility and potential for robust behavior. This research introduces a novel sy...
This paper presents a novel approach to digital circuit synthesis leveraging the power of Evolutionary Reinforcement Learning (ERL). Traditional circuit synthesis methods often rely on deterministic algorithms that struggle to effectively manage the complexity and dynamism inherent in modern design requirements. This r...
This research investigates the application of reinforcement learning to automatically generate and optimize geometric patterns, focusing on identifying novel patterns exhibiting complex, self-organizing behavior. Traditional geometric pattern generation often relies on predefined rules, limiting the potential for truly...
This paper explores the application of reinforcement learning to dynamically optimize topology in parallel computing systems. The core claim is to develop a system capable of automatically adjusting the topology of parallel environments—specifically, the number of threads and processes—to maximize throughput and minimi...
This paper introduces the Neuro-Symbolic Symbiosis Architecture (NSSA), a novel framework designed to enhance reasoning and learning capabilities by dynamically adjusting the interaction between neural networks and symbolic knowledge bases. The core claim is that by modulating the interaction strength between these two...
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