108 papers indexed here
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This paper investigates the application of reinforcement learning to automatically optimize lattice parameterization for complex systems, particularly in materials science and molecular modeling. The core claim is to develop a system that dynamically adjusts lattice parameters based on real-time feedback, aiming to imp...
Traditional cognitive computing systems often struggle with robust reasoning and adaptability, particularly in dynamic and uncertain environments. This research proposes a novel approach to cognitive computing by integrating Bayesian belief networks (BBNs) and reinforcement learning (RL). BBNs provide a framework for r...
The design of effective feedback loops is a fundamental challenge in many engineering and scientific disciplines. Traditional approaches often involve manual tuning of parameters to achieve desired outcomes, which is a time-consuming and potentially error-prone process. This paper introduces the Self-Organizing Feedbac...
This paper introduces a novel approach to large-scale code analysis leveraging hierarchical abstraction learning. The core idea is to accelerate code analysis by learning hierarchical representations of code structures, effectively capturing semantic relationships across multiple levels of granularity. A reinforcement...
This paper presents a novel approach to program code optimization leveraging reinforcement learning (RL). Traditional static code analysis techniques often fall short in achieving optimal performance due to their lack of adaptability and global optimization capabilities. Our proposed system utilizes an RL agent to dyna...
This paper investigates the application of Multi-Agent Reinforcement Learning (MARL) to the problem of distributed resource management. Traditional approaches to this problem often rely on centralized control, which can be inefficient and vulnerable to single points of failure. This research proposes a decentralized so...
This paper introduces a novel Relational Reinforcement Learning (RRL) framework designed to address the challenges of dynamic resource allocation in complex environments. Traditional Reinforcement Learning (RL) approaches often fall short when dealing with environments that constantly evolve and demand adaptive resourc...
This paper introduces a novel dynamic topology optimization algorithm powered by quantum machine learning. Traditional topology optimization methods often require manual design of the topology, leading to limited flexibility and computational expense. Our algorithm leverages quantum machine learning to automatically le...
This paper investigates a novel reinforcement learning algorithm utilizing adaptive reward function adjustment to enhance learning performance. Traditional reinforcement learning often relies on fixed reward functions, which can struggle to adapt to complex environments and unexpected situations. We propose a method th...
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 presents the Non-Euclidean Geometry Synchronization Algorithm (NESA), a novel algorithm designed to automatically generate and validate geometric theorems within complex non-Euclidean geometries. The core aim is to provide a verifiable framework for mathematical proof and abstraction, moving beyond manual co...
This paper investigates the application of Multi-Agent Reinforcement Learning (MARL) to optimize software development processes. Traditional software development methodologies often struggle with adaptability and efficiency, particularly in complex projects. This research proposes a novel approach leveraging MARL to dy...
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