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2,613 papers
This paper explores the potential of algorithmic emergence to generate complex, fractal-like structures with inherent properties of self-replication and adaptation. We propose a novel design incorporating a genetic algorithm coupled with reinforcement learning to iteratively refine a structure, fostering self-organizat...
This is the author’s preprint version. It has not undergone journal peer review. The manuscript presents a matched stochastic benchmark of multi-agent reinforcement-learning architectures and heuristic dispatching policies for multi-stage OSAT manufacturing.
This paper investigates the application of probability-based constraint satisfaction (CS) algorithms to dynamic problem environments. Traditional CS methods often struggle with complex landscapes and require extensive search, leading to computational bottlenecks. We propose a novel reinforcement learning (RL) framework...
These findings support maneuver phase as an interpretable context for prediction-horizon adaptation within the evaluated architecture, while limiting the conclusions to the investigated scenario and experimental setting.
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This paper investigates the challenges of achieving robust cooperative navigation in multi-agent reinforcement learning (MARL) scenarios, addressing the limitations of relying solely on extrinsic rewards. We propose a novel framework integrating intrinsic motivation – specifically, curiosity and surprise – into each ag...
This paper investigates the use of dynamic constraint satisfaction within fractal generation algorithms to enhance structure complexity and robustness. Traditional fractal algorithms often rely on fixed constraints, limiting the potential for intricate and diverse forms. We propose an algorithmic approach that utilizes...
This paper introduces an innovative adaptive quantum algorithm designed to address the limitations of traditional optimization techniques, particularly when dealing with non-linear problems. The algorithm leverages reinforcement learning to dynamically adjust its parameters based on the input data, offering a significa...
This paper presents a resilient learning‐based adaptive sliding‐mode consensus control framework for nonlinear multi‐agent systems subject to switching communication topologies and actuator‐side cyberattacks. A distributed sliding‐mode leader‐follower tracking consensus protocol is first developed to ensure robust co...
Quantum field theory, a cornerstone of modern physics, traditionally employs static, predefined geometries to represent and model quantum phenomena. However, limitations in this approach hinder the exploration of novel quantum states and emergent properties. This paper proposes a novel dynamic fractal geometry algorith...
This paper explores the fusion of nonlinear time series systems (NTS) modeling with reinforcement learning (RL) for addressing complex control problems characterized by high nonlinearity and time-varying dynamics. The core idea is to leverage the representation power of NTS models to capture system behavior and then ut...
The Single-Machine Scheduling Problem with Sequence-Dependent Setup Times (SMSP-SDST) is a classical NP-hard combinatorial optimization problem encountered in intelligent manufacturing, production planning, semiconductor processing, logistics, and adaptive production systems. Conventional exact optimization techniques...
This paper explores the design and implementation of a novel self-organizing constraint network architecture, tentatively named "Self-Organizing Constraint Networks with Adaptive Noise," leveraging reinforcement learning and quantum-enhanced constraint adaptation. The core claim centers around dynamically adjusting pri...
A Blog post by Ai2 on Hugging Face
Research Manager Mike Walker reflects on a career spanning hacker culture, cybersecurity, and AI, sharing lessons about trust, teamwork, leadership, and why solving difficult problems requires curiosity, judgment, agility—and an understanding of the rules. The post Learn the rules: The hacker mindset appeared first on Microsoft Research.
Learn how Google Antigravity and our Cheminformatics Skills accelerate drug discovery workflows, search compounds, and propose novel rigidified macrocycles.
Prabha Jha used her experience with MIT Open Learning’s program for senior executives to develop AI solutions for her employer, Verizon, and to write a book.
Education Innovation
Assistant Professor Benjamin Lindquist studies how engineers deliberately injected randomness into early AI, giving rise to deep-learning models that sometimes hallucinate.
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