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Search · four archives
24 papers · ranked by Valyu relevance
Jinzhong Zhang, Hongkai Li, Tan Zhang, Zhen He + 2 more
The greater cane rat algorithm (GCRA) represents an emerging swarm intelligence paradigm derived from the instinctual survival patterns exhibited by greater cane rats (GCRs), which simulates the typical male-dominated survival patterns of the GCR species, including rainy-season mating and reproduction behaviors…
Shouang Wei, Min Zhang, Xin Lin, Bo Jiang + 2 more
—Large language models (LLMs) are shifting from answer providers to intelligent tutors in educational settings, yet current supervised fine-tuning methods only learn surface teaching patterns without dynamic adaptation capabilities. Recent reinforcement learning approaches address this limitation but face two critical…
Seksan Winyangkul, Mahmoud Alfouneh, Suwin Sleesongsom, Ameersing Luximon
A self-adaptive variant of teaching-learning-based optimization, incorporating a diversity archive and referred to as ATLBO-DA, has been proposed. Combined with a new path repairing technique (PRT), it efficiently accomplishes the four-bar linkage path generation problem, but an upgraded version is needed. An update of…
Dan Liu, Feifei Li, Di Lu, Wenhua Yu + 1 more
Emotion and cognition analysis in educational interventions is crucial for enhancing personalized learning outcomes. However, existing models often encounter challenges in multimodal data integration and adaptive strategy optimization. To address these challenges, we propose the SymCART model, an intelligent…
Russell Jeter, Dmitrii Todorov, Yaroslav Molkov
A clinician guiding a stroke patient through a 45-minute rehabilitation session, a coach planning a training day, a teacher choosing the order of practice problems, they all face the same question: “given everything practiced so far, what should the next trial be?” The motor-learning literature offers two coarse…
Francesca Mignacco, Francesco Mori, Derek Abbott
Learning is a complex dynamical process shaped by a range of interconnected decisions. Careful design of hyperparameter schedules for artificial neural networks or efficient allocation of cognitive resources by biological learners can dramatically affect performance. Yet, theoretical understanding of optimal learning…
Ali Larian, Qian Lin, Chang Zong Wu, Daniel S. Brown
As autonomous agents are increasingly deployed across diverse operational contexts, aligning their behavior with human intent demands reward functions that remain robust to such changes rather than overfitting to any single environment. Inverse reinforcement learning (IRL) provides a principled way to infer such…
Jan Arne Telle, Brigt Håvardstun, Jose Hernandez-Orallo
Most models of machine teaching and learning assume the learner makes no errors in its internal deductive inference. However, humans and large language models in few-shot learning regimes are two important examples of learners where this does not hold. They fail on some consistency checks, and they can fail…
Authors not listed
Bayesian optimization (BO) has become increasingly important for experimental optimization across scientific domains, yet implementing BO pipelines requires significant programming expertise and familiarity with specialized frameworks. This creates a barrier for domain experts who could benefit from BO but lack the…
Yang Cao, Bingchuan Wu, Miao Wen, Yang Lou
Differential evolution has become one of the mainstream solvers for complex optimization problems due to its concise structure and strong global search ability. However, the performance of the DE algorithm is highly sensitive to its mutation and crossover strategies and related control parameters. Traditional adaptive…
Guolong Zhong, Venkatesh Chennam Vijay
The concept of parametric design is changing the way 3D modeling works, allowing precise manipulation of complex forms in the areas of architecture, digital fabrication, and product design. However, exploring and optimizing large coupled spaces of parameters remains a significant computational challenge. We present a…
Changin Oh, Kathleen P. Wilkie
We present the Toroidal Search Algorithm (TSA), a novel population-based metaheuristic optimization method inspired by the topology of a torus. Conventional metaheuristics frequently suffer from boundary stagnation, a phenomenon that severely degrades performance in bounded and high-dimensional search spaces. TSA…
Authors not listed
Realizing the promise of artificial intelligence (AI) to accelerate scientific progress and deliver technological impact depends on how effectively AI can be integrated into real-world decision- making processes. As Peter Norvig states, “Somewhat remarkably, almost all AI research until very recently has assumed that…
Deepak Edakkattil Gopinath, Xiongyi Cui, Jonathan DeCastro, Avinash Balachandran + 1 more
Learning-based automated coaching systems for complex motor tasks such as high-performance driving remain limited in the ability to be adaptive by their reliance only on local, context-dependent reasoning, failing to account for the long-term temporal nature of student learning and the cumulative impact of repeated…
Authors not listed
The integration of machine learning methods is transforming many areas of research by, for instance, accelerating molecular dynamics simulations and enabling improved prediction and optimization of chemical reactions. However, despite this progress, the adoption of data-driven approaches in atomic layer deposition…
Jaward Sesay, Yue Yu, Siwei Dong, Yemin Shi + 2 more
Effective personalized AI-assisted learning demands systems that can not only generate accurate learner-specific educational materials, but also dynamically adapt their instruction to diverse learners. However, existing educational agents have primarily focused on lecture content automation and simulations, which often…
Dhruva V. Raman, Christopher R. Dunne, Katie Davyson, Timothy O’Leary
Animals inhabit continually changing environments where it is not always possible to infer causes of relevant changes, such as the appearance of a new threat. In such nonstationary settings, learning a predictive model is challenging because a surprising observation could be due to chance, or due to systematic but…
Francesca Greenstreet, Jesse P. Geerts, Juan A. Gallego, Claudia Clopath
The initial stage of learning motor skills involves exploring vast action spaces, making it impractical to learn the value of every possible action independently. This poses a challenge for standard reinforcement learning approaches, which excel in constrained domains but struggle when the space of possible actions is…
Authors not listed
Incorporating prior domain knowledge into Bayesian optimization (BO) remains difficult for statistical methods, which also typically suffer from limited interpretability. Large language models (LLMs) offer complementary strengths in reasoning and knowledge integration, but it remains unclear when and how they improve…
Xiaotong Fang, Payam Piray
Inferring the true cause of noise—distinguishing between volatility (environmental change) and stochasticity (outcome randomness)—is essential for learning in noisy environments. While most studies rely on binary outcomes, previous models are designed for continuous outcome and use ad hoc approximations to handle…
Authors not listed
This comprehensive review examines the evolution of autonomous materials synthesis laboratories that integrate artificial intelligence with advanced robotics to accelerate discovery. Traditional materials development pipelines typically require 10-20 years, but self-driving laboratories (SDLs) and Materials…
Authors not listed
Artificial intelligence (AI) is reshaping chemical engineering. Still, its role in safety-critical operations is limited because we rarely see tools that link physical models with data-driven methods. This study brings together three elements: physics-constrained neural networks, uncertainty quantification, and a…
Authors not listed
Machine olfaction—the artificial replication of the sense of smell—faces significant challenges due to the absence of large, standardized training datasets. Unlike vision, language, and audio models, which benefit from extensive corpora such as ImageNet, GLUE, and AudioSet, olfaction lacks scaled equivalents and…
Jianning Chen, Masakazu Taira, Kenji Doya
Behavioral strategies can change in response to environmental and internal states, either gradually or abruptly, enabling flexible adaptation. Such strategy regulation is central to meta-learning, the ability to learn to learn. Previous studies analyzed temporal or condition-dependent strategy change using models and…