Results 61 to 70 of about 86,402 (258)

Reinforcement learning: Computational theory and biological mechanisms [PDF]

HFSP Journal, 2007
Reinforcement learning is a computational framework for an active agent to learn behaviors on the basis of a scalar reward signal. The agent can be an animal, a human, or an artificial system such as a robot or a computer program. The reward can be food, water, money, or whatever measure of the performance of the agent.
openaire   +2 more sources

Ontology‐Aligned Structuring and Reuse of Multimodal Materials Data and Workflows Toward Automatic Reproduction

Advanced Engineering Materials, EarlyView.
Reproduction of stacking fault energy calculations from literature with a semi‐automated large language model‐assisted extraction procedure: extraction of simulation protocol, atomistic structures, computational parameters, and reported results, ontology alignment, knowledge graph construction and, finally, recomputation forvalidation.
Sepideh Baghaee Ravari, Abril Azocar Guzman, Sarath Menon, Stefan Sandfeld, Tilmann Hickel, Markus Stricker   +5 more
wiley   +1 more source

Do student teachers experience self-worth threats in computational thinking?

Computers in Human Behavior Reports
Theory: The successful implementation of computational thinking into primary schools requires that primary school teachers feel safe and confident in teaching this topic to young learners. However, many student teachers face low expectancy of success and
Veronika Barkela, Areum Han, Anke Maria Weber   +2 more
doaj   +1 more source

Computational Practices, Educational Theories, and Learning Development

Problemos, 2018
[full article, abstract in English; abstract in Lithuanian] Many countries are adopting computing (or informatics) in schools, for pupils from 5 years of age. Educational philosophies (and learning theories) that such curricula might be based on are not clear in curriculum documentation.
Passey, Don, Dagienė, Valentina, Atieno, Loice, Baumann, Wilfried   +3 more
openaire   +4 more sources

Modeling Dislocation Cutting of γ′ Precipitates in Ni‐Base Superalloys: Linking Atomistic and Dislocation Dynamics Simulations

Advanced Engineering Materials, EarlyView.
Dislocation cutting of γ′ precipitates in Ni‐based superalloys is investigated by linking atomistic simulations with discrete dislocation dynamics. The critical cutting stress is shown to be governed by the antiphase boundary energy, while line tension effects promote edge‐preferred cutting.
Frédéric Houllé, Siwen Gao, Marc Fivel, Anxin Ma, Daria Smirnova, Sriram Anand, Arun Prakash, Julien Guénolé, Alexander Hartmaier, Erik Bitzek   +9 more
wiley   +1 more source

COMPUTATIONAL THINKING SKILLS IN MATHEMATICS PROBLEM-SOLVING: A SYSTEMATIC LITERATURE REVIEW

Jurnal Nalar Pendidikan
Computational thinking and Problem-Based Learning have educational benefits for learners and have been widely used in teaching and learning. However, research into integrating these theories into the teaching and learning process is scarce, leaving ...
Shera Afidatunisa, Dadang Juandi
doaj   +1 more source

Towards an Atlas of Computational Learning Theory.

, 2016
A major part of our knowledge about Computational Learning stems from comparisons of the learning power of different learning criteria. These comparisons inform about trade-offs between learning restrictions and, more generally, learning settings; furthermore, they inform about what restrictions can be observed without losing learning power.
Timo Kötzing, Martin Schirneck
openaire   +3 more sources

A Co-Evolutionary Computing for Statistical Learning Theory [PDF]

International Journal of Fuzzy Logic and Intelligent Systems, 2005
Learning and evolving are two basics for data mining. As compared with classical learning theory based on objective function with minimizing training errors, the recently evolutionary computing has had an efficient approach for constructing optimal model without the minimizing training errors.
openaire   +1 more source

New AI‐Assisted Approach for Expanding the Solution Space: Application to Lattice Structure Design

Advanced Engineering Materials, EarlyView.
This work introduces an innovative framework for designing structured materials by ex panding the design space through reparameterization of qualitative variables into continuous structural descriptors. Combined with machine‐learning‐based prediction and multi‐objective optimization, the approach enables the discovery of novel lattice architectures ...
G. H. Gahimbare, Y. Beideler, D. Chune, R. De Guio, T. Roland, H. Chibane   +5 more
wiley   +1 more source

An Improved Extreme Learning Machine Based on Full Rank Cholesky Factorization

MATEC Web of Conferences, 2018
Extreme learning machine (ELM) is a new novel learning algorithm for generalized single-hidden layer feedforward networks (SLFNs). Although it shows fast learning speed in many areas, there is still room for improvement in computational cost.
Liu Zuozhi, Wu JinJian, Wang Jianpeng
doaj   +1 more source