Results 61 to 70 of about 325,728 (267)
MGDP: Mastering a Generalized Depth Perception Model for Quadruped Locomotion
Advanced Science, EarlyView.ABSTRACT Perception‐based Deep Reinforcement Learning (DRL) controllers demonstrate impressive performance on challenging terrains. However, existing controllers still face core limitations, struggling to achieve both terrain generality and platform transferability, and are constrained by high computational overhead and sensitivity to sensor noise.
Yinzhao Dong +9 more
wiley +1 more source
International Journal of Interactive Multimedia and Artificial Intelligence, 2016 This paper presents the results and conclusions found when predicting the behavior of gamers in commercial videogames datasets. In particular, it uses Variable-Order Markov (VOM) to build a probabilistic model that is able to use the historic behavior of
Yago Saez +3 more
doaj +1 more source
Sensors, 2022 In industrial processes, the composition of raw material and the production environment are complex and changeable, which makes the production process have multiple steady states.
Ning Chen +5 more
doaj +1 more source
Exceptional Antimodes in Multi‐Drive Cavity Magnonics
Advanced Electronic Materials, EarlyView.Driven‐dissipative cavity‐magnonics provides a flexible platform for engineering non‐Hermitian physics such as exceptional points. Here, using a four‐port, three‐mode system with controllable microwave interference, antimodes and coherent perfect extinction (CPE) are realized, enabling active tuning to antimode exceptional points.
Mawgan A. Smith +4 more
wiley +1 more source
Modeling the exchange rate using price levels and country risk
Cogent Economics & Finance, 2015 This paper builds two factor discrete time models in order to investigate the effect of sovereign risk on the nominal exchange rates in a Markov switching framework. The empirical section of the paper uses seven currencies from Chile, the Czech Republic,
Gábor Regős
doaj +1 more source
Experimental results : Reinforcement Learning of POMDPs using Spectral Methods [PDF]
, 2017 We propose a new reinforcement learning algorithm for partially observable Markov decision processes (POMDP) based on spectral decomposition methods.
Anandkumar, Animashree +2 more
core +1 more source
The Geography of Success: A Spatial Analysis of Export Intensity in the Italian Wine Industry
Agribusiness, EarlyView.ABSTRACT This paper investigates the paradox of how Italy's fragmented, SME‐dominated wine industry achieves global export success. Moving beyond purely firm‐centric explanations, we test whether export intensity is spatially dependent, clustering geographically in regional ecosystems.
Nicolas Depetris Chauvin, Jonas Di Vita
wiley +1 more source
A general framework for quantifying the effects of land-use history on ecosystem dynamics
, 2019 Land-use legacies are important for explaining present-day ecological patterns and processes. However, an overarching approach to quantify land-use history effects on ecosystem properties is lacking, mainly due to the scarcity of high-quality, complete ...
Blondeel, Haben +8 more
core +1 more source
AI in chemical engineering: From promise to practice
AIChE Journal, EarlyView.Abstract Artificial intelligence (AI) in chemical engineering has moved from promise to practice: physics‐aware (gray‐box) models are gaining traction, reinforcement learning complements model predictive control (MPC), and generative AI powers documentation, digitization, and safety workflows.
Jia Wei Chew +4 more
wiley +1 more source
PPM-Decay: A computational model of auditory prediction with memory decay.
PLoS Computational Biology, 2020 Statistical learning and probabilistic prediction are fundamental processes in auditory cognition. A prominent computational model of these processes is Prediction by Partial Matching (PPM), a variable-order Markov model that learns by internalizing n ...
Peter M C Harrison +3 more
doaj +1 more source