Zobrazeno 1 - 10
of 19
pro vyhledávání: '"Åkerblom, Niklas"'
We propose a novel framework for contextual multi-armed bandits based on tree ensembles. Our framework integrates two widely used bandit methods, Upper Confidence Bound and Thompson Sampling, for both standard and combinatorial settings. We demonstra
Externí odkaz:
http://arxiv.org/abs/2402.06963
We consider a combinatorial Gaussian process semi-bandit problem with time-varying arm availability. Each round, an agent is provided a set of available base arms and must select a subset of them to maximize the long-term cumulative reward. Assuming
Externí odkaz:
http://arxiv.org/abs/2312.12676
Online decision making plays a crucial role in numerous real-world applications. In many scenarios, the decision is made based on performing a sequence of tests on the incoming data points. However, performing all tests can be expensive and is not al
Externí odkaz:
http://arxiv.org/abs/2308.10699
In this work, we address the problem of long-distance navigation for battery electric vehicles (BEVs), where one or more charging sessions are required to reach the intended destination. We consider the availability and performance of the charging st
Externí odkaz:
http://arxiv.org/abs/2301.07156
Autor:
Lindroth, Tobias, Svensson, Axel, Åkerblom, Niklas, Pourabdollah, Mitra, Chehreghani, Morteza Haghir
Today, there is an ongoing transition to more sustainable transportation, for which an essential part is the switch from combustion engine vehicles to battery electric vehicles (BEVs). BEVs have many advantages from a sustainability perspective, but
Externí odkaz:
http://arxiv.org/abs/2210.16002
Bottleneck identification is a challenging task in network analysis, especially when the network is not fully specified. To address this task, we develop a unified online learning framework based on combinatorial semi-bandits that performs bottleneck
Externí odkaz:
http://arxiv.org/abs/2206.08144
Modeling driver behavior provides several advantages in the automotive industry, including prediction of electric vehicle energy consumption. Studies have shown that aggressive driving can consume up to 30% more energy than moderate driving, in certa
Externí odkaz:
http://arxiv.org/abs/2203.02179
Publikováno v:
Artificial Intelligence, 317:103879 (2023)
Energy efficient navigation constitutes an important challenge in electric vehicles, due to their limited battery capacity. We employ a Bayesian approach to model the energy consumption at road segments for efficient navigation. In order to learn the
Externí odkaz:
http://arxiv.org/abs/2111.02314
Publikováno v:
Machine Learning 112 (2023) 131-150
In this paper, we study bottleneck identification in networks via extracting minimax paths. Many real-world networks have stochastic weights for which full knowledge is not available in advance. Therefore, we model this task as a combinatorial semi-b
Externí odkaz:
http://arxiv.org/abs/2109.08467
Publikováno v:
IJCAI 2020, Pages 2051-2057
Energy-efficient navigation constitutes an important challenge in electric vehicles, due to their limited battery capacity. We employ a Bayesian approach to model the energy consumption at road segments for efficient navigation. In order to learn the
Externí odkaz:
http://arxiv.org/abs/2003.01416