Zobrazeno 1 - 10
of 2 793
pro vyhledávání: '"Kebriaei P"'
This paper aims to investigate the impact of interference in social network algorithms via user-bot interactions, focusing on the Stochastic Bounded Confidence Model (SBCM). This paper explores two approaches: positioning bots controlled by agents in
Externí odkaz:
http://arxiv.org/abs/2409.11426
Autor:
Ghaemi, Hafez, Jamshidi, Shirin, Mashreghi, Mohammad, Ahmadabadi, Majid Nili, Kebriaei, Hamed
Markov games (MGs) and multi-agent reinforcement learning (MARL) are studied to model decision making in multi-agent systems. Traditionally, the objective in MG and MARL has been risk-neutral, i.e., agents are assumed to optimize a performance metric
Externí odkaz:
http://arxiv.org/abs/2406.06041
Classical multi-agent reinforcement learning (MARL) assumes risk neutrality and complete objectivity for agents. However, in settings where agents need to consider or model human economic or social preferences, a notion of risk must be incorporated i
Externí odkaz:
http://arxiv.org/abs/2402.05906
Autor:
Ghavami, Mahsa, Bakhshayesh, Babak Ghaffarzadeh, Haeri, Mohammad, Como, Giacomo, Kebriaei, Hamed
This paper introduces a consensus-based generalized multi-population aggregative game coordination approach with application to electric vehicles charging under transmission line constraints. The algorithm enables agents to seek an equilibrium soluti
Externí odkaz:
http://arxiv.org/abs/2310.11983
In this paper, we focus on modeling and analysis of demand-side management in a microgrid where agents utilize grid energy and a shared battery charged by renewable energy sources. We model the problem as a generalized stochastic dynamic aggregative
Externí odkaz:
http://arxiv.org/abs/2310.02996
This paper proposes a novel control design for voltage tracking of an islanded AC microgrid in the presence of {nonlinear} loads and parametric uncertainties at the primary level of control. The proposed method is based on the Tube-Based Robust Model
Externí odkaz:
http://arxiv.org/abs/2309.00742
Dynamic Difficulty Adjustment (DDA) is a viable approach to enhance a player's experience in video games. Recently, Reinforcement Learning (RL) methods have been employed for DDA in non-competitive games; nevertheless, they rely solely on discrete st
Externí odkaz:
http://arxiv.org/abs/2308.12726
Publikováno v:
IET Generation, Transmission & Distribution, Vol 18, Iss 18, Pp 2943-2955 (2024)
Abstract Thanks to reinforcement learning (RL), decision‐making is more convenient and more economical in different situations with high uncertainty. In line with the same fact, it is proposed that prosumers can apply RL to earn more profit in the
Externí odkaz:
https://doaj.org/article/38638c84a6444ef3b8f0ed924b20bfbb
Autor:
Jayastu Senapati, Elias Jabbour, Nicholas J. Short, Nitin Jain, Fadi Haddad, Tharakeswara Bathala, Iuliia Kovalenko, Aram Bidikian, Farhad Ravandi, Issa Khouri, Tapan M. Kadia, Rebecca Garris, Guillermo Montalban Bravo, Kelly Chien, Elizabeth Shpall, Partow Kebriaei, Hagop M. Kantarjian
Publikováno v:
Blood Cancer Journal, Vol 14, Iss 1, Pp 1-4 (2024)
Externí odkaz:
https://doaj.org/article/cb51845e37254d6d8ab98fdbb4b106f4
We propose an incentive mechanism for the sponsored content provider market in which the communication of users can be represented by a graph and the private information of the users is assumed to have a continuous distribution function. The content
Externí odkaz:
http://arxiv.org/abs/2303.14113