64 citations · 69 across the 7 of their papers we have counts for
8 papers
PriPG-RL: Privileged Planner-Guided Reinforcement Learning for Partially Observable Systems with Anytime-Feasible MPC
Mohsen Amiri, Ali Beikmohammadi, Sindri Magnuśson +1
This paper addresses the problem of training a reinforcement learning (RL) policy under partial observability by exploiting a privileged, anytime-feasible planner agent available e…
Socio-technical aspects of Agentic AI
Praveen Kumar Donta, Alaa Saleh, Ying Li +15
Agentic Artificial Intelligence (AI) represents a fundamental shift in the design of intelligent systems, characterized by interconnected components that collectively enable autono…
A Cost-Sensitive Transformer Model for Prognostics Under Highly Imbalanced Industrial Data
Ali Beikmohammadi, Mohammad Hosein Hamian, Neda Khoeyniha +3
The rapid influx of data-driven models into the industrial sector has been facilitated by the proliferation of sensor technology, enabling the collection of vast quantities of data…
Comparing NARS and Reinforcement Learning: An Analysis of ONA and -Learning Algorithms
Ali Beikmohammadi, Sindri Magnússon
In recent years, reinforcement learning (RL) has emerged as a popular approach for solving sequence-based tasks in machine learning. However, finding suitable alternatives to RL re…
Human-Inspired Framework to Accelerate Reinforcement Learning
Ali Beikmohammadi, Sindri Magnússon
Reinforcement learning (RL) is crucial for data science decision-making but suffers from sample inefficiency, particularly in real-world scenarios with costly physical interactions…
NARS vs. Reinforcement learning: ONA vs. Q-Learning
Ali Beikmohammadi
One of the realistic scenarios is taking a sequence of optimal actions to do a task. Reinforcement learning is the most well-known approach to deal with this kind of task in the ma…
Using MM principles to deal with incomplete data in K-means clustering
Ali Beikmohammadi
Among many clustering algorithms, the K-means clustering algorithm is widely used because of its simple algorithm and fast convergence. However, this algorithm suffers from incompl…
SWP-LeafNET: A novel multistage approach for plant leaf identification based on deep CNN
Ali Beikmohammadi, Karim Faez, Ali Motallebi
Modern scientific and technological advances allow botanists to use computer vision-based approaches for plant identification tasks. These approaches have their own challenges. Lea…