Welcome to our monthly digest, where you can catch up with any AIhub stories you may have missed, peruse the latest news, recap recent events, and more. This month, we hear about a Ge'ez-Amharic-English dataset, meet AAAI Fellow Mausam, and learn about network resource allocation. Each year the AAAI recognizes a group of individuals who have made significant, sustained contributions to the field of artificial intelligence by appointing them as Fellows. Over the course of the next few months, we'll be talking to some of the 2024 AAAI Fellows. In the first interview in the series, we met Professor Mausam and found out about his research, career path, mentorship, and why it is important to add some creative pursuits to your life.

In their work FlagVNE: A Flexible and Generalizable Reinforcement Learning Framework for Network Resource Allocation, accepted at IJCAI 2024, Tianfu Wang, Qilin Fan, Chao Wang, Long Yang, Leilei Ding, Nicholas Jing Yuan and Hui Xiong introduce a framework for addressing resource allocation problems. In this interview, Tianfu Wang tells us more about their framework, the implications of their research, and what they are planning next. Our paper focuses on addressing resource allocation problems using a reinforcement learning (RL) framework, specifically in the domain of network virtualization, known as virtual network embedding (VNE). VNE involves efficiently mapping virtual network requests onto physical infrastructure. However, existing RL-based VNE methods are limited by the unidirectional action design and one-size-fits-all training strategy, resulting in restricted searchability and generalizability.

This paper studies an online optimal resource reservation problem in communication networks with job transfers where the goal is to minimize the reservation cost while maintaining the blocking cost under a certain budget limit. To tackle this problem, we propose a novel algorithm based on a randomized exponentially weighted method that encompasses long-term constraints. We then analyze the performance of our algorithm by establishing an upper bound for the associated regret and the cumulative constraint violations. Finally, we present numerical experiments where we compare the performance of our algorithm with those of reinforcement learning where we show that our algorithm surpasses it.