Hierarchical reinforcement learning (RL) is a general framework which studies how to exploit the structure of actions and tasks to accelerate policy learning in large domains. Pri...
This paper analyzes a class of common-component allocation rules, termed no-holdback (NHB) rules, in continuous-review assemble-to-order (ATO) systems. We assume that component in...
Assigning tasks to agents is complex, especially in highly dynamic environments. Typical protocol-based approaches for task assignment such as Contract Net have proven their value...
This paper presents gradienTv, a distributed, market-based approach to live streaming. In gradienTv, multiple streaming trees are constructed using a market-based approach, such th...
Amir H. Payberah, Jim Dowling, Fatemeh Rahimian, S...
Collocation methods are a well developed approach for the numerical solution of smooth and weakly-singular Volterra integral equations. In this paper we extend these methods, thro...