This paper summarizes research on a new emerging framework for learning to plan using the Markov decision process model (MDP). In this paradigm, two approaches to learning to plan...
Sridhar Mahadevan, Sarah Osentoski, Jeffrey Johns,...
Belief propagation (BP) is an effective algorithm for solving energy minimization problems in computer vision. However, it requires enormous memory, bandwidth, and computation beca...
Chao-Chung Cheng, Chia-Kai Liang, Homer H. Chen, L...
Agents often have to construct plans that obey resource limits for continuous resources whose consumption can only be characterized by probability distributions. While Markov Deci...
er provides new techniques for abstracting the state space of a Markov Decision Process (MDP). These techniques extend one of the recent minimization models, known as -reduction, ...
In this paper, we develop an acyclic network of queues for the design of a dynamic flow shop, where each service station represents a machine. The orders arrive at the input stati...
Amir Azaron, Hideki Katagiri, Kosuke Kato, Masatos...