— Most state-based approaches to fault diagnosis of discrete-event systems require a complete and accurate model of the system to be diagnosed. In this paper, we address the prob...
Abstract. This paper shows that we can take advantage of information about the probabilities of the occurrences of events, when this information is available, to refine the classic...
Reinforcement Learning (RL) is analyzed here as a tool for control system optimization. State and action spaces are assumed to be continuous. Time is assumed to be discrete, yet th...
Models of agent-environment interaction that use predictive state representations (PSRs) have mainly focused on the case of discrete observations and actions. The theory of discre...
Brain machine interfaces work by mapping the relevant neural activity to the intended movement known as ‘decoding’. Here, we develop a recursive Bayesian decoder for goaldirec...
Maryam Modir Shanechi, Gregory W. Wornell, Ziv Wil...