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...
This paper presents a Boolean based symbolic model checking algorithm for the verification of analog/mixedsignal (AMS) circuits. The systems are modeled in VHDL-AMS, a hardware des...
David Walter, Scott Little, Nicholas Seegmiller, C...
In this paper, we introduce FPS, a mechanism to define performance measures for stochastic process algebra models. FPS is a functional performance specification language which desc...
This paper presents our Recurrent Control Neural Network (RCNN), which is a model-based approach for a data-efficient modelling and control of reinforcement learning problems in di...
This paper presents an action selection framework based on an assemblage of self-organizing neural networks called Cooperative Extended Kohonen Maps. This framework encapsulates t...
Kian Hsiang Low, Wee Kheng Leow, Marcelo H. Ang Jr...