Automatically constructing novel representations of tasks from analysis of state spaces is a longstanding fundamental challenge in AI. I review recent progress on this problem for...
Abstract. Starting from a biochemical signalling pathway model expressed in a process algebra enriched with quantitative information we automatically derive both continuous-space a...
Muffy Calder, Adam Duguid, Stephen Gilmore, Jane H...
Rich representations in reinforcement learning have been studied for the purpose of enabling generalization and making learning feasible in large state spaces. We introduce Object...
Discrete-event dynamic systems with feedback, where the behavior of the system depends on the system state, are difficult to model due to the uncertainties and dependencies of sys...
This paper addresses the synthesis approach to output feedback robust model predictive control for systems with polytopic description, bounded state disturbance and measurement no...
BaoCang Ding, YuGeng Xi, Marcin T. Cychowski, Thom...