This paper studies the input design problem for system identification where time domain constraints have to be considered. A finite Markov chain is used to model the input of the s...
Abstract. We present a new reinforcement learning approach for deterministic continuous control problems in environments with unknown, arbitrary reward functions. The difficulty of...
To realize large scale socially embedded systems, this paper proposes a multiagent-based participatory design that consists of steps called 1) participatory simulation, where scen...
This papers presents a novel representation of classifier conditions based on convex hulls. A classifier condition is represented by a sets of points in the problem space. These p...
Although several researchers have integrated methods for reinforcement learning (RL) with case-based reasoning (CBR) to model continuous action spaces, existing integrations typic...