We present new algorithms for reinforcement learning, and prove that they have polynomial bounds on the resources required to achieve near-optimal return in general Markov decisio...
We present a novel method for state minimization of incompletely-specified finite state machines. Where classic methods simply minimize the number of states, ours directly addre...
— Closed-loop, asymptotically stable walking motions are designed for a 5-link, planar bipedal robot model with one degree of underactuation. Parameter optimization is applied to...
In this paper we present arithmetic real-coded variation operators tailored for time slot and turn optimization on TDMA-scheduled resources with evolutionary algorithms. Our opera...
In this paper, we study the optimal software release problem considering cost, reliability and testing eficiency. We first propose a generalized logistic testing-effort function t...