The distributed task allocation problem occurs in domains like web services, the grid, and other distributed systems. In this problem, the system consists of servers and mediators...
Reinforcement learning problems are commonly tackled with temporal difference methods, which use dynamic programming and statistical sampling to estimate the long-term value of ta...
Dependency networks are a compelling alternative to Bayesian networks for learning joint probability distributions from data and using them to compute probabilities. A dependency ...
Abstract. We present an implementation of model-based online reinforcement learning (RL) for continuous domains with deterministic transitions that is specifically designed to achi...
Traditional approaches for sequential logic optimization include (1) explicit state-based techniques such as state minimization, (2) structural techniques such as retiming, and (3...