The reward functions that drive reinforcement learning systems are generally derived directly from the descriptions of the problems that the systems are being used to solve. In so...
Dependency networks are a compelling alternative to Bayesian networks for learning joint probability distributions from data and using them to compute probabilities. A dependency ...
Planning agents often lack the computational resources needed to build full planning trees for their environments. Agent designers commonly overcome this finite-horizon approxima...
Jonathan Sorg, Satinder P. Singh, Richard L. Lewis
—Multiobjective optimization problems have been widely addressed using evolutionary computation techniques. However, when dealing with more than three conflicting objectives (th...
Mario Garza-Fabre, Gregorio Toscano Pulido, Carlos...
—Evolutionary algorithms have been very popular optimization methods for a wide variety of applications. However, in spite of their advantages, their computational cost is still ...
Mohsen Davarynejad, Jafar Rezaei, Jos L. M. Vranck...