Reinforcement learning is a paradigm under which an agent seeks to improve its policy by making learning updates based on the experiences it gathers through interaction with the en...
We describe two Go programs, ¢¡¤£¦¥ and ¢¡¤§¨£ , developed by a Monte-Carlo approach that is simpler than Bruegmann’s (1993) approach. Our method is based on Abra...
Future robotic planetary exploration will need to traverse geographically diverse and challenging terrain. Cliffs, ravines, and fissures are of great scientific interest because th...
Erik Mumm, Shane Farritor, Paolo Pirjanian, Chris ...
We study stochastic models to mitigate the risk of poor Quality-of-Service (QoS) in computational markets. Consumers who purchase services expect both price and performance guaran...
In an attempt to cope with time-varying workload, traditional adaptive Time Warp protocols are designed to react in response to performance changes by altering control parameter c...