In recent years there has been a flurry of works on learning Bayesian networks from data. One of the hard problems in this area is how to effectively learn the structure of a beli...
We present a design for an automated theorem prover that controls its search based on ideas from several areas of artificial intelligence (AI). The combination of case-based reaso...
Most research in learning for planning has concentrated on efficiency gains. Another important goal is improving the quality of final plans. Learning to improve plan quality has b...
The generation of the set of all ground actions for a given set of ADL operators, which are allowed to have conditional effects and preconditions that can be represented using arbi...
A classical problem of stochastic simulation is how to estimate the variance of the sample mean of dependent but stationary outputs. Many variance estimators, such as the batch me...