Estimation of parameters of random field models from labeled training data is crucial for their good performance in many image analysis applications. In this paper, we present an approach for ap- proximate maximum likelihood parameter learning in discriminative field models, which is based on...

Much recent work in reinforcement learning and stochastic optimal control has focused on algorithms that search directly through a space of policies rather than building approximate value functions. Policy search has numerous advantages: it does not rely on the Markov assumption, domain...

The present work examines whether user's trust of and reliance on automation, were affected by the manipulations of user's perception of the responding agent. These manipulations included agent reliability, agent type, and failure salience. Previous work has shown that automation is not...