Approximate Linear Programming (ALP) is a reinforcement learning technique with nice theoretical properties, but it often performs poorly in practice. We identify some reasons for...
Functional logic programming and probabilistic programming have demonstrated the broad benefits of combining laziness (non-strict evaluation with sharing of the results) with non-...
Sebastian Fischer, Oleg Kiselyov, Chung-chieh Shan
Quantifier reasoning in Satisfiability Modulo Theories (SMT) is a long-standing challenge. The practical method employed in modern SMT solvers is to instantiate quantified formulas...
Listening to music on personal, digital devices while mobile is an enjoyable, everyday activity. We explore a scheme for exploiting this practice to immerse listeners in navigatio...
Nigel Warren, Matt Jones, Steve Jones, David Bainb...
Probability distributions are useful for expressing the meanings of probabilistic languages, which support formal modeling of and reasoning about uncertainty. Probability distribu...