We investigate dynamical models of human motion that can
support both synthesis and analysis tasks. Unlike coarser
discriminative models that work well when action classes are ...
While traditional mechanism design typically assumes isomorphism between the agents’ type- and action spaces, in many situations the agents face strict restrictions on their act...
We target the problem of closed-loop learning of control policies that map visual percepts to continuous actions. Our algorithm, called Reinforcement Learning of Joint Classes (RLJ...
In functional programming, monadic characterizations of computational effects are normally understood denotationally: they describe how an effectful program can be systematically ...
A modular synthesis flow is essential for a scalable and hierarchical design methodology. This paper considers a particular modular flow where each module has interface methods an...