In many real-world problems, the learning agent needs to learn a problem’s abstractions and solution simultaneously. However, most such abstractions need to be designed and refined by hand for different problems and domains of application. This paper …
Several goal-oriented problems in the real-worldcan be naturally expressed as Stochastic ShortestPath Problems (SSPs). However, the computa-tional complexity of solving SSPs makes findingsolutions to even moderately sized …
Much of the research on learning symbolic models of AIagents focuses on agents with stationary models. This as-sumption fails to hold in settings where the agent’s capa-bilities may change as a result of learning, …
In this talk, I present a poster on my collaborative interdisciplinary research on using first-order probabilistic logic for inferring properties of intergalactic space.
In the recent years, deep learning and feature learning have drawn significant attention in the field of Music Information Retrieval (MIR) research, inspired by good results in speech recognition and computer vision. Here, we tackle the problem of …