Markov Decision Processes (MDPs), currently a popular method for modeling and solving decision theoretic planning problems, are limited by the Markovian assumption: rewards and dy...
Relational world models that can be learned from experience in stochastic domains have received significant attention recently. However, efficient planning using these models rema...
Work allocation planning is a vital and notoriously difficult task in areas characterised by large work forces, contiguous and sometimes overlapping shifts, regulatory and corpora...
— The DPC algorithm developed in our previous work is an efficient way of computing optimal trajectories for multiple robots in a distributed fashion with timeparameterized cons...
— Sampling-based motion planners are often used to solve very high-dimensional planning problems. Many recent algorithms use projections of the state space to estimate properties...