| Planning under uncertainty is an increasingly important research field, and it is clear that the design of robust and scalable algorithms which consider uncertainty is key to the development of effective autonomous and semi-autonomous systems. Partially Observable Markov Decision Processes (POMDPs) offer a powerful mathematical framework for making optimal action choices in noisy and/or uncertain environments. However, integration of the POMDP model with real world applications has been slow due to the high computation cost of exact approaches to POMDP planning.;In recent years, point-based POMDP solvers have emerged as efficient methods for providing approximate solutions by planning over a small subset of the belief space. This thesis first provides a survey on many of the proposed point-based POMDP solvers. We then conduct an empirical analysis on the key components of point-based methods, the belief collection and belief updating processes. This is an important contribution, as previous publications on point-based methods have only compared full algorithms, without comparing the underlying processes. As well, we verify the effect of a variety of parameters and optimizations that could be used within a point-based solver. Experiments are conducted on a variety of POMDP environments. |
