  Reinforcement learning and probabilistic reasoning algorithms aim at learning
from interaction experiences and reasoning with probabilistic contextual
knowledge respectively. In this research, we develop algorithms for robot task
completions, while looking into the complementary strengths of reinforcement
learning and probabilistic reasoning techniques. The robots learn from
trial-and-error experiences to augment their declarative knowledge base, and
the augmented knowledge can be used for speeding up the learning process in
potentially different tasks. We have implemented and evaluated the developed
algorithms using mobile robots conducting dialog and navigation tasks. From the
results, we see that our robot's performance can be improved by both reasoning
with human knowledge and learning from task-completion experience. More
interestingly, the robot was able to learn from navigation tasks to improve its
dialog strategies.
