Decision-Theoretic Planning with Concurrent Temporally Extended Actions
  We investigate a model for planning under uncertainty with temporallyextended
actions, where multiple actions can be taken concurrently at each decision
epoch. Our model is based on the options framework, and combines it with
factored state space models,where the set of options can be partitioned into
classes that affectdisjoint state variables. We show that the set of
decisionepochs for concurrent options defines a semi-Markov decisionprocess, if
the underlying temporally extended actions being parallelized arerestricted to
Markov options. This property allows us to use SMDPalgorithms for computing the
value function over concurrentoptions. The concurrent options model allows
overlapping execution ofoptions in order to achieve higher performance or in
order to performa complex task. We describe a simple experiment using a
navigationtask which illustrates how concurrent options results in a faster
planwhen compared to the case when only one option is taken at a time.
