@conference {1509745,
	title = {Practical POMDPs for Personal Assistant Domains },
	booktitle = {AAAI Spring Symposium},
	year = {2005},
	abstract = {Agents or agent teams deployed to assist humans often face the challenge of monitoring state of key processes
in their environment, including the state of their human
users, and making periodic decisions based on such monitoring. The challenge is particularly difficult given the
significant observational uncertainty, and uncertainty in
the outcome of agent{\textquoteright}s actions. POMDPs (partially observable markov decision problems) appear well-suited to
enable agents to address such uncertainties and costs; yet
slow run-times in generating optimal POMDP policies
presents a significant hurdle. This slowness can be attributed to cautious planning for all possible belief states,
e.g., the uncertainty in the monitored process is assumed
to range over all possible states at all times.
This paper introduces three key techniques to speedup
POMDP policy generation that exploit the notion of
progress or dynamics in personal assistant domains. The
key insight is that given an initial (possibly uncertain)
starting set of states, the agent needs to be prepared to
act only in a limited range of belief states; most other
belief states are simply unreachable given the dynamics
of the monitored process, and no policy needs to be generated for such belief states. The techniques we propose
are complementary to most existing exact and approximate POMDP policy generation algorithms. Indeed, we
illustrate our technique by enhancing generalized incremental pruning (GIP), one of the most efficient exact
algorithms for POMDP policy generation and illustrate
orders of magnitude speedup in policy generation. Such
speedup would facilitate agents{\textquoteright} deploying POMDPs in
assisting human users.},
	author = {Varakantham, Pradeep and Rajiv T. Maheswaran and Tambe, Milind}
}
