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Shiqi Zhang, Piyush Khandelwal, and Peter Stone. Dynamically Constructed (PO)MDPs for Adaptive Robot Planning. In IJCAI'16 Workshop on Autonomous Mobile Service Robots, July 2016.
To operate in human-robot coexisting environments, intelligent robots need to simultaneously reason with commonsense knowledge and plan under uncertainty. Markov decision processes (MDPS) and partially observable MDPs (POMDPs), are good at planning under uncertainty toward maximizing long-term rewards; P-LOG, a declarative programming language under Answer Set semantics, is strong in commonsense reasoning. In this paper, we present a novel algorithm called DCPARP to dynamically represent, reason about, and construct (PO)MDPs using P-LOG. DCPARP successfully shields exogenous domain attributes from (PO)MDPs so as to limit computational complexity, but still enables (PO)MDPs to adapt to the value changes these attributes produce. We conduct a large number of experimental trials using two example problems in simulation and demonstrate DCPARP on a real robot. Results show significant improvements compared to competitive baselines.
@InProceedings{WSR16-szhang1,
author = {Shiqi Zhang and Piyush Khandelwal and Peter Stone},
title = {Dynamically Constructed (PO)MDPs for Adaptive Robot Planning},
booktitle = {IJCAI'16 Workshop on Autonomous Mobile Service Robots},
location = {New York City, USA},
month = {July},
year = {2016},
abstract = {
To operate in human-robot coexisting environments, intelligent robots need
to simultaneously reason with commonsense knowledge and plan under
uncertainty. Markov decision processes (MDPS) and partially observable
MDPs (POMDPs), are good at planning under uncertainty toward
maximizing long-term rewards; P-LOG, a declarative programming language
under Answer Set semantics, is strong in commonsense reasoning. In
this paper, we present a novel algorithm called DCPARP to dynamically
represent, reason about, and construct (PO)MDPs using P-LOG.
DCPARP successfully shields exogenous domain attributes from (PO)MDPs
so as to limit computational complexity, but still enables (PO)MDPs to
adapt to the value changes these attributes produce. We conduct a large
number of experimental trials using two example problems in simulation and
demonstrate DCPARP on a real robot. Results show significant improvements
compared to competitive baselines.
},
}
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