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CORPP: Commonsense Reasoning and Probabilistic Planning, as Applied to Dialog with a Mobile Robot.
Shiqi
Zhang and Peter Stone.
In Proceedings of the 29th Conference on Artificial
Intelligence (AAAI), January 2015.
[PDF]306.0kB [postscript]1.5MB
In order to be fully robust and responsive to a dynamically changing real-world environment, intelligent robots will need to engage in a variety of simultaneous reasoning modalities. In particular, in this paper we consider their needs to i) reason with commonsense knowledge, ii) model their nondeterministic action outcomes and partial observability, and iii) plan toward maximizing long-term rewards. On one hand, Answer Set Programming (ASP) is good at representing and reasoning with commonsense and default knowledge, but is ill-equipped to plan under probabilistic uncertainty. On the other hand, Partially Observable Markov Decision Processes (POMDPs) are strong at planning under uncertainty toward maximizing long-term rewards, but are not designed to incorporate commonsense knowledge and inference. This paper introduces the CORPP algorithm which combines P-log, a probabilistic extension of ASP, with POMDPs to integrate commonsense reasoning with planning under uncertainty. Our approach is fully implemented and tested on a shopping request identification problem both in simulation and on a real robot. Compared with existing approaches using P-log or POMDPs individually, we observe significant improvements in both efficiency and accuracy.
@InProceedings{AAAI15-szhang,
author = {Shiqi Zhang and Peter Stone},
title = {CORPP: Commonsense Reasoning and Probabilistic Planning, as Applied
to Dialog with a Mobile Robot},
booktitle = {Proceedings of the 29th Conference on Artificial Intelligence (AAAI)},
location = {Austin, Texas},
month = {January},
year = {2015},
abstract = {
In order to be fully robust and responsive to a dynamically changing
real-world environment, intelligent robots will need to engage in a
variety of simultaneous reasoning modalities. In particular, in this paper
we consider their needs to i) reason with commonsense knowledge, ii) model
their nondeterministic action outcomes and partial observability, and iii)
plan toward maximizing long-term rewards. On one hand, Answer Set
Programming (ASP) is good at representing and reasoning with commonsense
and default knowledge, but is ill-equipped to plan under probabilistic
uncertainty. On the other hand, Partially Observable Markov Decision
Processes (POMDPs) are strong at planning under uncertainty toward
maximizing long-term rewards, but are not designed to incorporate
commonsense knowledge and inference. This paper introduces the CORPP
algorithm which combines P-log, a probabilistic extension of ASP, with
POMDPs to integrate commonsense reasoning with planning under uncertainty.
Our approach is fully implemented and tested on a shopping request
identification problem both in simulation and on a real robot. Compared
with existing approaches using P-log or POMDPs individually, we observe
significant improvements in both efficiency and accuracy.
},
}
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