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Reuth Mirsky, Ran Galun, Yaakov (Kobi) Gal, and Gal A. Kaminka. Comparing
Plan Recognition Algorithms through Standard Libraries. In AAAI workshop on Plan-, Activity-, and Intent- Recognition
(PAIR), 2018.
Available also on AAAI's
PAIR workshop archives
Plan recognition is one of the fundamental problems of AI, applicable to many domains, from user interfaces to cyber security. We focus on a class of algorithms that use plan libraries as input to the recognition process. Despite the prevalence of these approaches, they lack a standard representation, and have not been compared to each other on common testbeds. This paper directly addresses this gap by providing a standard plan library representation and evaluation criteria to consider. Our representation is comprehensive enough to describe a variety of known plan recognition problems, yet it can be easily applied to existing algorithms, which can be evaluated using our defined criteria. We demonstrate this technique on two known algorithms, SBR and PHATT. We provide meaningful insights both about the differences and abilities of the algorithms. We show that SBR is superior to PHATT both in terms of computation time and space, but at the expense of functionality and compact representation. We also show that depth is the single feature of a plan library that increases the complexity of the recognition, regardless of the algorithm used.
@inproceedings{pair18reuth,
author = {Reuth Mirsky and Ran Galun and Yaakov (Kobi) Gal and Gal A. Kaminka},
title = {Comparing Plan Recognition Algorithms through Standard Libraries},
booktitle = {{AAAI} workshop on Plan-, Activity-, and Intent- Recognition ({PAIR})},
year = {2018},
abstract = {
Plan recognition is one of the fundamental problems of AI,
applicable to many domains, from user interfaces to cyber
security. We focus on a class of algorithms that use plan libraries
as input to the recognition process. Despite the prevalence of
these approaches, they lack a standard representation, and
have not been compared to each other on common
testbeds. This paper directly addresses this gap by providing
a standard plan library representation and evaluation criteria
to consider. Our representation is comprehensive enough to
describe a variety of known plan recognition problems, yet
it can be easily applied to existing algorithms, which can be
evaluated using our defined criteria.
We demonstrate this technique on two known algorithms,
SBR and PHATT. We provide meaningful insights both about
the differences and abilities of the algorithms. We show that
SBR is superior to PHATT both in terms of computation time
and space, but at the expense of functionality and compact representation.
We also show that depth is the single feature of a plan library that
increases the complexity of the recognition, regardless of the
algorithm used.
},
wwwnote = {Available also on <a href="https://www.aaai.org/ocs/index.php/WS/AAAIW18/paper/viewFile/16937/15630">AAAI's PAIR workshop archives</a>},
}
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