Gal A. Kaminka's Publications

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Keyhole Adversarial Plan Recognition for Recognition of Suspicious and Anomalous Behavior

Dorit Avrahami-Zilberbrand and Gal A. Kaminka. Keyhole Adversarial Plan Recognition for Recognition of Suspicious and Anomalous Behavior. In Gita Sukthankar, Robert P. Goldman, Christopher Geib, David V. Pynadath, and Hung Bui, editors, Plan, Activity, and Intent Recognition, pp. 87–121, Morgan Kaufmann, 2014.

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Abstract

A key application of plan recognition is to adversarial settings. There are two approaches to adversarial plan recognition. The first is suspicious activity recognition, i.e., directly recognizing plans, activities and behaviors that are known to be suspect (e.g., carrying a suitcase, then leaving it behind in a dense area). The second is anomalous activity recognition, in which we indirectly recognize suspect behavior, by first ruling out normal, non-suspect, behaviors as explanations for the observations. Different challenges are raised in pursuing these two approaches. In this work we present set of efficient plan recognition algorithms that are capable of handling the variety of challenges required of realistic adversarial plan recognition tasks. We describe an efficient hybrid adversarial plan recognition system that is composed of two processes: a plan recognizer capable of efficiently detecting anomalous behavior, and a utility-based plan-recognizer (UPR) incorporating the observer's own biases---in the form of a utility function---into the plan recognition process. This allows choosing recognition hypotheses based on their expected cost to the observer. These two components form a highly efficient adversarial plan recognizer capable of recognizing abnormal and potentially dangerous activities. We evaluate the system with extensive experiments, using real-world and simulated activity data, from a variety of sources.

Additional Information

BibTeX

@InCollection{dorit-pairbook,
author = {Dorit Avrahami-Zilberbrand and Gal A. Kaminka},
title = {Keyhole Adversarial Plan Recognition for Recognition of Suspicious and Anomalous Behavior},
booktitle = {Plan, Activity, and Intent Recognition},
OPTcrossref = {},
OPTkey = {},
pages = {87--121},
year = {2014},
editor = {Gita Sukthankar and Robert P. Goldman and Christopher Geib and David V. Pynadath and Hung Bui},
OPTvolume = {},
OPTnumber = {},
OPTseries = {},
OPTaddress = {},
OPTmonth = {},
OPTorganization = {},
publisher = {Morgan Kaufmann},
OPTnote = {In press},
OPTannote = {},
OPTurl = {},
OPTdoi = {},
OPTisbn = {978-0123985323},
OPTlocalfile = {},
abstract = {A key application of plan recognition is to adversarial settings. 
There are two approaches to adversarial plan recognition. 
The first is \emph{suspicious activity recognition}, i.e., directly 
recognizing plans, activities and behaviors that are known to be 
suspect (e.g., carrying a suitcase, then leaving it behind in a 
dense area).  The second is \emph{anomalous activity recognition}, 
in which we \textit{indirectly} recognize suspect behavior, by first ruling out 
normal, non-suspect, behaviors as explanations for the observations. 
Different challenges are raised in pursuing these two approaches. 
In this work we present set of efficient plan recognition 
algorithms that are capable of handling the variety of challenges 
required of realistic adversarial plan recognition tasks. We 
describe an efficient hybrid adversarial plan recognition system 
that is composed of two processes: a  
plan recognizer capable of efficiently detecting anomalous 
behavior, and a utility-based plan-recognizer (UPR) 
 incorporating the observer's  
own biases---in the form of a utility 
function---into the plan recognition process. This allows choosing 
recognition hypotheses based on their expected cost to the 
observer. These two components form a highly efficient adversarial 
plan recognizer capable of recognizing abnormal and potentially 
dangerous activities. We evaluate the system with extensive 
experiments, using real-world and simulated activity data, from a 
variety of sources.},
  wwwnote = { }, 
}

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