Gal A. Kaminka: Publications

Sorted by DateClassified by Publication TypeClassified by TopicGrouped by Student (current)Grouped by Former Students

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.

Download

(unavailable)

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., directlyrecognizing plans, activities and behaviors that are known to besuspect (e.g., carrying a suitcase, then leaving it behind in adense area). The second is anomalous activity recognition,in which we indirectly recognize suspect behavior, by first ruling outnormal, 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 recognitionalgorithms that are capable of handling the variety of challengesrequired of realistic adversarial plan recognition tasks. Wedescribe an efficient hybrid adversarial plan recognition systemthat is composed of two processes: aplan recognizer capable of efficiently detecting anomalousbehavior, and a utility-based plan-recognizer (UPR) incorporating the observer'sown biases---in the form of a utilityfunction---into the plan recognition process. This allows choosingrecognition hypotheses based on their expected cost to theobserver. These two components form a highly efficient adversarialplan recognizer capable of recognizing abnormal and potentiallydangerous activities. We evaluate the system with extensiveexperiments, using real-world and simulated activity data, from avariety 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 = { },
}

Generated by bib2html.pl (written by Patrick Riley ) on Mon Nov 16, 2020 22:25:46