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@PhdThesis{dorit-phd, 
author = {Dorit Avrahami-Zilberbrand}, 
title = {Efficient Hybrid Algorithms for Plan Recognition and Detection of Suspicious and Anomalous Behavior }, 
school = {{B}ar {I}lan {U}niversity}, 
year = {2009}, 
OPTkey = {}, 
OPTtype = {}, 
OPTaddress = {}, 
OPTmonth = {}, 
 OPTnote = {}, 
  abstract = { 
Plan recognition is the process of inferring other agents' plans 
and goals based on their observable actions. Modern applications 
of plan recognition, in particular in surveillance and security 
raise several challenges. First, a number of key capabilities are 
missing from all but a handful of plan recognizers: (a) handling 
complex multi-featured observations; (b) dealing with plan  
execution duration constraints; (c) handling lossy observations 
(where an observation is intermittently lost); and (d) handling 
interleaved plans. Second, essentially all previous work in plan 
recognition has focused on recognition accuracy itself, with no 
regard to the use of the information in the recognizing agent. As 
a result, low-likelihood recognition hypotheses that may imply 
significant meaning to the observer, are ignored in existing work. 
In this work we present set of efficient plan recognition 
algorithms that are capable of handling the variety of features 
required of realistic recognition tasks. We also present novel 
efficient algorithms that allow the observer to incorporate her 
own biases and preferences---in the form of a utility 
function---into the plan recognition process. This allows choosing 
recognition hypotheses based on their expected utility to the 
observer. We call this Utility-based Plan Recognition (UPR). We 
demonstrate the efficacy of the techniques described above, by 
applying them to the problem of detecting anomalous and suspicious 
behavior. The system contains the symbolic plan recognition  
algorithm, which detects anomalous behavior, and the utility-based 
plan recognizer which reasons about the expected cost of 
hypotheses. These two components form a highly efficient hybrid 
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 = {}, 
OPTannote = {} 
}