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@COMMENT This file came from Gal A. Kaminka's publication pages at
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@InProceedings{aamas08noa,
  author = 	 {Noa Agmon and Vladimir Sadov and Gal A. Kaminka and Sarit Kraus},
  title = 	 {The Impact of Adversarial Knowledge on Adversarial Planning in Perimeter Patrol},
  OPTcrossref =  {},
  OPTkey = 	 {},
  booktitle = AAMAS-08,
  volume = {1},
  pages = 	 {55--62},
  year = 	 {2008},
  abstract = {This paper considers the problem of multi-robot patrolling around a closed area,
  in the presence of an adversary trying to penetrate the area. Previous work on planning in
  similar adversarial environments addressed worst-case settings, in which the adversary has full
  knowledge of the defending robots.
 It was shown that non deterministic algorithms may be effectively used to maximize the chances of
 blocking such a full-knowledge opponent, and such algorithms guarantee a ``lower bound'' to the
 performance of the team. However, an open question remains as to the impact of the knowledge of the
 opponent on the performance of the robots. This paper explores this question
 in depth and provides theoretical results, supported by extensive experiments with 68 human subjects
 concerning the compatibility of algorithms to the extent of information possessed by the subjects.
 First, we analytically examine the case of a zero-knowledge opponent - a different extreme - and show
 that surprisingly, this seemingly best-case scenario (from the point of view of defending robots) is
 optimally addressed by a deterministic, non-randomizing patrol. Moreover, we show empirically that an optimal
 algorithm for the full-knowledge opponent fails miserably in this case. We then address the case in
 which the adversary gained partial information, propose the combine algorithm that maximizes the
 expected probability of penetration detection along with minimizing the deviation between the
 probabilities of penetration detection along the perimeter, and support the performance of this
 algorithm in the experiments.},
  wwwnote = {},
  OPTeditor = 	 {},
  OPTvolume = 	 {},
  OPTnumber = 	 {},
  OPTseries = 	 {},
  OPTaddress = 	 {},
  OPTmonth = 	 {},
  OPTorganization = {},
  OPTpublisher = {},
  OPTannote = 	 {}
}