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@mastersthesis{shify-msc,
  author = {Shify Treger}, 
  title = { Towards Computational Modeling of Human Goal Recognition},
  school = {{B}ar {I}lan {U}niversity},
  year = {2022},
  OPTkey = {},
  OPTtype = {},
  OPTaddress = {},
  OPTmonth = {},
  OPTnote = {Available at \url{http://www.cs.biu.ac.il/~galk/Publications/b2hd-shify-msc.html}},
  OPTannote = {},
  wwwnote = {}, 
  abstract = {
  	Recently, we are seeing the emergence of plan- and goal-recognition algorithms which are based on the principle of \textit{rationality}. 
    These avoid the use of a plan library that compactly encodes all possible observable plans, and instead generate plans dynamically to 
    match the observations. However, recent experiments by Berkovitz (2018) show that in many cases, humans seem to have reached quick (correct) decisions when 
    observing motions which were far from rational (optimal), while optimal motions were slower to be recognized.  Intrigued by these 
    findings, we experimented with a variety of rationality-based recognition algorithms on the same data. 
    The results clearly show that none of the algorithms reported in the literature accounts for human subject decisions, even in 
    this simple task.  This is our first contribution.   We then investigate the novel algorithm on a different domain of shape recognition 
    based on a work by Vered \textit{et al} (2016). They contrasted human behavior in recognizing hand-drawn shapes with an algorithm based on 
    the rationality assumption. Here we show that our proposed novel algorithm accounts for human results from the shape recognition domain 
    slightly better than the existing algorithms. },
}