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@article{frontiers22shify,
 author = {Shify Treger and Gal A. Kaminka},
 title = {Towards Computational Modeling of Human Goal Recognition},
 journal = {Frontiers in Artificial Intelligence},
 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 (\emph{Berkovitz, The effect of spatial cognition and context on robot movement 
             legibility in human-robot collaboration, 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 hypothesize that humans utilize plan-recognition in service of goal recognition, i.e., match observations to known plans, and use the  
    set of recognized plans to conclude as to the likely goals. To test this hypothesis, a second contribution in this paper is the  
    introduction of a novel offline recognition algorithm. While preliminary, the algorithm accounts for the results reported by Berkovitz  
    significantly better than the existing algorithms.  Moreover, the proposed algorithm marries rationality-based and plan-library based methods seamlessly.
 },
 volume = {4},
 year = {2022},
 URL={https://www.frontiersin.org/article/10.3389/frai.2021.737327},       
 DOI={10.3389/frai.2021.737327},      
 ISSN={2624-8212},   
}