@article{iglesias11IDA,
  author  = {Jose Antonio Iglesias and Plamen Angelov and Agapito Ledezma and Araceli Sanchis},
  title = {A Plan Classifier Based on Chi-Square Distribution Tests},
  journal = {Intelligent Data Analysis},
  year = {2011},
  volume = {15}, 
  issue = {2},
  pages = {131-149},
  abstract = {Recognizing the behavior of others is a significant aspect of many different human tasks in different environments. To make good decisions in a social context, humans often need to recognize the plan underlying the behavior of others, and make predictions based on this recognition. This process, when carried out by software agents or robots, is known as plan recognition, or agent modeling. Most existing techniques for plan recognition assume the availability of carefully hand-crafted plan libraries, which encode the a-priori known behavioral repertoire of the observed agents; during run-time, plan recognition algorithms match the observed behavior of the agents against the plan-libraries, and matches are reported as hypotheses. Unfortunately, techniques for automatically acquiring plan-libraries from observations, e.g., by learning or data-mining, are only beginning to emerge.},
  issn = {1088-467X}
}

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