Sequential plan recognition Conference Paper uri icon


  • Plan recognition algorithms need to maintain all candidate hypotheses which are consistent with the observations, even though there is only a single hypothesis that is the correct one. Unfortunately, the number of possible hypotheses can be exponentially large in practice. This paper addresses the problem of how to disambiguate between many possible hypotheses that are all consistent with the actions of the observed agent. One way to reduce the number of hypotheses is to consult a domain expert or the acting agent directly about its intentions. This process can be performed sequentially, updating the set of hypotheses during the recognition process. The paper specifically addresses the problem of how to minimize the number of queries made that are required to find the correct hypothesis. It adapts a number of probing techniques for choosing which plan to query, such as …

publication date

  • May 9, 2016