CRADLE: An online plan recognition algorithm for exploratory domains

Reuth Mirsky, Ya'akov Kobi Gal, Stuart M. Shieber

Research output: Contribution to journalArticlepeer-review

14 Scopus citations

Abstract

In exploratory domains, agents' behaviors include switching between activities, extraneous actions, and mistakes. Such settings are prevalent in real world applications such as interaction with open-ended software, collaborative office assistants, and integrated development environments. Despite the prevalence of such settings in the real world, there is scarce work in formalizing the connection between high-level goals and low-level behavior and inferring the former from the latter in these settings. We present a formal grammar for describing users' activities in such domains. We describe a new top-down plan recognition algorithm called CRADLE (Cumulative Recognition of Activities and Decreasing Load of Explanations) that uses this grammar to recognize agents' interactions in exploratory domains.We compare the performance of CRADLE with state-of-The-Art plan recognition algorithms in several experimental settings consisting of real and simulated data. Our results show that CRADLE was able to output plans exponentially more quickly than the state-of-The-Art without compromising its correctness, as determined by domain experts. Our approach can form the basis of future systems that use plan recognition to provide real-Time support to users in a growing class of interesting and challenging domains.

Original languageEnglish
Article number45
JournalACM Transactions on Intelligent Systems and Technology
Volume8
Issue number3
DOIs
StatePublished - 1 Apr 2017

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Artificial Intelligence

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