Sequential plan recognition

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

8 Scopus citations

Abstract

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 maximal information gain and maximum likelihood. These approaches were evaluated on a domain from the literature using a well known plan recognition algorithm. The results showed that the information gain approach was able to find the correct plan using significantly fewer queries than the maximum likelihood approach as well as a baseline approach choosing random plans. Our technique can inform the design of future plan recognition systems that interleave the recognition process with intelligent interventions of their users.

Original languageEnglish
Title of host publicationAAMAS 2016 - Proceedings of the 2016 International Conference on Autonomous Agents and Multiagent Systems
PublisherInternational Foundation for Autonomous Agents and Multiagent Systems (IFAAMAS)
Pages1347-1348
Number of pages2
ISBN (Electronic)9781450342391
StatePublished - 1 Jan 2016
Event15th International Conference on Autonomous Agents and Multiagent Systems, AAMAS 2016 - Singapore, Singapore
Duration: 9 May 201613 May 2016

Publication series

NameProceedings of the International Joint Conference on Autonomous Agents and Multiagent Systems, AAMAS
ISSN (Print)1548-8403
ISSN (Electronic)1558-2914

Conference

Conference15th International Conference on Autonomous Agents and Multiagent Systems, AAMAS 2016
Country/TerritorySingapore
CitySingapore
Period9/05/1613/05/16

Keywords

  • Activity Recognition
  • Human-Aware AI
  • Plan recognition

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