Discovering underlying plans based on distributed representations of actions

Xin Tian, Hankz Hankui Zhuo, Subbarao Kambhampati

Research output: Chapter in Book/Report/Conference proceedingConference contribution

8 Scopus citations

Abstract

Plan recognition aims to discover target plans (i.e., sequences of actions) behind observed actions, with history plan libraries or domain models in hand. Previous approaches either discover plans by maximally "matching" observed actions to plan libraries, assuming target plans are from plan libraries, or infer plans by executing domain models to best explain the observed actions, assuming complete domain models are available. In real world applications, however, target plans are often not from plan libraries and complete domain models are often not available, since building complete sets of plans and complete domain models are often difficult or expensive. In this paper we view plan libraries as corpora and learn vector representations of actions using the corpora; we then discover target plans based on the vector representations. Our approach is capable of discovering underlying plans that are not from plan libraries, without requiring domain models provided. We empirically demonstrate the effectiveness of our approach by comparing its performance to traditional plan recognition approaches in three planning domains.

Original languageEnglish (US)
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)
Pages1135-1143
Number of pages9
ISBN (Electronic)9781450342391
StatePublished - 2016
Event15th International Conference on Autonomous Agents and Multiagent Systems, AAMAS 2016 - Singapore, Singapore
Duration: May 9 2016May 13 2016

Other

Other15th International Conference on Autonomous Agents and Multiagent Systems, AAMAS 2016
CountrySingapore
CitySingapore
Period5/9/165/13/16

ASJC Scopus subject areas

  • Artificial Intelligence
  • Software
  • Control and Systems Engineering

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  • Cite this

    Tian, X., Zhuo, H. H., & Kambhampati, S. (2016). Discovering underlying plans based on distributed representations of actions. In AAMAS 2016 - Proceedings of the 2016 International Conference on Autonomous Agents and Multiagent Systems (pp. 1135-1143). International Foundation for Autonomous Agents and Multiagent Systems (IFAAMAS).