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Domain-independent deceptive planning

  • Adrian Price
  • , Ramon Fraga Pereira
  • , Peta Masters
  • , Mor Vered

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

Abstract

We investigate deceptive planning, the problem of generating a plan such that an observer is unable to determine its ultimate goal. Most work in this area has focused on path and/or motion planning. However planning problems can be quite varied and challenging. We present domain-independent approaches for deceptive plan generation utilising the concepts of landmarks, centroids, and minimum covering states. We introduce new, domain-independent metrics to evaluate a plan's deceptivity as a ratio between its deceptive quantity and cost; and we extensively evaluate the performance of our proposed approaches over widely different planning domains providing guidelines as to when to use each approach.

Original languageEnglish
Title of host publicationProceedings of the International Joint Conference on Autonomous Agents and Multiagent Systems
EditorsAlessandro Ricci, William Yeoh
Place of PublicationNew York NY USA
PublisherAssociation for Computing Machinery (ACM)
Pages95-103
Number of pages9
ISBN (Electronic)9781450394321
Publication statusPublished - 2023
EventInternational Conference on Autonomous Agents and Multiagent Systems 2023 - London, United Kingdom
Duration: 29 May 20232 Jun 2023
Conference number: 22nd
https://dl.acm.org/doi/proceedings/10.5555/3545946 (Proceedings)
https://aamas2023.soton.ac.uk/ (Website)

Conference

ConferenceInternational Conference on Autonomous Agents and Multiagent Systems 2023
Abbreviated titleAAMAS 2023
Country/TerritoryUnited Kingdom
CityLondon
Period29/05/232/06/23
Internet address

Keywords

  • Deception
  • Domain-Independent
  • Plan Recognition
  • Planning

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