Domain-Independent Deceptive Planning

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

Research output: Contribution to journalConference articlepeer-review

5 Scopus citations

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
Pages (from-to)95-103
Number of pages9
JournalProceedings of the International Joint Conference on Autonomous Agents and Multiagent Systems, AAMAS
Volume2023-May
StatePublished - 2023
Externally publishedYes
Event22nd International Conference on Autonomous Agents and Multiagent Systems, AAMAS 2023 - London, United Kingdom
Duration: 29 May 20232 Jun 2023

Bibliographical note

Publisher Copyright:
© 2023 International Foundation for Autonomous Agents and Multiagent Systems (www.ifaamas.org). All rights reserved.

Funding

Peta Masters’ research is supported by the UKRI Trustworthy Autonomous Systems (TAS) Hub (EP/V00784X/1).

FundersFunder number
UKRI Trustworthy Autonomous Systems
Tennessee Academy of ScienceEP/V00784X/1

    Keywords

    • Deception
    • Domain-Independent
    • Plan Recognition
    • Planning

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