Plan recognition design

Reuth Mirsky, Roni Stern, Ya'akov Gal, Meir Kalech

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

1 Scopus citations


Goal Recognition Design (GRD) is the problem of designing a domain in a way that will allow easy identification of agents' goals. This paper extends the original GRD problem to domains that are described using hierarchical plans (GRD- PL), and defines the Plan Recognition Design (PRD) problem which is the task of designing a domain using plan libraries in order to facilitate fast identification of an agent's plan. While GRD can help to explain faster which goal the agent is trying to achieve, PRD can help in faster understanding of how the agent is going to achieve its goal. Building on the GRD paradigm, we define for each of these two new problems (GRD-PL and PRD) a measure that quantifies the worst-case distinctiveness of a given planning domain. Then, we study the relation between these measures, showing that the worst case distinctiveness of GRD-PL is a lower bound to the worst case plan distinctiveness of PRD, and that they are equal under certain conditions. Methods for computing each of these measures are presented, and we evaluate these methods in three known hierarchical planning domains from the literature. Results show that in many cases, solving the simpler problem of GRD-PL provides an optimal solution for the PRD problem as well.

Original languageEnglish
Title of host publicationWS-17-01
Subtitle of host publicationArtificial Intelligence and Operations Research for Social Good; WS-17-02: Artificial Intelligence, Ethics, and Society; WS-17-03: Artificial Intelligence for Connected and Automated Vehicles; WS-17-04: Artificial Intelligence for Cyber Security; WS-17-05: Artificial Intelligence for Smart Grids and Buildings; WS-17-06: Computer Poker and Imperfect Information Games; WS-17-07: Crowdsourcing, Deep Learning and Artificial Intelligence Agents; WS-17-08: Distributed Machine Learning; WS-17-09: Joint Workshop on Health Intelligence; WS-17-10: Human-Aware Artificial Intelligence; WS-17-11: Human-Machine Collaborative Learning; WS-17-12: Knowledge-Based Techniques for Problem Solving and Reasoning; WS-17-13: Plan, Activity, and Intent Recognition; WS-17-14: Symbolic Inference and Optimization; WS-17-15: What's Next for AI in Games?
PublisherAI Access Foundation
Number of pages8
ISBN (Electronic)9781577357865
StatePublished - 2017
Externally publishedYes
Event31st AAAI Conference on Artificial Intelligence, AAAI 2017 - San Francisco, United States
Duration: 4 Feb 201710 Feb 2017

Publication series

NameAAAI Workshop - Technical Report
VolumeWS-17-01 - WS-17-15


Conference31st AAAI Conference on Artificial Intelligence, AAAI 2017
Country/TerritoryUnited States
CitySan Francisco

Bibliographical note

Publisher Copyright:
© 2017, Association for the Advancement of Artificial Intelligence ( All rights reserved.


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