Goal recognition design for non-optimal agents

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Abstract

Goal recognition design involves the offline analysis of goal recognition models by formulating measures that assess the ability to perform goal recognition within a model and finding efficient ways to compute and optimize them. In this work we present goal recognition design for non-optimal agents, which extends previous work by accounting for agents that behave non-optimally either intentionally or naively. The analysis we present includes a new generalized model for goal recognition design and the worst case distinctiveness (wed) measure. For two special cases of sub-optimal agents we present methods for calculating the wed, part of which are based on novel compilations to classical planning problems. Our empirical evaluation shows the proposed solutions to be effective in computing and optimizing the wed.

Original languageEnglish
Title of host publicationProceedings of the 29th AAAI Conference on Artificial Intelligence, AAAI 2015 and the 27th Innovative Applications of Artificial Intelligence Conference, IAAI 2015
Pages3298-3304
Number of pages7
ISBN (Electronic)9781577357032
StatePublished - 1 Jun 2015
Event29th AAAI Conference on Artificial Intelligence, AAAI 2015 and the 27th Innovative Applications of Artificial Intelligence Conference, IAAI 2015 - Austin, United States
Duration: 25 Jan 201530 Jan 2015

Publication series

NameProceedings of the National Conference on Artificial Intelligence
Volume5

Conference

Conference29th AAAI Conference on Artificial Intelligence, AAAI 2015 and the 27th Innovative Applications of Artificial Intelligence Conference, IAAI 2015
Country/TerritoryUnited States
CityAustin
Period25/01/1530/01/15

ASJC Scopus subject areas

  • Software
  • Artificial Intelligence

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