Metaphysics of planning domain descriptions

Siddharth Srivastava, Stuart Russell, Alessandro Pinto

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

1 Scopus citations


Domain models for sequential decision-making typically represent abstract versions of real-world systems. In practice, such abstract representations are compact, easy to maintain, and afford faster solution times. Unfortunately, as we show in the paper, simple ways of abstracting solvable real-world problems may lead to models whose solutions are incorrect with respect to the real-world problem. There is some evidence that such limitations have restricted the applicability of sequential decision-making technology in the real world, as is apparent in the case of task and motion planning in robotics. We show that the situation can be ameliorated by a combination of increased expressive power-for example, allowing angelic nondeterminism in action effects-and new kinds of algorithmic approaches designed to produce correct solutions from initially incorrect or non-Markovian abstract models.

Original languageEnglish (US)
Title of host publicationSequential Decision Making for Intelligent Agents - Papers from the AAAI 2015 Fall Symposium, Technical Report
PublisherAI Access Foundation
Number of pages8
ISBN (Electronic)9781577357520
StatePublished - 2015
Externally publishedYes
EventAAAI 2015 Fall Symposium - Arlington, United States
Duration: Nov 12 2015Nov 14 2015

Publication series

NameAAAI Fall Symposium - Technical Report


OtherAAAI 2015 Fall Symposium
Country/TerritoryUnited States

ASJC Scopus subject areas

  • Engineering(all)


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