Integrating probabilistic graphical models and declarative programming for knowledge representation and reasoning in robotics

Shiqi Zhang, Mohan Sridharan, Michael Gelfond

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Abstract

This paper describes an architecture that combines the complementary strengths of declarative programming and probabilistic graphical models to enable robots to represent, reason with, and learn from, qualitative and quantitative descriptions of uncertainty and knowledge. An action language is used for the low-level (LL) and high-level (HL) system descriptions in the architecture, and the definition of recorded histories in the HL is expanded to allow prioritized defaults. For any given goal, tentative plans created in the HL using default knowledge and commonsense reasoning are implemented in the LL using probabilistic algorithms, with the corresponding observations used to update the HL history. Tight coupling between the two levels enables automatic selection of relevant variables and generation of suitable action policies in the LL for each HL action, and supports reasoning with violation of defaults, noisy observations and unreliable actions in large and complex domains. The architecture is evaluated in simulation and on physical robots moving objects to specific places in indoor domains; the benefit on robots is a reduction in task execution time of 39% compared with a purely probabilistic, but still hierarchical, approach.
Original languageEnglish
Title of host publicationProceedings of 2nd Workshop on Planning and Robotics (PlanRob 2014) at ICAPS 2014
EditorsAlberto Finzi, Andrea Orlandini
PublisherAssociation for the Advancement of Artificial Intelligence
Pages127-135
Number of pages9
Publication statusPublished - 2014
Event2nd Workshop on Planning and Robotics (PlanRob 2014) at ICAPS 2014 - Portsmouth, New Hampshire, USA, United States
Duration: 22 Jun 201522 Jun 2015

Conference

Conference2nd Workshop on Planning and Robotics (PlanRob 2014) at ICAPS 2014
Country/TerritoryUnited States
CityPortsmouth, New Hampshire, USA
Period22/06/1522/06/15

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