no code implementations • 17 Aug 2015 • Mohan Sridharan, Michael Gelfond, Shiqi Zhang, Jeremy Wyatt
This paper describes an architecture for robots that combines the complementary strengths of probabilistic graphical models and declarative programming to represent and reason with logic-based and probabilistic descriptions of uncertainty and domain knowledge.
no code implementations • 5 May 2014 • Shiqi Zhang, Mohan Sridharan, Michael Gelfond, Jeremy Wyatt
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.
