Representing and Reasoning with Logical and Probabilistic Knowledge on Robots

Mohan Sridharan, Michael Gelfond

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

Abstract / Description of output

This paper describes an architecture that enables robots to represent and reason with logic-based and probabilistic descriptions of uncertainty and domainknowledge. An action language is used to describe tightly-coupled transition diagrams of the domain at two different resolutions. For any given goal, reasoning with commonsense knowledge at coarse-resolution provides a plan of abstract actions. To implement each such abstract action, therelevant part of the fine-resolution transition diagram is identified, and used with a probabilistic representation of the uncertainty in sensing and actuation, to obtain a sequence of concrete actions. The outcomes of executing these concrete actions are used for subsequent coarse-resolution reasoning. We illustrate the architecture’s capabilities using examples of a mobile robot finding and moving objects in an indoor domain.
Original languageEnglish
Title of host publicationSixth International Workshop on Statistical Relational AI (StaRAI) at the International Joint Conference on Artificial Intelligence (IJCAI 2016)
Publication statusPublished - 11 Jul 2016
EventSixth International Workshop on Statistical Relational AI (StaRAI) at the International Joint Conference on Artificial Intelligence (IJCAI 2016) - New York, United States
Duration: 11 Jul 2016 → …

Workshop

WorkshopSixth International Workshop on Statistical Relational AI (StaRAI) at the International Joint Conference on Artificial Intelligence (IJCAI 2016)
Country/TerritoryUnited States
CityNew York
Period11/07/16 → …

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