Automation in handling uncertainty in semantic-knowledge based robotic task-planning by using Markov Logic Networks

2.50
Hdl Handle:
http://hdl.handle.net/10547/336080
Title:
Automation in handling uncertainty in semantic-knowledge based robotic task-planning by using Markov Logic Networks
Authors:
Al-Moadhen, Ahmed; Packianather, Michael; Setchi, Rossi; Qiu, Renxi
Abstract:
Generating plans in real world environments by mobile robot planner is a challenging task due to the uncertainty and environment dynamics. Therefore, task-planning should take in its consideration these issues when generating plans. Semantic knowledge domain has been proposed as a source of information for deriving implicit information and generating semantic plans. This paper extends the Semantic-Knowledge Based (SKB) plan generation to take into account the uncertainty in existing of objects, with their types and properties, and proposes a new approach to construct plans based on probabilistic values which are derived from Markov Logic Networks (MLN). An MLN module is established for probabilistic learning and inferencing together with semantic information to provide a basis for plausible learning and reasoning services in supporting of robot task-planning. In addition, an algorithm has been devised to construct MLN from semantic knowledge. By providing a means of modeling uncertainty in system architecture, task-planning serves as a supporting tool for robotic applications that can benefit from probabilistic inference within a semantic domain. This approach is illustrated using test scenarios run in a domestic environment using a mobile robot.
Citation:
Al-Moadhen A., Packianather M., Setchi R., Qiu R. (2014) 'Automation in Handling Uncertainty in Semantic-knowledge based Robotic Task-planning by Using Markov Logic Networks', Procedia Computer Science, 35, pp. 1023-1032
Publisher:
Elsevier
Journal:
Procedia Computer Science
Issue Date:
2014
URI:
http://hdl.handle.net/10547/336080
DOI:
10.1016/j.procs.2014.08.188
Additional Links:
http://linkinghub.elsevier.com/retrieve/pii/S1877050914011533
Type:
Article
Language:
en
ISSN:
1877-0509
Appears in Collections:
Centre for Research in Distributed Technologies (CREDIT)

Full metadata record

DC FieldValue Language
dc.contributor.authorAl-Moadhen, Ahmeden
dc.contributor.authorPackianather, Michaelen
dc.contributor.authorSetchi, Rossien
dc.contributor.authorQiu, Renxien
dc.date.accessioned2014-11-25T12:41:21Z-
dc.date.available2014-11-25T12:41:21Z-
dc.date.issued2014-
dc.identifier.citationAl-Moadhen A., Packianather M., Setchi R., Qiu R. (2014) 'Automation in Handling Uncertainty in Semantic-knowledge based Robotic Task-planning by Using Markov Logic Networks', Procedia Computer Science, 35, pp. 1023-1032en
dc.identifier.issn1877-0509-
dc.identifier.doi10.1016/j.procs.2014.08.188-
dc.identifier.urihttp://hdl.handle.net/10547/336080-
dc.description.abstractGenerating plans in real world environments by mobile robot planner is a challenging task due to the uncertainty and environment dynamics. Therefore, task-planning should take in its consideration these issues when generating plans. Semantic knowledge domain has been proposed as a source of information for deriving implicit information and generating semantic plans. This paper extends the Semantic-Knowledge Based (SKB) plan generation to take into account the uncertainty in existing of objects, with their types and properties, and proposes a new approach to construct plans based on probabilistic values which are derived from Markov Logic Networks (MLN). An MLN module is established for probabilistic learning and inferencing together with semantic information to provide a basis for plausible learning and reasoning services in supporting of robot task-planning. In addition, an algorithm has been devised to construct MLN from semantic knowledge. By providing a means of modeling uncertainty in system architecture, task-planning serves as a supporting tool for robotic applications that can benefit from probabilistic inference within a semantic domain. This approach is illustrated using test scenarios run in a domestic environment using a mobile robot.en
dc.language.isoenen
dc.publisherElsevieren
dc.relation.urlhttp://linkinghub.elsevier.com/retrieve/pii/S1877050914011533en
dc.rightsArchived with thanks to Procedia Computer Scienceen
dc.subjecttask-planningen
dc.subjectsemantic-knowledge baseden
dc.subjectplanning under uncertaintyen
dc.subjectMarkov logic networksen
dc.subjectroboticsen
dc.titleAutomation in handling uncertainty in semantic-knowledge based robotic task-planning by using Markov Logic Networksen
dc.typeArticleen
dc.identifier.journalProcedia Computer Scienceen
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