Bosch May 2016 - Nov 2017
Lead, Iot Personalization Technologies @ Bezirk
Bosch Dec 2013 - May 2016
Senior Research Scientist @ Bosch Research and Technology Center
Wright State University Dec 2013 - May 2016
Adjunct Faculty
Riverside Research Sep 2011 - Dec 2013
Research Staff Member
Kno.e.sis - Ohio Center of Excellence In Knowledge-Enabled Computing Jan 2007 - Dec 2013
Ph.d Candidate
Education:
Wright State University 2007 - 2013
Doctorates, Doctor of Philosophy, Computer Science, Engineering, Computer Science and Engineering
The University of Georgia 2004 - 2005
Bachelors, Bachelor of Arts, Cognitive Science
The University of Georgia 1998 - 2002
Bachelors, Bachelor of Science, Computer Science
Skills:
Semantic Web Semantic Technologies Knowledge Representation Rdf Linked Data Artificial Intelligence Owl Java Ontologies Machine Learning Ontology Engineering
Amit P. Sheth - Dayton OH, US Cory A. Henson - Beavercreek OH, US Krishnaprasad Thirunarayan - Beavercreek OH, US
Assignee:
Wright State University - Dayton OH
International Classification:
G06N 5/02
US Classification:
706 55
Abstract:
Machine semantic perception is discussed. One example system can comprise an environmental knowledgebase (KB) associating features with properties, and an interface component receiving sensor data associated with observed properties. The KB and sensor observations can be encoded in bit-matrix and bit-vector representations, respectively, for efficient storage and computation. A perception component can perform semantic perception on observed properties based on the KB. The perception component can determine explanatory features associated with the observed properties through abductive reasoning, and determine discriminatory properties associated with the explanatory features through deductive reasoning. These can be executed in an iterative and interleaved Perception Cycle for efficient computation of minimum actionable explanations of observations. The bit-matrix and bit-vector representations are presented for efficient computation of minimum actionable explanation using perception cycle, the iterative and interleaved application of hybrid abductive and deductive reasoning to seek contextually relevant discriminatory observations to systematically narrow explanatory features.