Knowledge-Based Entity Prediction for Improved Machine Perception in Autonomous Systems

Knowledge-based entity prediction (KEP) is a novel task that aims to improve machine perception in autonomous systems. KEP leverages relational knowledge from heterogeneous sources in predicting potentially unrecognized entities. In this article, we provide a formal definition of KEP as a knowledge completion task. Three potential solutions are then introduced, which employ several machine learning and data mining techniques. Finally, the applicability of KEP is demonstrated on two autonomous systems from different domains; namely, autonomous driving and smart manufacturing. We argue that in complex real-world systems, the use of KEP would significantly improve machine perception while pushing the current technology one step closer to achieving full autonomy.

Authors

• Ruwan Wickramarachchi AI Institute, University of South Carolina, Columbia, SC, USA
• Cory Henson Bosch Center for Artificial Intelligence, Pittsburgh, PA, USA
• Amit Sheth University of South Carolina

Keywords

• Autonomous vehicles
• Task analysis
• Semantics
• Process control
• Planning
• Data mining
• Accidents
• Knowledge based systems
• Predictive models
• Autonomous systems
• Autonomic System
• Machine Perception
• Machine Learning
• Computer Vision
• Machine Learning Techniques
• Object Detection
• Related Knowledge
• Semantic Segmentation
• Residential Neighborhoods
• Current Perceptions
• Scene Understanding
• Smart Manufacturing
• Causal Reasoning
• Semantic Types
• Ontology
• Subsequent Processing
• Pedestrian
• Nodes In The Graph
• Cyclical Process
• Association Rule Mining
• Perception Module
• Commonsense Knowledge
• Association Rules
• Representation Of The World
• External Knowledge
• Entity Types
• Correct Label
• Related Entities
• entity prediction
• autonomous driving
• event perception
• knowledge-infused learning