Paper Info
Title
Managing Uncertainty in Text-to-Sketch Tracking Problems
Abstract
Text-to-Sketch (T2S) is a class of problems in which geolocation is performed using natural language descriptions of a location or locations as input. This is a challenging problem due to the many sources of uncertainty inherent to the task: there is often syntactic and semantic ambiguity present in the input observations, as well as referential ambiguity when the language used to describe the scene may refer to many possible objects or locations in the world. Tracking problems, in which the Text-to-Sketch paradigm is extended to incorporate multiple locations and movements over a temporal dimension, introduce additional uncertainty. We describe a tool for managing the uncertainty in Text-to-Sketch problems called MUTTS. The MUTTS system combines traditional natural language processing (NLP) tools with algorithms used to manage uncertainty in mobile robot navigation to allow the temporal and geographical constraints in the text to incrementally reduce the overall uncertainty of a subject's location and produce high quality sketches of the subject's location and movements over time.
Year
DOI
Venue
2011
10.1109/ICTAI.2011.70
ICTAI
Keywords
Field
DocType
natural language description,text-to-sketch paradigm,text-to-sketch problem,input observation,traditional natural language processing,multiple location,referential ambiguity,overall uncertainty,additional uncertainty,text-to-sketch tracking problems,mutts system,natural language,particle filters,path planning,particle filter,uncertainty,semantics,mobile robot navigation,natural language processing,mobile robots
Motion planning,Computer science,Geolocation,Natural language,Artificial intelligence,Mobile robot navigation,Ambiguity,Semantics,Mobile robot,Machine learning,Sketch
Conference
ISSN
Citations 
PageRank 
1082-3409
1
0.35
References 
Authors
2
2
Name
Order
Citations
PageRank
Matthew D. Schmill19814.67
Tim Oates21069190.77