Application of ε -testers algorithms under sketch calculation model in robot navigation problem

Authors:
Carlos Rodriguez Lucatero
Affiliations:
Universidad Autónoma Metropolitana Campus Cuajimalpa, Department of Information Technology, Mexico D.F., Mexico
Venue:
ICS'09 Proceedings of the 13th WSEAS international conference on Systems
Year:
2009

Citing 11
Cited 0

A theory of the learnable

Communications of the ACM
How to learn an unknown environment (extended abstract)

SFCS '91 Proceedings of the 32nd annual symposium on Foundations of computer science
Self-testing/correcting with applications to numerical problems

Journal of Computer and System Sciences - Special issue: papers from the 22nd ACM symposium on the theory of computing, May 14–16, 1990
Designing programs that check their work

Journal of the ACM (JACM)
Inferring Finite Automata with Stochastic Output Functions and an Application to Map Learning

Machine Learning
Robust Characterizations of Polynomials withApplications to Program Testing

SIAM Journal on Computing
Property testing and its connection to learning and approximation

FOCS '96 Proceedings of the 37th Annual Symposium on Foundations of Computer Science
On the value of private information

TARK '01 Proceedings of the 8th conference on Theoretical aspects of rationality and knowledge
The problem of robot random motion tracking learning algorithms

ISPRA'07 Proceedings of the 6th WSEAS International Conference on Signal Processing, Robotics and Automation
New lower bound techniques for robot motion planning problems

SFCS '87 Proceedings of the 28th Annual Symposium on Foundations of Computer Science
Creating and utilizing symbolic representations of spatial knowledge using mobile robots

Creating and utilizing symbolic representations of spatial knowledge using mobile robots

Quantified Score

Hi-index	0.00

Visualization

Abstract

The goal of this article is the application of efficient approximate testing methods for the robot tracking problem as well as for the building map problem. In the Databases field, one wish to classify uncertain data and to answer queries in an approximated way in the case that data sources are incoherent. Our interest is based on the fundamental problems in the randomized approximate testing algorithms. Concerning robot navigation we would like to apply efficient data sketching algorithms for testing the automata inferred on robot motion tracking problems as well as on maps sensorialy generated by robot exploration and building map process.