Supervised learning algorithm for automatic adaption of situation templates using uncertain data

Authors:
Oliver Zweigle;Kai Häussermann;Uwe-Philipp Käppeler;Paul Levi
Affiliations:
University of Stuttgart, Stuttgart;University of Stuttgart, Stuttgart;University of Stuttgart, Stuttgart;University of Stuttgart, Stuttgart
Venue:
Proceedings of the 2nd International Conference on Interaction Sciences: Information Technology, Culture and Human
Year:
2009

Citing 8
Cited 0

A theory of diagnosis from first principles

Artificial Intelligence
Probabilistic reasoning in intelligent systems: networks of plausible inference

Probabilistic reasoning in intelligent systems: networks of plausible inference
Using crude probability estimates to guide diagnosis

Readings in model-based diagnosis
A theory of measurement in diagnosis from first principles

Artificial Intelligence
Maintaining knowledge about temporal intervals

Communications of the ACM
Ontology Learning for the Semantic Web

IEEE Intelligent Systems
Managing uncertainty in sensor database

ACM SIGMOD Record
Making the World Wide Space happen: New challenges for the Nexus context platform

PERCOM '09 Proceedings of the 2009 IEEE International Conference on Pervasive Computing and Communications

Quantified Score

Hi-index	0.03

Visualization

Abstract

In this paper a learning algorithm for the automatic adaption of a situation template is presented. The approach strongly relies on human-machine interaction as user feedback is a substantial part to automatically adapt a global knowledgebase in this case. The work bases on the assumption of uncertain data and includes elements from the domain of Bayesian Networks and Machine Learning. It is embedded into the cluster of excellence Nexus at the University of Stuttgart which has the aim to build a distributed context aware user-friendly system for sharing context data.