Inferring Affordances Using Learning Techniques

Abstract : Interoperability among heterogeneous systems is a key challenge in today's networked environment, which is characterised by continual change in aspects such as mobility and availability. Automated solutions appear then to be the only way to achieve interoperability with the needed level of flexibility and scalability. While necessary, the techniques used to achieve interaction, working from the highest application level to the lowest protocol level, come at a substantial computational cost, especially when checks are performed indiscriminately between systems in unrelated domains. To overcome this, we propose to use machine learning to extract the high-level functionality of a system and thus restrict the scope of detailed analysis to systems likely to be able to interoperate.
Document type :
Conference papers
Complete list of metadatas

Cited literature [11 references]  Display  Hide  Download

https://hal.inria.fr/inria-00591264
Contributor : Amel Bennaceur <>
Submitted on : Wednesday, June 1, 2011 - 12:06:14 PM
Last modification on : Friday, May 25, 2018 - 12:02:02 PM
Long-term archiving on : Friday, September 2, 2011 - 2:21:08 AM

File

fet2011.pdf
Files produced by the author(s)

Identifiers

Collections

Citation

Amel Bennaceur, Johansson Richard, Moschitti Alessandro, Spalazzese Romina, Daniel Sykes, et al.. Inferring Affordances Using Learning Techniques. International Workshop on Eternal Systems (EternalS'11), May 2011, Budapest, Hungary. ⟨10.1007/978-3-642-28033-7_7⟩. ⟨inria-00591264⟩

Share

Metrics

Record views

318

Files downloads

396