Inferring Affordances Using Learning Techniques

Amel Bennaceur (1) , Johansson Richard (2) , Moschitti Alessandro (2) , Spalazzese Romina (3) , Daniel Sykes (1) , Rachid Saadi (1) , Valérie Issarny (1)

1 ARLES - Software architectures and distributed systems
2 DISI - Information Engineering and Computer Science Department
3 DI - Dipartimento di Informatica [Italy]

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.

Domaines: Informatique ubiquitaire

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⟩