Adaptable Deep Learning Generation by Automatic Service Composition

Author(s):  
Incheon Paik ◽  
Ryo Ataka
2008 ◽  
Vol 29 (3-4) ◽  
pp. 265-276 ◽  
Author(s):  
Maria J. Santofimia ◽  
Francisco Moya ◽  
Felix J. Villanueva ◽  
David Villa ◽  
Juan C. Lopez

2013 ◽  
Vol 756-759 ◽  
pp. 2120-2124
Author(s):  
Shan Zhou ◽  
Fang Yu Zhang

The paper proposes a method for semantic message matching in automatic service composition. It develops a framework in which the exported message description and behavior description of a service, and represents the behavior of a service with a finite state machine. Since the service interface definition can be represented by ontology concepts, the internal representation language enables us to define some issues required by service composition formally, qualitative and quantitative constraints plus reasoning on concepts, and the service behavior can be represented using linear logic formulas, so the inference rules of linear logic can check the match-ability and satisfy-ability of service message.


2012 ◽  
Vol 4 (1) ◽  
pp. 16-28 ◽  
Author(s):  
Maria J. Santofimia ◽  
Xavier del Toro ◽  
Felix J. Villanueva ◽  
Jesus Barba ◽  
Francisco Moya ◽  
...  

The incapability to foresee or react to all the events that take place in a specific environment supposes an important handicap for Ambient Intelligence systems, expected to be self-managed, proactive, and goal-driven. Endowing such systems with capabilities to understand and reason about context seems like a promising solution to overcome this hitch. Supported on the service-oriented paradigm, composing rather than combining services provides a reasonable mean to implement versatile systems. This paper describes how systems for Ambient Intelligence can be improved by combining automatic service composition and reasoning capabilities upon a distributed middleware framework.


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