Model checking and abstraction
ACM Transactions on Programming Languages and Systems (TOPLAS)
Strong planning under partial observability
Artificial Intelligence
Run-Time Monitoring of Instances and Classes of Web Service Compositions
ICWS '06 Proceedings of the IEEE International Conference on Web Services
Computational Complexity of Web Service Composition Based on Behavioral Descriptions
ICTAI '08 Proceedings of the 2008 20th IEEE International Conference on Tools with Artificial Intelligence - Volume 01
Type-Aware Web Service Composition Using Boolean Satisfiability Solver
CECANDEEE '08 Proceedings of the 2008 10th IEEE Conference on E-Commerce Technology and the Fifth IEEE Conference on Enterprise Computing, E-Commerce and E-Services
Observation reduction for strong plans
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
A parametric hierarchical planner for experimenting abstraction techniques
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
Automated composition of web services by planning at the knowledge level
IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
Conceptual modeling approaches for dynamic web service composition
The evolution of conceptual modeling
Behavioural description based web service composition using abstraction and refinement
International Journal of Web and Grid Services
Efficient anytime algorithm for large-scale QoS-aware web service composition
International Journal of Web and Grid Services
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The Web Service Composition (WSC) problem with respect to behavioral descriptions deals with the automatic synthesis of a coordinator web service, c, that controls a set of web services to reach a goal state. Despite its importance, however, solving the WSC problem for a general case (when c has only partial observations) remains to be doubly exponential in the number of variables in web service descriptions, rendering any attempts to compute an exact solution for modest size impractical. Toward this challenge, in this paper, we propose two novel (signature preserving and subsuming) approximation-based approaches using abstraction and refinement. We empirically validate that our proposals can solve realistic problems efficiently.