Markov Decision Processes: Discrete Stochastic Dynamic Programming
Markov Decision Processes: Discrete Stochastic Dynamic Programming
Composing Web services on the Semantic Web
The VLDB Journal — The International Journal on Very Large Data Bases
Journal of Artificial Intelligence Research
Information gathering during planning for Web Service composition
Web Semantics: Science, Services and Agents on the World Wide Web
Introduction to semantic web services and web process composition
SWSWPC'04 Proceedings of the First international conference on Semantic Web Services and Web Process Composition
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Many of the previous methods for composing Web processes utilize either classical planning techniques such as hierarchical task networks (HTNs), or decision-theoretic planners such as Markov decision processes (MDPs). While offering a way to automatically compose a desired Web process, these techniques do not scale to large processes. In addition, classical planners assume away the uncertainties involved in service invocations such as service failure. In this paper, we present a hierarchical approach for composing Web processes that may be nested – some of the components of the process may be Web processes themselves. We model the composition problem using a semi-Markov decision process (SMDP) that generalizes MDPs by allowing actions to be temporally extended. We use these actions to represent the invocation of lower level Web processes whose execution times are uncertain and different from simple service invocations.