scholarly journals Using Markov Decision Process Model with Logic Scoring of Preference Model to Optimize HTN Web Services Composition

2011 ◽  
Vol 8 (2) ◽  
pp. 53-73 ◽  
Author(s):  
Jiuyun Xu ◽  
Kun Chen ◽  
Stephan Reiff-Marganiec
Keyword(s):  
Web Services ◽  
Markov Decision Process ◽  
Decision Process ◽  
Process Model ◽  
Ai Planning ◽  
Web Services Composition ◽  
Services Composition ◽  
Composition Problem ◽  
Htn Planning ◽  
Markov Decision

Automatic Web services composition can be achieved using AI planning techniques. HTN planning has been adopted to handle the OWL-S Web service composition problem. However, existing composition methods based on HTN planning have not considered the choice of decompositions available to a problem, which can lead to a variety of valid solutions. In this paper, the authors propose a model of combining a Markov decision process model and HTN planning to address Web services composition. In the model, HTN planning is enhanced to decompose a task in multiple ways and find more than one plan, taking into account both functional and non-functional properties. Furthermore, an evaluation method to choose the optimal plan and experimental results illustrate that the proposed approach works effectively. The paper extends previous work by refining a number of aspects of the approach and applying it to a realistic case study.

Usage of AI-planning techniques to solve problems of Web-services composition

2016 ◽  
pp. 196-203
Author(s):  
O.V. Zakharova ◽  
Keyword(s):  
Web Services ◽  
Process Model ◽  
Ai Planning ◽  
Web Services Composition ◽  
Existing Problems ◽  
Services Composition ◽  
Htn Planning ◽  
Service Similarity ◽  
Planning Methods ◽  
Task Similarity

Automated composition of services described by their process model is difficult but very vital task. Its decision needs strict formalization and strong semantization of a service. Similarity of definitions of intelligent planning tasks and services composition objectives demonstrates the possibility of using AI-planning approaches for resolving Web services composition problems, provided a number of solutions to existing problems. In this article the analysis of task similarity and differences of HTN-planning and composition of Web-services presented by process model is executed. BPEL-services are considered. It is defined main problems that appears when AI-planning methods are used and it is proposed approaches for their resolving by integration DL and HTN-planning. Translation algorithms from BPEL to HTN-DL is also discussed.

scholarly journals A comparative illustration of AI planning-based web services composition

2006 ◽  
Vol 5 (5) ◽  
pp. 1-10 ◽  
Author(s):  
Seog-Chan Oh ◽  
Dongwon Lee ◽  
Soundar R. T. Kumara
Keyword(s):  
Web Services ◽  
Ai Planning ◽  
Web Services Composition ◽  
Services Composition

Complex Network Theory Based Web Services Composition Benchmark Toolkit

2013 ◽  
pp. 1-22
Author(s):  
Seog-Chan Oh ◽  
Dongwon Lee
Keyword(s):  
Web Services ◽  
Complex Network ◽  
Network Theory ◽  
Ground Truth ◽  
Ai Planning ◽  
Web Services Composition ◽  
Complex Network Theory ◽  
Network Characteristics ◽  
Services Composition ◽  
Test Sets

In recent years, while many research proposals have been made toward novel algorithmic solutions of a myriad of web services composition problems, their validation has been less than satisfactory. One of the reasons for this problem is the lack of real benchmark web services data with which researchers can test and verify their proposals. In this chapter, to remedy this challenge, we present a novel benchmark toolkit, WSBen, which is capable of generating synthetic web services data with diverse scenarios and configurations using complex network theory. Web services researchers therefore can evaluate their web services discovery and composition algorithms in a more systematic fashion. The development of WSBen is inspired by our preliminary study on real-world web services crawled from the Web. The proposed WSBen can: (1) generate a collection of synthetic web services files in the WSDL format conforming to diverse complex network characteristics; (2) generate queries and ground truth sets for testing discovery and composition algorithms; (3) prepare auxiliary files to help further statistical analysis; (4) convert WSDL test sets to the formats that conventional AI planners can read; and (5) provide a graphical interface to control all these functions. To illustrate the application of the WSBen, in addition, we present case studies selected from three domains: (1) web services composition; (2) AI planning; and (3) the laws of networks in Physics community. The WSBen toolkit is available at: http://pike.psu.edu/sw/wsben/. This chapter is an invited extension of authors’ previous publication (Oh & Lee, 2009).

Coffee, Tea, or …?: A Markov Decision Process Model for Airline Meal Provisioning

2004 ◽  
Vol 38 (1) ◽  
pp. 107-118 ◽  
Author(s):  
Jason H. Goto ◽  
Mark E. Lewis ◽  
Martin L. Puterman
Keyword(s):  
Markov Decision Process ◽  
Decision Process ◽  
Process Model ◽  
Markov Decision
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