Abstract
Software requirement specification (SRS) is an important step in software engineering. Extracting requirement specification from an application field is a difficult task. In this paper, we consider software requirement as a problem to be solved by intelligent planning. To do this, one of the difficult things is how to represent the domain, since the software requirement has a feature of changeability. Thus, we divide the work into two tasks: the first one is to describe an incomplete domain of software requirement using PDDL(Planning Domains Definition Language) [7]; the second one is to complete the domain by learning from plan samples which is extracted from business processes. We modify the tool of [9] to learn action models with quantified conditional effects, which is the second task. In this way, people only need to do the first task and extract plan samples, which means the efforts of human beings are saved. At the end of the paper, we give our experiment result to show the efficiency of our method.
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Yang, Q., Wu, K.H., Jiang, Y.F.: Learning Action Models from Plan Examples with Incomplete Knowledge. In: Proceedings of the 2005 International Conference on Automated Planning and Scheduling (ICAPS 2005), Monterey, CA, USA, June, pp. 241–250 (2005)
Blythe, J., Kim, J., Ramachandran, S., Gil, Y.: An Integrated Environment for Knowledge Acquisition. In: Proceedings of the 2001 International Conference on Intelligent User Interfaces (IUI 2001), Santa Fe, NM, pp. 13–20 (2001)
Gil, Y.: Learning by Experimentation: Incremental Refinement of Incomplete Planning Domains. In: Eleventh Intl. Conf. on Machine Learning, pp. 87–95 (1994)
Oates, T., Cohen, P.R.: Searching for Planning Operators with Context-dependent and Probabilistic Effects. In: Proceedings of the Thirteenth National Conference on AI (AAAI 1996), Portland, OR, pp. 865–868 (1996)
Sommerville, I.: Software Engineering. Addison-Wesley (2000)
Wan, H., Zheng, Y.X., Li, L.: Software Requirement Specification Based on Answer Sets Semantics and Subject-Predicate-Object. In: The 8th International Conference for Young Computer Scientists (ICYCS 2005), Beijing, China, September 20-22 (2005)
Fox, M., Long, D.: PDDL2.1: an Extension to PDDL for Expressing Temporal Planning Domains. Journal of Artificial Intelligence Research 20, 61–124 (2003)
Borchers, B., Furman, J.: A Two-phase Exact Algorithm for MAX-SAT and Weighted MAX-SAT Problems. Journal of Combinatorial Optimization 2(4), 299–306 (1999)
Zhuo, H.K., Li, L., Bian, R., Wan, H.: Requirement Specification Based on Action Model Learning. In: Huang, D.-S., Heutte, L., Loog, M. (eds.) ICIC 2007. LNCS, vol. 4681, pp. 565–574. Springer, Heidelberg (2007)
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Zhuo, H., Li, L., Yang, Q., Bian, R. (2008). Learning Action Models with Quantified Conditional Effects for Software Requirement Specification. In: Huang, DS., Wunsch, D.C., Levine, D.S., Jo, KH. (eds) Advanced Intelligent Computing Theories and Applications. With Aspects of Theoretical and Methodological Issues. ICIC 2008. Lecture Notes in Computer Science, vol 5226. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-87442-3_107
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DOI: https://doi.org/10.1007/978-3-540-87442-3_107
Publisher Name: Springer, Berlin, Heidelberg
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