Abstract
In automated planning, a plan is synthesized to achieve the given goals in the assumed operational environment. However, during the plan’s execution, the operational environment may changes so that replanning a new plan is necessary against the changing environment. In some situations, it is impossible to achieve some goals anyhow; in other situations, a new plan can achieve the main goals if it chooses to give up some subsidiary goals. Therefore, the ability of goal-awareness is essential to determine which goals to achieve in the new plan. This paper proposes an efficient analysis algorithm to identify the achievability and conflicts of reachability goals and safety goals. The algorithm reuses the existing analysis result and efficiently analyzes only the part that changes when unforeseen change happens in the environment. This time-saving feature makes our algorithm ideal for rapid response and replanning during the plan’s execution. We evaluated the absolute computation time and relative reduction rate through two case studies. The result shows that our analysis algorithm averagely reduces 97.8% of the calculation time, compared with analyzing without reusing existing analysis results.
The research was partially supported by National Institute of Information and Communications Technology (NICT) and JSPS KAKENHI.
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References
Aizawa, K., Tei, K., Honiden, S.: Identifying safety properties guaranteed in changed environment at runtime. In: 2018 IEEE International Conference on Agents, pp. 75–80, July 2018
Borda, A., Pasquale, L., Koutavas, V., Nuseibeh, B.: Compositional verification of self-adaptive cyber-physical systems. In: 2018 IEEE/ACM 13th International Symposium on Software Engineering for Adaptive and Self-managing Systems, pp. 1–11, May 2018
Ciolek, D., D’Ippolito, N., Pozanco, A., Sardiña, S.: Multi-tier automated planning for adaptive behavior. In: Proceedings of the International Conference on Automated Planning and Scheduling, vol. 30, no. 1, pp. 66–74, June 2020
D’Ippolito, N.R., Braberman, V., Piterman, N., Uchitel, S.: Synthesis of live behaviour models. In: Proceedings of the 18th ACM SIGSOFT International Symposium on Foundations of Software Engineering, FSE 2010, pp. 77–86. Association for Computing Machinery, New York (2010)
Ghallab, M., Nau, D., Traverso, P.: Automated Planning: Theory and Practice. The Morgan Kaufmann Series in Artificial Intelligence, Morgan Kaufmann, Amsterdam (2004)
Grädel, E., Thomas, W., Wilke, T. (eds.): Automata Logics, and Infinite Games: A Guide to Current Research. Springer, Heidelberg (2002)
Maia, P., Vieira, L., Chagas, M., Yu, Y., Zisman, A., Nuseibeh, B.: Dragonfly: a tool for simulating self-adaptive drone behaviours. In: 14th International Symposium on Software Engineering for Adaptive and Self-managing Systems, pp. 107–113. IEEE, May 2019
Tsigkanos, C., Pasquale, L., Menghi, C., Ghezzi, C., Nuseibeh, B.: Engineering topology aware adaptive security: preventing requirements violations at runtime. In: 2014 IEEE 22nd International Requirements Engineering Conference, pp. 203–212, August 2014
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Li, J., Tei, K., Honiden, S. (2021). Identifying Achievable Goals for Adaptive Replanning Against Runtime Environment Change. In: Abraham, A., Piuri, V., Gandhi, N., Siarry, P., Kaklauskas, A., Madureira, A. (eds) Intelligent Systems Design and Applications. ISDA 2020. Advances in Intelligent Systems and Computing, vol 1351. Springer, Cham. https://doi.org/10.1007/978-3-030-71187-0_87
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DOI: https://doi.org/10.1007/978-3-030-71187-0_87
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