@inproceedings{pandey_hybrid_2020,
	title = {Hybrid {Planning} {Using} {Learning} and {Model} {Checking} for {Autonomous} {Systems}},
	doi = {10.1109/ACSOS49614.2020.00026},
	abstract = {Self-adaptive software systems rely on planning to make adaptation decisions autonomously. Planning is required to produce high-quality adaptation plans in a timely manner; however, quality and timeliness of planning are conflicting in nature. This conflict can be reconciled with hybrid planning, which can combine reactive planning (to quickly provide an emergency response) with deliberative planning that take time but determine a higher-quality plan. While often effective, reactive planning sometimes risks making the situation worse. Hence, a challenge in hybrid planning is to decide whether to invoke reactive planning until the deliberative planning is ready with a high-quality plan. To make this decision, this paper proposes a novel learning-based approach. We demonstrate that this learning-based approach outperforms existing techniques that are based on specifying fixed conditions to invoke reactive planning in two domains: enterprise cloud systems and unmanned aerial vehicles.},
	booktitle = {2020 {IEEE} {International} {Conference} on {Autonomic} {Computing} and {Self}-{Organizing} {Systems} ({ACSOS})},
	author = {Pandey, A. and Ruchkin, I. and Schmerl, B. and Garlan, D.},
	month = aug,
	year = {2020},
	keywords = {automated planning, Autonomous systems, machine learning, Model checking, Planning, Probabilistic logic, probabilistic model checking, Software systems, Training, Uncertainty},
	pages = {55--64}
}