Hybrid Planning Using Learning and Model Checking for Autonomous Systems
Ashutosh Pandey,
Ivan Ruchkin,
Bradley Schmerl and
David Garlan.
In Proceedings of the 2020 IEEE Conference on Autonomic Computing and Self-organizing Systems (ACSOS), Washington, D.C., 19-23 August 2020. Supplemental Material | Presentation Video.
Online links:  Plain Text
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. |
Keywords: Formal Methods, Machine Learning, Self-adaptation.
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