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Instance-based Learning for Hybrid Planning

Ashutosh Pandey, Bradley Schmerl and David Garlan.

In Proceedings of the 3rd International Workshop on Data-driven Self-regulating Systems (DSS 2017), Tucson, AZ, USA, 18-22 September 2017.

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Due to the fundamental trade-off between quality and timeliness of planning, designers of self-adaptive systems often have to compromise between an approach that is quick to find an adaptation plan and an approach that is slow but finds a quality adaptation plan. To deal with this trade-off, in our previous work, we proposed a hybrid planning approach that combines a deliberative and a reactive planning approach to find a balance between quality and timeliness of planning. However, when reactive and deliberative planning is combined to instantiate a hybrid planner, the key challenge is to decide which approach(es) should be invoked to solve a planning problem. To this end, this paper proposes to use a data-driven instance-based learning to find an appropriate combination of the two planning approaches when solving a planning problem. As an initial proof of concept, the paper presents results of a small experiment that demonstrates the potential of the proposed approach to identify a combination of the two planning approaches to solve a planning problem.

Keywords: Planning, Self-adaptation.  
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