%
% GENERATED FROM http://acme.able.cs.cmu.edu
%    by   : anonymous
%    IP   : ec2-3-149-25-117.us-east-2.compute.amazonaws.com
%    at   : Thu, 21 Nov 2024 23:51:46 -0500 GMT
%    
% Selection : Publication #546
%


<tr><td><table><tr><td>

@InProceedings{2017:Pandey:IBLHP,
      AUTHOR = {Pandey, Ashutosh and Schmerl, Bradley and Garlan, David},
      TITLE = {Instance-based Learning for Hybrid Planning},
      YEAR = {2017},
      MONTH = {18-22 September},
      BOOKTITLE = {Proceedings of the 3rd International Workshop on Data-driven Self-regulating Systems (DSS 2017)},
      ADDRESS = {Tucson, AZ, USA},
      PDF = {http://acme.able.cs.cmu.edu/pubs/uploads/pdf/IBL-DSS2017.pdf},
      ABSTRACT = {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}
}