@InProceedings{SEsCPS:Explanation:2018,
      AUTHOR = {Sukkerd, Roykrong and Simmons, Reid and Garlan, David},
      TITLE = {Towards Explainable Multi-Objective Probabilistic Planning},
      YEAR = {2018},
      MONTH = {27 May},
      BOOKTITLE = {Proceedings of the 4th International Workshop on Software Engineering for Smart Cyber-Physical Systems (SEsCPS\'18)},
      ADDRESS = {Gothenburg, Sweden},
      PDF = {http://acme.able.cs.cmu.edu/pubs/uploads/pdf/ICSE-WS-SEsCPS-13.pdf},
      ABSTRACT = {Use of multi-objective probabilistic planning to synthesize behavior of CPSs can play an important role in engineering systems that must self-optimize for multiple quality objectives and operate under uncertainty. However, the reasoning behind automated planning is opaque to end-users. They may not understand why a particular behavior is generated, and therefore not be able to calibrate their confidence in the systems working properly. To address this problem, we propose a method to automatically generate verbal explanation of multi-objective probabilistic planning, that explains why a particular behavior is generated on the basis of the optimization objectives. Our explanation method involves describing objective values of a generated behavior and explaining any tradeoff made to reconcile competing objectives. We contribute: (i) an explainable planning representation that facilitates explanation generation, and (ii) an algorithm for generating contrastive justification as explanation for why a generated behavior is best with respect to the planning objectives. We demonstrate our approach on a mobile robot case study.},
      KEYWORDS = {Explainable Software, Self-adaptation}
}