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Towards Explainable Multi-Objective Probabilistic Planning

Roykrong Sukkerd, Reid Simmons and David Garlan.


In Proceedings of the 4th International Workshop on Software Engineering for Smart Cyber-Physical Systems (SEsCPS\'18), Gothenburg, Sweden, 27 May 2018.

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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.  
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