Highly Incremental: A Simple Programmatic Approach for Many Objectives

Lecture notes in computer science, pp. 559–577

Abstract

Abstract We present a one-fits-all programmatic approach to reason about a plethora of objectives on probabilistic programs. The first ingredient is to add a reward-statement to the language. We then define a program transformation applying a monotone function to the cumulative reward of the program. The key idea is that this transformation uses incremental differences in the reward. This simple, elegant approach enables to express e.g., higher moments, threshold probabilities of rewards, the expected excess over a budget, and moment-generating functions. All these objectives can now be analyzed using a single existing approach: probabilistic wp-reasoning. We automated verification using the Caesar deductive verifier and report on the application of the transformation to some examples.

Authors 2

1. Philipp Schröer corresponding Aachen

   RWTH Aachen University

   Affiliation as printed

   RWTH Aachen University, Aachen, Germany

2. Joost-Pieter Katoen Aachen

   RWTH Aachen University

   Affiliation as printed

   RWTH Aachen University, Aachen, Germany