Computable decision-making on the reals and other spaces via partiality and nondeterminism

Benjamin Sherman, Luke Sciarappa, Michael Carbin, Adam Chlipala. Computable decision-making on the reals and other spaces via partiality and nondeterminism. Proceedings of the 33rd Annual ACM/IEEE Symposium on Logic in Computer Science (LICS'18). July 2018.

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Though many safety-critical software systems use floating point to represent real-world input and output, programmers usually have idealized versions in mind that compute with real numbers. Significant deviations from the ideal can cause errors and jeopardize safety. Some programming systems implement exact real arithmetic, which resolves this matter but complicates others, such as decision-making. In these systems, it is impossible to compute (total and deterministic) discrete decisions based on connected spaces such as R. We present programming language semantics based on constructive topology with variants allowing nondeterminism and/or partiality. Either nondeterminism or partiality suffices to allow computable decision-making on connected spaces such as R. We then introduce pattern matching on spaces, a language construct for creating programs on spaces that generalizes pattern matching in functional programming, where patterns need not represent decidable predicates and also may overlap or be inexhaustive, giving rise to nondeterminism or partiality, respectively. Nondeterminism and/or partiality also yield formal logics for constructing approximate decision procedures. We implemented these constructs in the Marshall language for exact real arithmetic.