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An SMT-based active learning algorithm for nondeterministic weighted automata (WFAs) as a practical and robust alternative to Hankel/L*-style methods and is capable of learning numerous minimal WFAs over both finite and infinite semirings.
The empirical evaluation shows two complementary strengths of Slice when paired with discrete backends: it enables exact inference for challenging continuous programs that lie beyond the reach of previous exact systems, and is competitive with state-of-the-art exact inference systems for continuous programs.
The transformer and synthesis rules sound against an operational MDP semantics, without requiring a finite state space: this approach can be seen as a symbolic approach - at program level - for multiobjective optimization over infinite MDPs.
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