Eva Darulova

Eva Darulova, Anastasia Volkova

Elementary function calls are a common feature in numerical programs. While their implementions in library functions are highly optimized, their computation is nonetheless very expensive compared to plain arithmetic. Full accuracy is, however, not always needed. Unlike arithmetic, where the performance difference between for example single and double precision floating-point arithmetic is relatively small, elementary function calls provide a much richer tradeoff space between accuracy and efficiency. Navigating this space is challenging. First, generating approximations of elementary function calls which are guaranteed to satisfy accuracy error bounds is highly nontrivial. Second, the performance of such approximations generally depends on several parameters which are unintuitive to choose manually, especially for non-experts. We present a fully automated approach and tool which approximates elementary function calls inside small programs while guaranteeing overall user provided error bounds. Our tool leverages existing techniques for roundoff error computation and approximation of individual elementary function calls, and provides automated selection of many parameters. Our experiments show that significant efficiency improvements are possible in exchange for reduced, but guaranteed, accuracy.

Ivan Gavran, Brendon Boldt, Eva Darulova et al.

We present Flipper, a natural language interface for describing high-level task specifications for robots that are compiled into robot actions. Flipper starts with a formal core language for task planning that allows expressing rich temporal specifications and uses a semantic parser to provide a natural language interface. Flipper provides immediate visual feedback by executing an automatically constructed plan of the task in a graphical user interface. This allows the user to resolve potentially ambiguous interpretations. Flipper extends itself via naturalization: its users can add definitions for utterances, from which Flipper induces new rules and adds them to the core language, gradually growing a more and more natural task specification language. Flipper improves the naturalization by generalizing the definition provided by users. Unlike other task-specification systems, Flipper enables natural language interactions while maintaining the expressive power and formal precision of a programming language. We show through an initial user study that natural language interactions and generalization can considerably ease the description of tasks. Moreover, over time, users employ more and more concepts outside of the initial core language. Such extensions are available to the Flipper community, and users can use concepts that others have defined.