AI/ML

Program Synthesis with Pragmatic Communication. (arXiv:2007.05060v1 [cs.AI])


Program synthesis techniques construct or infer programs from user-provided
specifications, such as input-output examples. Yet most specifications,
especially those given by end-users, leave the synthesis problem radically
ill-posed, because many programs may simultaneously satisfy the specification.
Prior work resolves this ambiguity by using various inductive biases, such as a
preference for simpler programs. This work introduces a new inductive bias
derived by modeling the program synthesis task as rational communication,
drawing insights from recursive reasoning models of pragmatics. Given a
specification, we score a candidate program both on its consistency with the
specification, and also whether a rational speaker would chose this particular
specification to communicate that program. We develop efficient algorithms for
such an approach when learning from input-output examples, and build a
pragmatic program synthesizer over a simple grid-like layout domain. A user
study finds that end-user participants communicate more effectively with the
pragmatic program synthesizer over a non-pragmatic one.

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