Functional verification is a complex and time-consuming task in the design process. Recently, various approaches have been developed to improve verification efficiency, including advanced coverage analysis techniques, coverage-driven verification methodologies and coverage-directed stimulus generation techniques. One remaining challenge is to fully automate functional coverage closure. This paper presents a novel approach for coverage-directed stimulus generation based on inductive learning from examples. Test sequences and their related coverage are examined to induce general rules which describe the characteristics of these tests. Coverage closure can be automated by applying the rule learning to clusters similar to the target coverage hole and combining the resulting rules to obtain directives for test generation. The validity of the approach is demonstrated on a pilot case study.