Zhiming Li

Yushi Cao, Zhiming Li, Tianpei Yang et al.

Despite achieving superior performance in human-level control problems, unlike humans, deep reinforcement learning (DRL) lacks high-order intelligence (e.g., logic deduction and reuse), thus it behaves ineffectively than humans regarding learning and generalization in complex problems. Previous works attempt to directly synthesize a white-box logic program as the DRL policy, manifesting logic-driven behaviors. However, most synthesis methods are built on imperative or declarative programming, and each has a distinct limitation, respectively. The former ignores the cause-effect logic during synthesis, resulting in low generalizability across tasks. The latter is strictly proof-based, thus failing to synthesize programs with complex hierarchical logic. In this paper, we combine the above two paradigms together and propose a novel Generalizable Logic Synthesis (GALOIS) framework to synthesize hierarchical and strict cause-effect logic programs. GALOIS leverages the program sketch and defines a new sketch-based hybrid program language for guiding the synthesis. Based on that, GALOIS proposes a sketch-based program synthesis method to automatically generate white-box programs with generalizable and interpretable cause-effect logic. Extensive evaluations on various decision-making tasks with complex logic demonstrate the superiority of GALOIS over mainstream baselines regarding the asymptotic performance, generalizability, and great knowledge reusability across different environments.

Zhiming Li, Xiaofei Xie, Haoliang Li et al.

Hitherto statistical type inference systems rely thoroughly on supervised learning approaches, which require laborious manual effort to collect and label large amounts of data. Most Turing-complete imperative languages share similar control- and data-flow structures, which make it possible to transfer knowledge learned from one language to another. In this paper, we propose a cross-lingual transfer learning framework, PLATO, for statistical type inference, which allows us to leverage prior knowledge learned from the labeled dataset of one language and transfer it to the others, e.g., Python to JavaScript, Java to JavaScript, etc. PLATO is powered by a novel kernelized attention mechanism to constrain the attention scope of the backbone Transformer model such that the model is forced to base its prediction on commonly shared features among languages. In addition, we propose the syntax enhancement that augments the learning on the feature overlap among language domains. Furthermore, PLATO can also be used to improve the performance of the conventional supervised learning-based type inference by introducing cross-lingual augmentation, which enables the model to learn more general features across multiple languages. We evaluated PLATO under two settings: 1) under the cross-domain scenario that the target language data is not labeled or labeled partially, the results show that PLATO outperforms the state-of-the-art domain transfer techniques by a large margin, e.g., it improves the Python to TypeScript baseline by +5.40%@EM, +5.40%@weighted-F1, and 2) under the conventional monolingual supervised learning based scenario, PLATO improves the Python baseline by +4.40%@EM, +3.20%@EM (parametric).