Authors: Ria Das, Joshua B. Tenenbaum, Armando Solar-Lezama, Zenna Tavares

Abstract: While program synthesis has recently garnered interest as an alternative to deep-learning-based approaches to AI, it still faces several limitations. One is that existing methods cannot learn models with time-varying latent state, a common feature of real-world systems. We develop a new synthesis approach that overcomes this challenge by uniting two disparate communities within synthesis: functional synthesis and automata synthesis. We instantiate our algorithm in the domain of causal learning in 2D, Atari-style grid worlds, and our ongoing evaluation shows promising results.