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AutumnSynth: Synthesis of Reactive Programs with Structured Latent State
Ria Das · Zenna Tavares · Josh Tenenbaum · Armando Solar-Lezama

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in Advances in Programming Languages and Neurosymbolic Systems (AIPLANS) »
Event URL: https://openreview.net/forum?id=Qw8eyl2_N_- »

The human ability to efficiently discover causal theories of their environments from observations is a feat of nature that remains elusive in machines. In this work, we attempt to make progress on this frontier by formulating the challenge of causal mechanism discovery from observed data as one of program synthesis. We focus on the domain of time-varying, Atari-like 2D grid worlds, and represent causal models in this domain using a programming language called Autumn. Discovering the causal structure underlying a sequence of observations is equivalent to identifying the program in the Autumn language that generates the observations. We introduce a novel program synthesis algorithm, called AutumnSynth, that approaches this synthesis challenge by integrating standard methods of synthesizing functions with an automata synthesis approach, used to discover the model's latent state. We evaluate our method on a suite of Autumn programs designed to express the richness of the domain, and our results signal the potential of our formulation.

Author Information

Ria Das (Massachusetts Institute of Technology)
Zenna Tavares (MIT)
Josh Tenenbaum (MIT)

Josh Tenenbaum is an Associate Professor of Computational Cognitive Science at MIT in the Department of Brain and Cognitive Sciences and the Computer Science and Artificial Intelligence Laboratory (CSAIL). He received his PhD from MIT in 1999, and was an Assistant Professor at Stanford University from 1999 to 2002. He studies learning and inference in humans and machines, with the twin goals of understanding human intelligence in computational terms and bringing computers closer to human capacities. He focuses on problems of inductive generalization from limited data -- learning concepts and word meanings, inferring causal relations or goals -- and learning abstract knowledge that supports these inductive leaps in the form of probabilistic generative models or 'intuitive theories'. He has also developed several novel machine learning methods inspired by human learning and perception, most notably Isomap, an approach to unsupervised learning of nonlinear manifolds in high-dimensional data. He has been Associate Editor for the journal Cognitive Science, has been active on program committees for the CogSci and NIPS conferences, and has co-organized a number of workshops, tutorials and summer schools in human and machine learning. Several of his papers have received outstanding paper awards or best student paper awards at the IEEE Computer Vision and Pattern Recognition (CVPR), NIPS, and Cognitive Science conferences. He is the recipient of the New Investigator Award from the Society for Mathematical Psychology (2005), the Early Investigator Award from the Society of Experimental Psychologists (2007), and the Distinguished Scientific Award for Early Career Contribution to Psychology (in the area of cognition and human learning) from the American Psychological Association (2008).

Armando Solar-Lezama (MIT)

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