Unsupervised Grounding of Plannable First-Order Logic Representation from Images
This addresses the challenge of bridging perceptual systems with symbolic planning for AI researchers, though it is incremental as it builds on prior work like Latplan.
The paper tackled the problem of obtaining discrete, logical predicates from images for symbolic reasoning in reinforcement learning, proposing an unsupervised architecture that captures interpretable relations and enables compatibility with classical planning in experiments on 8-Puzzle and Blocksworld environments.
Recently, there is an increasing interest in obtaining the relational structures of the environment in the Reinforcement Learning community. However, the resulting "relations" are not the discrete, logical predicates compatible to the symbolic reasoning such as classical planning or goal recognition. Meanwhile, Latplan (Asai and Fukunaga 2018) bridged the gap between deep-learning perceptual systems and symbolic classical planners. One key component of the system is a Neural Network called State AutoEncoder (SAE), which encodes an image-based input into a propositional representation compatible to classical planning. To get the best of both worlds, we propose First-Order State AutoEncoder, an unsupervised architecture for grounding the first-order logic predicates and facts. Each predicate models a relationship between objects by taking the interpretable arguments and returning a propositional value. In the experiment using 8-Puzzle and a photo-realistic Blocksworld environment, we show that (1) the resulting predicates capture the interpretable relations (e.g. spatial), (2) they help obtaining the compact, abstract model of the environment, and finally, (3) the resulting model is compatible to symbolic classical planning.