20 citations · 28 across the 3 of their papers we have counts for
8 papers
Discrete Word Embedding for Logical Natural Language Understanding
Masataro Asai, Zilu Tang
We propose an unsupervised neural model for learning a discrete embedding of words. Unlike existing discrete embeddings, our binary embedding supports vector arithmetic operations…
Learning Neural-Symbolic Descriptive Planning Models via Cube-Space Priors: The Voyage Home (to STRIPS)
Masataro Asai, Christian Muise
We achieved a new milestone in the difficult task of enabling agents to learn about their environment autonomously. Our neuro-symbolic architecture is trained end-to-end to produce…
Neural-Symbolic Descriptive Action Model from Images: The Search for STRIPS
Masataro Asai
Recent work on Neural-Symbolic systems that learn the discrete planning model from images has opened a promising direction for expanding the scope of Automated Planning and Schedul…
Towards Stable Symbol Grounding with Zero-Suppressed State AutoEncoder
Masataro Asai, Hiroshi Kajino
While classical planning has been an active branch of AI, its applicability is limited to the tasks precisely modeled by humans. Fully automated high-level agents should be instead…
Unsupervised Grounding of Plannable First-Order Logic Representation from Images
Masataro Asai
Recently, there is an increasing interest in obtaining the relational structures of the environment in the Reinforcement Learning community. However, the resulting "relations" are…
Photo-Realistic Blocksworld Dataset
Masataro Asai
In this report, we introduce an artificial dataset generator for Photo-realistic Blocksworld domain. Blocksworld is one of the oldest high-level task planning domain that is well d…