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Planning with Learned Binarized Neural Networks Benchmarks for MaxSAT Evaluation 2021

arXiv 2108.00633 ↗First posted 2 Aug 2021cs.AIcited by 0

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Abstract (arXiv)

This document provides a brief introduction to learned automated planning problem where the state transition function is in the form of a binarized neural network (BNN), presents a general MaxSAT encoding for this problem, and describes the four domains, namely: Navigation, Inventory Control, System Administrator and Cellda, that are submitted as benchmarks for MaxSAT Evaluation 2021.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 3 of its 25 references that are in the map. First posted on arXiv 2 Aug 2021.

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References · 3

  1. 2015Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shiftcited by 6,979
  2. 2019Reward Potentials for Planning with Learned Neural Network Transition Modelscited by 2
  3. 2020Encoding Linear Constraints into SATcited by 2

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