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Synthesis of Procedural Models for Deterministic Transition Systems

arXiv 2307.14368 ↗First posted 25 Jul 2023cs.FL cs.AIcited by 0

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Abstract (arXiv)

This paper introduces a general approach for synthesizing procedural models of the state-transitions of a given discrete system. The approach is general in that it accepts different target languages for modeling the state-transitions of a discrete system; different model acquisition tasks with different target languages, such as the synthesis of STRIPS action models, or the update rule of a cellular automaton, fit as particular instances of our general approach. We follow an inductive approach to synthesis meaning that a set of examples of state-transitions, represented as (pre-state, action, post-state) tuples, are given as input. The goal is to synthesize a structured program that, when executed on a given pre-state, outputs its associated post-state. Our synthesis method implements a combinatorial search in the space of well-structured terminating programs that can be built using a Random-Access Machine (RAM), with a minimalist instruction set, and a finite amount of memory. The combinatorial search is guided with functions that asses the complexity of the candidate programs, as well as their fitness to the given input set of examples.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 11 of its 58 references that are in the map. First posted on arXiv 25 Jul 2023.

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References · 11

  1. 2019Mastering Atari, Go, Chess and Shogi by Planning with a Learned Modelcited by 1,353
  2. 2020Understanding the Power and Limitations of Teaching with Imperfect Knowledgecited by 23
  3. 2021Learning First-Order Representations for Planning from Black-Box States: New Resultscited by 23
  4. 2021Generalized Planning as Heuristic Searchcited by 18
  5. 2021Safe Learning of Lifted Action Modelscited by 16
  6. 2014Learning Partially Observable Deterministic Action Modelscited by 15
  7. 2022Scaling-up Generalized Planning as Heuristic Search with Landmarkscited by 14
  8. 2020STRIPS Action Discoverycited by 5
  9. 2021Target Languages (vs. Inductive Biases) for Learning to Act and Plancited by 5
  10. 2020Online Action Recognitioncited by 3
  11. 2022Computing Programs for Generalized Planning as Heuristic Searchcited by 3

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