Synthesis of Procedural Models for Deterministic Transition Systems

TL;DR

This paper presents a versatile method for synthesizing procedural models of state transitions in discrete systems, capable of handling various target languages and model types through inductive program synthesis guided by combinatorial search.

Contribution

It introduces a general, inductive approach for synthesizing structured programs that model state transitions, adaptable to different modeling languages and tasks.

Findings

Applicable to multiple modeling tasks like STRIPS and cellular automata

Uses combinatorial search guided by complexity and fitness functions

Successfully synthesizes structured transition models from example data

Abstract

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.