Publication
A more bio-plausible approach to the evolutionary inference of finite state machines
AbstractWith resemblance of finite-state machines to some biological mechanisms in cells and numerous applications of finite automata in different fields, this paper uses analogies and metaphors to introduce an element of bio-plausibility to evolutionary grammatical inference. Inference of a finite-state machine that generalizes well over unseen input-output examples is an NP-complete problem. Heuristic algorithms exist to minimize the size of an FSM keeping it consistent with all the input-output sequences. However, their performance dramatically degrades in presence of noise in the training set. Evolutionary algorithms perform better for noisy data sets but they do not scale well and their performance drops as size or complexity of the target machine grows. Here, inspired by a biological perspective, an evolutionary algorithm with a novel representation and a new fitness function for inference of Moore finite-state machines of limited size is proposed and compared with one of the latest evolutionary techniques.
Download publicationAssociated Researchers
Related Resources
See what’s new.
2024
The Problem of Generative Parroting: Navigating Toward Responsible AI (Part 3)The third part of this series explores using Masked Autoencoders to…
1991
A Multi-Scale Stochastic Modelling Primitive for Computer GraphicsStochastic modelling has been successfully used in computer graphics…
2022
In-Depth Mouse: Integrating Desktop Mouse into Virtual RealityVirtual Reality (VR) has the potential for productive knowledge work,…
2005
Towards integrated performance-driven generative design toolsPerformance-driven generative design methods are capable of producing…
Get in touch
Something pique your interest? Get in touch if you’d like to learn more about Autodesk Research, our projects, people, and potential collaboration opportunities.
Contact us