University of Illinois Urbana-Champaign

Sketching: a Cognitively inspired Compositional Theorem Prover that Learns to Prove - a Proposal

Brando Miranda

Content Files
IBM Theorm proving with cognitive science 1 page - Draft 2.pdf
Proposed Plan of Graduate Study and Research and the applicant's long-range career goals Ford 2020 (abstractions) (Final Draft).pdf

Permalink

https://hdl.handle.net/2142/109134

Description

Title
Sketching: a Cognitively inspired Compositional Theorem Prover that Learns to Prove - a Proposal
Author(s)
Brando Miranda
Issue Date
2020-12-23
Keyword(s)
  • meta-learning
  • machine learning
  • sketching
  • theorem proving
  • agi
  • ai safety
  • AI
  • artificial general intelligence
  • automatic theorem proving
Date of Ingest
2020-12-23T18:02:24Z
Abstract
Mathematics is a powerful tool that has benefited humanity for millenia. Proficiency in this subject has far reaching implications for society because physics, mechanical engineering, computer science, statistics, machine learning and the scientific method itself depend on it. For example, being able to automate mathematical proofs would allow us to create safe and interpretable A.I. because a system with provable guarantees is a system we understand and can trust. This is why in this proposal we suggest a novel way of implementing the concept of sketching for automated theorem provers using powerful ideas from cognitive science and artificial neural networks; these ideas are: compositionality, learning-to-learn, learning as model building and curriculum-learn.
Type of Resource
text
Genre of Resource
proposal
Permalink
http://hdl.handle.net/2142/109134

Owning Collections

Research and Tech Reports - Computer Science PRIMARY