Moa Johansson

Docent vid Data Science och AI

Källa: chalmers.se

Kontaktuppgifter ORCID

Publikationer (34)
Projekt (3)

Visar 34 publikationer

2024

What Can Large Language Models Do for Theorem Proving and Formal Methods?

Moa Johansson

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol. 14380 LNCS, p. 391-394

Paper i proceeding

2023

Finding the structure of parliamentary motions in the Swedish Riksdag 1971–2015

Sebastianus Cornelis Jacobus Bruinsma, Moa Johansson

Quality and Quantity. Vol. In press

Artikel i vetenskaplig tidskrift

2023

Sudden Semantic Shifts in Swedish NATO Discourse

Brian Bonafilia, Sebastianus Cornelis Jacobus Bruinsma, Denitsa Saynova et al

Association for Computational Linguistics . Annual Meeting Conference Proceedings. Vol. 4, p. 184-193

Paper i proceeding

2023

Exploring Mathematical Conjecturing with Large Language Models

Moa Johansson, Nicholas Smallbone

CEUR Workshop Proceedings. Vol. 3432, p. 62-77

Paper i proceeding

2023

Pacing Patterns of Half-Marathon Runners: An analysis of ten years of results from Gothenburg Half Marathon

Moa Johansson, Johan Atterfors, Johan Lamm

International Journal of Computer Science in Sport. Vol. 22 (1), p. 124-138

Artikel i vetenskaplig tidskrift

2023

The Effect of Scaling, Retrieval Augmentation and Form on the Factual Consistency of Language Models

Lovisa Hagström, Denitsa Saynova, Tobias Norlund et al

EMNLP 2023 - 2023 Conference on Empirical Methods in Natural Language Processing, Proceedings, p. 5457-5476

Paper i proceeding

2022

Machine Learning of Pacing Patterns for Half Marathon

Johan Atterfors, Johan Lamm, Moa Johansson

Preprint

2022

Generating Weekly Training Plans in the Style of a Professional Swimming Coach Using Genetic Algorithms and Random Trees

Rikard Eriksson, Johan Nicander, Moa Johansson et al

Proceedings of the 9th International Performance Analysis Workshop and Conference & 5th IACSS Conference (Advances in Intelligent Systems and Computing 1426). Vol. 1426, p. 61-68

Paper i proceeding

2022

Machine Learning Techniques for Gait Analysis in Skiing

Savya Sachi Gupta, Moa Johansson, Dan Kuylenstierna et al

Proceedings of the 9th International Performance Analysis Workshop and Conference & 5th IACSS Conference, p. 126-129

Paper i proceeding

2022

Towards Learning Abstractions via Reinforcement Learning

Erik Jergéus, Leo Karlsson Oinonen, Emil Carlsson et al

CEUR Workshop Proceedings. Vol. 3400, p. 120-126

Paper i proceeding

2022

TriCo—Triple Co-piloting of Implementation, Specification and Tests

Wolfgang Ahrendt, Dilian Gurov, Moa Johansson et al

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol. 13701 LNCS, p. 174-187

Paper i proceeding

2021

Automated Conjecturing in QuickSpec

Moa Johansson, Nicholas Smallbone

1 st Mathematical Reasoning in General Artificial Intelligence Workshop, ICLR 2021.

Paper i proceeding

2021

Conjectures, tests and proofs: An overview of theory exploration

Moa Johansson, Nicholas Smallbone

Electronic Proceedings in Theoretical Computer Science, EPTCS. Vol. 341, p. 1-16

Paper i proceeding

2020

Template-based Theory Exploration: Discovering Properties of Functional Programs by Testing

Sólrún Einarsdóttir, Nicholas Smallbone, Moa Johansson

ACM International Conference Proceeding Series, p. 67-78

Paper i proceeding

2020

Theory exploration: Conjecturing, testing and reasoning about programs

Moa Johansson

Electronic Proceedings in Theoretical Computer Science, EPTCS. Vol. 320

Paper i proceeding

2019

Preface

José Júlio Alferes, Moa Johansson

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol. 11372 LNCS, p. V-VI

Övrig text i vetenskaplig tidskrift

2019

Proving Type Class Laws for Haskell

Andreas Arvidsson, Moa Johansson, Robin Touche

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol. 10447 LNCS, p. 61-74

Paper i proceeding

2019

Lemma Discovery for Induction: A Survey

Moa Johansson

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol. 11617 LNAI, p. 125-139

Paper i proceeding

2019

Identifying cross country skiing techniques using power meters in ski poles.

Moa Johansson, Marie Korneliusson, Nickey Lizbat Lawrence

Communications in Computer and Information Science. Vol. 1056, p. 52-57

Paper i proceeding

2018

Into the infinite - theory exploration for coinduction

Sólrún Einarsdóttir, Moa Johansson, Johannes Åman Pohjala

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol. 11110 LNAI, p. 70-86

Paper i proceeding

2018

Towards Machine Learning on Data from Professional Cyclists

Oscar Ivarsson, Agrin Hilmkil, Moa Johansson et al

Proceedings of the XII World Congress of Performance Analysis of Sport. Vol. 2018

Paper i proceeding

2017

QuickSpec: a lightweight theory exploration tool for programmers (system demonstration)

Maximilian Algehed, Koen Lindström Claessen, Moa Johansson et al

SIGPLAN Notices (ACM Special Interest Group on Programming Languages). Vol. 52 (10), p. 38-39

Artikel i vetenskaplig tidskrift

2017

Automated theory exploration for interactive theorem proving: An introduction to the hipster system

Moa Johansson

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol. 10499 LNCS, p. 1-11

Paper i proceeding

2017

Quick Specifications for the Busy Programmer

Nicholas Smallbone, Moa Johansson, Koen Lindström Claessen et al

Journal of Functional Programming. Vol. 27

Artikel i vetenskaplig tidskrift

2017

QuickSpec: A lightweight theory exploration tool for programmers (system demonstration)

Maximilian Algehed, Koen Claessen, Moa Johansson et al

Haskell 2017 - Proceedings of the 10th ACM SIGPLAN International Symposium on Haskell, co-located with ICFP 2017, p. 38-39

Paper i proceeding

2016

Preface

Michael Kohlhase, Moa Johansson, B. Miller et al

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol. 9791, p. V-VII

Övrig text i vetenskaplig tidskrift

2015

On Interpolation in Automated Theorem Proving

M.P. Bonacina, Moa Johansson

Journal of Automated Reasoning. Vol. 54 (1), p. 69-97

Artikel i vetenskaplig tidskrift

2015

TIP: Tons of Inductive Problems

Koen Lindström Claessen, Moa Johansson, Dan Rosén et al

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol. 9150, p. 332-336

Paper i proceeding

2015

The Theory behind TheoryMine

A. Bundy, F. Cavallo, L. Dixon et al

IEEE Intelligent Systems. Vol. 30 (4), p. 64-69

Artikel i vetenskaplig tidskrift

2015

Conditional Lemma Discovery and Recursion Induction in Hipster.

Moa Johansson, Irene Lobo Valbuena

Electronic Communications of the EASST. Vol. 72

Artikel i vetenskaplig tidskrift

2015

Interpolation Systems for Ground Proofs in Automated Deduction: a Survey

M.P. Bonacina, Moa Johansson

Journal of Automated Reasoning. Vol. 54 (4), p. 353-390

Artikel i vetenskaplig tidskrift

2014

Hipster: Integrating theory exploration in a proof assistant

Moa Johansson, Dan Rosén, Nicholas Smallbone et al

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol. 8543, p. 108-122

Paper i proceeding

2013

Proof-pattern recognition and lemma discovery in ACL2

J. Heras, E. Komendantskaya, Moa Johansson et al

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol. 8312, p. 389-406

Paper i proceeding

2013

Automating Inductive Proofs using Theory Exploration

Koen Lindström Claessen, Dan Rosén, Moa Johansson et al

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol. 7898, p. 392-406

Paper i proceeding

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Visar 3 forskningsprojekt

2023–2026

NeSSy: Neuro-symbolisk syntes via förstärkningsinlärning

Moa Johansson Formella metoder

Vetenskapsrådet (VR)

2020–2023

Bias and methods of AI technology studying political behaviour

Moa Johansson Formella metoder

Marianne och Marcus Wallenberg Stiftelse

1 publikation finns

2015–2018

Lärande och teoribildning inom automatisk teorembevisning

Moa Johansson Programvaruteknik

Vetenskapsrådet (VR)

Det kan finnas fler projekt där Moa Johansson medverkar, men du måste vara inloggad som anställd på Chalmers för att kunna se dem.