LLAMA · Logic and Learning: an Algebra and Finite-Model-Theory Approach
„Хоризонт 2020“ — Действия „Мария Склодовска-Кюри“
- Период
- 2021-10-01 → 2025-01-15
- Финансиране от ЕС
- 175 572 €
- Участници
- 1
- Схема
- MSCA-IF
Линиите свързват координатора с партньорите.
Накратко на български
Математическите основи на машинното обучение изследват как алгоритми могат да усвоят логически правила, например за по-точното създаване на заявки към бази данни. Това помага за подобряване на системите за управление на данни и представянето на знания.
Кратко обяснение, генерирано от езиков модел по текста на CORDIS. Оригиналът е по-долу.
Резултати накратко
Logic and Learning: an Algebra and Finite-Model-Theory Approach
Computational learning theory is a branch of computer science that studies the mathematical and algorithmic underpinnings of machine learning. It provides the concepts and methods to classify the computational feasibility of different learning problems. This project lies at the intersection of computational learning theory and logic It was concerned with techniques for learning concept specified specified in logical languages such as first-order logic, in the presence of background knowledge that is similarly specified in logical languages such as first-order logic. The project builds on connections between learning theory, universal algebra and combinatorial graph theory in order to develop new learning algorithms with applications in data management and knowledge representation. Some of the main intended applications lie in the development of new methodologies and systems for the interactive example-driven specification and debugging of database queries and of description logic concepts for knowledge representation.
Текст от CORDIS, на английски · Данни: CORDIS, © Европейски съюз
Цел на проекта
Computational learning theory is a branch of computer science that studies the mathematical and algorithmic underpinnings of machine learning. It provides the concepts and methods to classify the computational feasibility of different learning problems. This project lies at the intersection of computational learning theory and logic, and it builds on recently identified new connections between learning theory and universal algebra. Its high-level goals are (i) to improve our understanding of learnability for fragments of first-order logic, motivated by applications in data management and knowledge representation, and (ii) to further develop and exploit the recently identified connections with universal algebra (as well as combinatorial graph theory, finite model theory, and fixed point logics), to developing a rich technical framework for proving new results. More concretely, we will study aspects of computational learning theory for fragments of first order logic under constraints (that is, in the presence of a background theory), with applications in data management and knowledge representation.
Оригинален текст от CORDIS (на английски).
Участници
- UNIVERSITEIT VAN AMSTERDAM · AmsterdamКоординаторНидерландия
Връзки
Данни: CORDIS, © Европейски съюз