Publication

Agential AI for Integrated Continual Learning, Deliberative Behavior, and Comprehensible Models

Boi Faltings
2025

Abstract

Contemporary machine learning faces key limitations, such as a lack of integration with planning, incomprehensible structure, and inability to learn continually. We present initial design for system, Agential AI (AAI), that overcomes these issues. AAI's core is a learning method that models temporal dynamics with guarantees of completeness, minimality, and continual learning. It integrates this with a behavior algorithm that plans on a learned model and encapsulates high-level behavior patterns. Preliminary experiments on a simple environment show AAI's effectiveness and potential.

Official source

https://infoscience.epfl.ch/entities/publication/6d863d81-e672-4ff3-a8fb-29d18a9f1f8b

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Boi Faltings
2025

Abstract

Official source

https://infoscience.epfl.ch/entities/publication/6d863d81-e672-4ff3-a8fb-29d18a9f1f8b

About this result

Related concepts (29)

Deep learning

Deep learning is part of a broader family of machine learning methods, which is based on artificial neural networks with representation learning. The adjective "deep" in deep learning refers to the use of multiple layers in the network. Methods used can be either supervised, semi-supervised or unsupervised.

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Learning is the process of acquiring new understanding, knowledge, behaviors, skills, values, attitudes, and preferences. The ability to learn is possessed by humans, animals, and some machines; there is also evidence for some kind of learning in certain plants. Some learning is immediate, induced by a single event (e.g. being burned by a hot stove), but much skill and knowledge accumulate from repeated experiences. The changes induced by learning often last a lifetime, and it is hard to distinguish learned material that seems to be "lost" from that which cannot be retrieved.

Online machine learning

In computer science, online machine learning is a method of machine learning in which data becomes available in a sequential order and is used to update the best predictor for future data at each step, as opposed to batch learning techniques which generate the best predictor by learning on the entire training data set at once. Online learning is a common technique used in areas of machine learning where it is computationally infeasible to train over the entire dataset, requiring the need of out-of-core algorithms.

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