Cumulative learning

Cumulative learning

Thórisson, Kristinn R. (Reykjavik University)
Bieger, J.E. (TU Delft Information and Communication Technology; Reykjavik University)
Li, X. (Temple University)
Wang, Pei (Temple University)

Hammer, Patrick (editor)
Agrawal, Pulin (editor)
Goertzel, Ben (editor)
Iklé, Matthew (editor)

2019

An important feature of human learning is the ability to continuously accept new information and unify it with existing knowledge, a process that proceeds largely automatically and without catastrophic side-effects. A generally intelligent machine (AGI) should be able to learn a wide range of tasks in a variety of environments. Knowledge acquisition in partially-known and dynamic task-environments cannot happen all-at-once, and AGI-aspiring systems must thus be capable of cumulative learning: efficiently making use of existing knowledge while learning new things, increasing the scope of ability and knowledge incrementally—without catastrophic forgetting or damaging existing skills. Many aspects of such learning have been addressed in artificial intelligence (AI) research, but relatively few examples of cumulative learning have been demonstrated to date and no generally accepted explicit definition exists of this category of learning. Here we provide a general definition of cumulative learning and describe how it relates to other concepts frequently used in the AI literature.

Artificial general intelligence
Autonomous knowledge acquisition
Cumulative learning
Knowledge representation

http://resolver.tudelft.nl/uuid:70fcb01e-7e6a-4748-8f24-d5760e60d29f

https://doi.org/10.1007/978-3-030-27005-6_20

Springer

9783030270049

Artificial General Intelligence - 12th International Conference, AGI 2019, Proceedings

12th International Conference on Artificial General Intelligence, AGI 2019, 2019-08-06 → 2019-08-09, Shenzhen, China

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 0302-9743, 11654 LNAI

Institutional Repository

conference paper

© 2019 Kristinn R. Thórisson, J.E. Bieger, X. Li, Pei Wang