Cumulative learning

Kristinn R. Thórisson, Jordi Bieger, Xiang Li, Pei Wang

Research output: Chapter in Book/Conference proceedings/Edited volumeConference contributionScientificpeer-review

5 Citations (Scopus)
158 Downloads (Pure)

Abstract

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.

Original languageEnglish
Title of host publicationArtificial General Intelligence - 12th International Conference, AGI 2019, Proceedings
EditorsPatrick Hammer, Pulin Agrawal, Ben Goertzel, Matthew Iklé
PublisherSpringer
Pages198-208
Number of pages11
ISBN (Print)9783030270049
DOIs
Publication statusPublished - 2019
Event12th International Conference on Artificial General Intelligence, AGI 2019 - Shenzhen, China
Duration: 6 Aug 20199 Aug 2019

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume11654 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference12th International Conference on Artificial General Intelligence, AGI 2019
CountryChina
CityShenzhen
Period6/08/199/08/19

Keywords

  • Artificial general intelligence
  • Autonomous knowledge acquisition
  • Cumulative learning
  • Knowledge representation

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