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  • 标题:Incremental and developmental perspectives for general-purpose learning systems
  • 本地全文:下载
  • 作者:Fernando Martínez-Plumed
  • 期刊名称:Inteligencia Artificial : Ibero-American Journal of Artificial Intelligence
  • 印刷版ISSN:1137-3601
  • 电子版ISSN:1988-3064
  • 出版年度:2017
  • 卷号:20
  • 期号:60
  • 页码:24-27
  • 语种:English
  • 出版社:Spanish Association for Intelligence Artificial
  • 摘要:The stupefying success of Articial Intelligence (AI) for specic problems, from recommender systems to self-driving cars, has not yet been matched with a similar progress in general AI systems, coping with a variety of (dierent) problems. This dissertation deals with the long-standing problem of creating more general AI systems, through the analysis of their development and the evaluation of their cognitive abilities. It presents a declarative general-purpose learning system and a developmental and lifelong approach for knowledge acquisition, consolidation and forgetting. It also analyses the use of the use of more ability-oriented evaluation techniques for AI evaluation and provides further insight for the understanding of the concepts of development and incremental learning in AI systems.
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