Incremental and developmental perspectives for general-purpose learning systems


  • Fernando Martínez-Plumed UPV



artificial intelligence, general-purpose learning systems, inductive programming, reinforcement learning, forgetting, task difficulty, cognitive development, evaluation of artificial systems, intelligence tests


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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How to Cite

Martínez-Plumed, F. (2017). Incremental and developmental perspectives for general-purpose learning systems. Inteligencia Artificial, 20(60), 24–27.