Neurosymbolic Integration : Cognitive Grounds and Computational Strategies
Identifieur interne : 00CC02 ( Main/Merge ); précédent : 00CC01; suivant : 00CC03Neurosymbolic Integration : Cognitive Grounds and Computational Strategies
Auteurs : Y. Lallement ; M. Hilario ; F. AlexandreSource :
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Abstract
The ultimate - if implicit - goal or artificial intelligence research is to model the full range of human cognitive capabilities. Symbolic AI and connectionism, the major AI paradigms, have each tried - and failed - to attain this goal. In the meantime, the idea has gained ground that this goal might still be within reach if we could harness the respective strengths of these two paradigms in integrated neurosymbolic models. This paper attempts to lay a cognitive basis for neurosymbolic integration and describes the different strategies that have been adopted to date. Unified approaches strive to attain symbol-processing capabilities using neural network techniques alone, while hybrid approaches blend symbolic and neural models in novel architectures with the hope of gleaning the best of both paradigms.
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