Neurosymbolic Integration: Cognitive Grounds and Computational Strategies
نویسندگان
چکیده
The ultimate|if implicit|goal of artiicial intelligence (AI) 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 diierent strategies that have been adopted to date. Uniied 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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Neurosymbolic integration: unified versus hybrid approaches
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