The more intelligent systems based on sub-symbolic techniques pervade our everyday lives, the less human can understand them. This is why symbolic approaches are getting more and more attention in the general effort to make AI interpretable, explainable, and trustable. Understanding the current ...
This paper contains a sketch of a generalized systems theory in order to deal with the so-called subsymbolic models, whose implicit informational content cannot be made entirely explicit in a reasonable amount of time. To deal with such a situation, a new genetic approach has been proposed, in...
1Literature Review Grid of Common PointsResearch Question:Howways to keep beauty for improve your live?Possible Answers:The ways to improve beautyarebeauty... CS And 被引量: 0发表: 2016年 加载更多研究点推荐 Symbolic and Subsymbolic Learning Vision 站内活动 ...
Wermter S, Sun R (2001) The present and the future of hybrid neural symbolic systems some reflections from the nips workshop. AI Mag 22(1):123–123 Google Scholar Kelley TD (2003) Symbolic and sub-symbolic representations in computational models of human cognition: what can be learned from ...
We then use the expressiveness and flexibility of LLMs to evaluate these sub-problems and by re-combining these operations we can solve the complex problem.In this turn, and with enough data, we can gradually transition between general purpose LLMs with zero and few-shot learning capabilities,...
High-Performance Symbolic Regression in Python and Julia pythondistributed-systemsdata-sciencemachine-learningalgorithmjuliagenetic-algorithmscikit-learnsymbolicsymbolic-regressionevolutionary-algorithmsautomlinterpretable-mlexplainable-aiequation-discovery UpdatedDec 18, 2024 ...
There is a long and unresolved debate between the symbolic and sub-symbolic methods. However, in recent years, there is a push towards in-between methods. In this work, we provide a comprehensive overview of the symbolic, sub-symbolic and in-between approaches focused in the domain of knowled...
We also show how to transform symbolic controllers designed for a symbolic sub-system into controllers for the original system. The resulting controllers combine symbolic controller dynamics with continuous feedback control laws and can thus be seen as hybrid systems. Furthermore, if the symbolic ...
The integration between symbolic and sub-symbolic AI methods, which we refer to as Neuro-symbolic AI, can assume the role of the above-mentioned framework: Neuro-symbolic AI can harvest expert know-how, combining it with semantically-structured data, ultimately transforming this knowledge corpus ...
Final remarks and conclusions are reported in Section 6. Section snippets Design of a symbolic subsymbolic controller The use of a neural network for systems identification and control has been experimented since the early revival of this computational paradigm in [9]. To ground our design approach...