In response to the global challenge of mental health problems, we proposes a
Logical Neural Network (LNN) based Neuro-Symbolic AI method for the diagnosis
of mental disorders. Due to the lack of effective therapy coverage for mental
disorders, there is a need for an AI solution that can assist therapists with
the diagnosis. However, current Neural Network models lack explainability and
may not be trusted by therapists. The LNN is a Recurrent Neural Network
architecture that combines the learning capabilities of neural networks with
the reasoning capabilities of classical logic-based AI. The proposed system
uses input predicates from clinical interviews to output a mental disorder
class, and different predicate pruning techniques are used to achieve
scalability and higher scores. In addition, we provide an insight extraction
method to aid therapists with their diagnosis. The proposed system addresses
the lack of explainability of current Neural Network models and provides a more
trustworthy solution for mental disorder diagnosis.

提出了基于逻辑神经网络（LNN）的神经符号人工智能方法来诊断心理障碍，该方法解决了神经网络模型缺乏可解释性的问题，提供了一种更值得信赖的心理障碍诊断解决方案。

逻辑神经网络在话语分类中的应用：精神障碍诊断的可解释人工智能

Utterance Classification with Logical Neural Network: Explainable AI for  Mental Disorder Diagnosis

Deep reinforcement learning (RL) methods often require many trials before
convergence, and no direct interpretability of trained policies is provided. In
order to achieve fast convergence and interpretability for the policy in RL, we
propose a novel RL method for text-based games with a recent neuro-symbolic
framework called Logical Neural Network, which can learn symbolic and
interpretable rules in their differentiable network. The method is first to
extract first-order logical facts from text observation and external word
meaning network (ConceptNet), then train a policy in the network with directly
interpretable logical operators. Our experimental results show RL training with
the proposed method converges significantly faster than other state-of-the-art
neuro-symbolic methods in a TextWorld benchmark.

为了在文本角色扮演游戏中实现快速收敛和可解释的知识表示，我们提出了一种新的基于逻辑神经网络的强化学习方法，该方法可以从文本观察中提取一阶逻辑事实并使用逻辑算子训练策略，实验结果表明该方法比其他基于神经元符号框架的方法更快收敛。