The field of Explainable AI (XAI) is seeking to shed light on the inner
workings of complex AI models and uncover the rationale behind their decisions.
One of the models gaining attention are probabilistic circuits (PCs), which are
a general and unified framework for tractable probabilistic models that support
efficient computation of various probabilistic queries. Probabilistic circuits
guarantee inference that is polynomial in the size of the circuit. In this
paper, we improve the explainability of probabilistic circuits by computing a
comprehensible, readable logical theory that covers the high-density regions
generated by a PC. To achieve this, pruning approaches based on generative
significance are used in a new method called PUTPUT (Probabilistic circuit
Understanding Through Pruning Underlying logical Theories). The method is
applied to a real world use case where music playlists are automatically
generated and expressed as readable (database) queries. Evaluation shows that
this approach can effectively produce a comprehensible logical theory that
describes the high-density regions of a PC and outperforms state of the art
methods when exploring the performance-comprehensibility trade-off.

本文提出了一种名为 PUTPUT（通过修剪基本逻辑理论进行概率电路理解）的方法，通过计算可理解的、可读的逻辑理论来改进概率电路的可解释性，并应用于一个真实的使用案例，即自动生成音乐播放列表并将其表示为可读（数据库）查询。评估结果表明，该方法能够有效地生成描述概率电路高密度区域的可理解的逻辑理论，并在性能和可理解性权衡方面优于现有方法。

从概率电路中推导可理解的理论

Deriving Comprehensible Theories from Probabilistic Circuits

Parallel neurosymbolic architectures have been applied effectively in NLP by
distilling knowledge from a logic theory into a deep model.However, prior art
faces several limitations including supporting restricted forms of logic
theories and relying on the assumption of independence between the logic and
the deep network. We present Concordia, a framework overcoming the limitations
of prior art. Concordia is agnostic both to the deep network and the logic
theory offering support for a wide range of probabilistic theories. Our
framework can support supervised training of both components and unsupervised
training of the neural component. Concordia has been successfully applied to
tasks beyond NLP and data classification, improving the accuracy of
state-of-the-art on collective activity detection, entity linking and
recommendation tasks.

该研究论文提出了一种名为 Concordia 的框架，可以有效地将逻辑理论的知识融入深度模型，支持广泛的概率性理论，并成功地应用于 NLP 以及数据分类的任务，以及集体活动检测，实体链接和推荐任务等领域，并且克服了之前其他研究所面临的限制。

Concordia 并行神经符号一体化

Parallel Neurosymbolic Integration with Concordia

We present SGDPLL(T), an algorithm that solves (among many other problems)
probabilistic inference modulo theories, that is, inference problems over
probabilistic models defined via a logic theory provided as a parameter
(currently, propositional, equalities on discrete sorts, and inequalities, more
specifically difference arithmetic, on bounded integers). While many solutions
to probabilistic inference over logic representations have been proposed,
SGDPLL(T) is simultaneously (1) lifted, (2) exact and (3) modulo theories, that
is, parameterized by a background logic theory. This offers a foundation for
extending it to rich logic languages such as data structures and relational
data. By lifted, we mean algorithms with constant complexity in the domain size
(the number of values that variables can take). We also detail a solver for
summations with difference arithmetic and show experimental results from a
scenario in which SGDPLL(T) is much faster than a state-of-the-art
probabilistic solver.

我们介绍了一个叫做 SGDPLL（T）的算法，它可以解决概率推理问题，并具有参数化逻辑理论的特点，可以应用于丰富的逻辑语言，如数据结构和关系数据。